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In recent years, distributed optimization problem have a wide range of applications in various fields. This paper considers the prescribed-time distributed optimization problem with/without constraints. Firstly, we assume the state of each agent is constrained, and the prescribed-time distributed optimization algorithm with constraints is designed on the basis of gradient projection algorithm and consensus algorithm. Secondly, the constrained distributed optimization problem is transformed into the unconstrained distributed optimization problem, and according to the gradient descent algorithm and consensus algorithm, we also propose the prescribed-time distributed optimization algorithm without constraints. By designing the appropriate objective functions, we prove the multi-agent system can converge to the optimal solution within any prescribed-time, and the convergence time is fully independent of the initial conditions and system parameters. Finally, three simulation examples are provided to verify the validity of the designed algorithms. • For distributed optimization problem with convex constraints, we design an effective prescribed-time distributed algorithm which can guarantee the multi-agent system converge to the optimal solution. • The designed algorithm in this paper can achieve convergence within any prescribed-time, and the convergence time is fully independent of the initial conditions and system parameters. • In this paper, we not only consider the condition that the agents' states are constrained, but also ensure that the multi-agent system can reach the optimal solution within any prescribed-time, which is more practical. [ABSTRACT FROM AUTHOR]

Abstract: The automatic control of grammage (basis weight) in paper and board production is based upon on-line grammage measurement. Furthermore, the automatic control of other quality variables such as moisture, ash content and coat weight, may rely on the grammage measurement. The integrity of Kr-85 based on-line grammage measurement systems was studied, by performing basic calibrations with traceably calibrated plastic reference standards. The calibrations were performed according to the EN ISO/IEC 17025 standard, which is a requirement for calibration laboratories. The observed relative measurement errors were 3.3% in the first time calibrations at the 95% confidence level. With the traceable basic calibration method, however, these errors can be reduced to under 0.5%, thus improving the integrity of on-line grammage measurements. Also a standardised algorithm, based on the experience from the performed calibrations, is proposed to ease the adjustment of the different grammage measurement systems. The calibration technique can basically be applied to all -radiation based grammage measurements. [Copyright &y& Elsevier]

This paper investigates the problem of optimal preview pinning control for Boolean networks. The primary objective is to design efficient control strategies that leverage future reference information for improved control performance. We propose a policy iteration algorithm specifically for Boolean networks based on an augmented error system, constructed using the state augmentation technique in combination with the Exclusive-Or operator approach. The algorithm effectively optimizes control policies using future information. Finally, an example is presented to illustrate the effectiveness of the proposed algorithm. • First study of optimal preview pinning control problem of BNs. • Transformation using the semi-tensor product and exclusive-OR operator. • Deduction of a policy iteration algorithm for BNs. [ABSTRACT FROM AUTHOR]

Particle Swarm Optimization (PSO) is considered as one of Maximum Power Point Tracking (MPPT) controller algorithm developed for PhotoVoltaic system (PV) to guarantee a maximum power extraction under different climatic conditions of temperature and irradiation. Classical MPPT algorithms like Perturbe and Observe (P&O), PSO, Adaptive Neuro-Fuzzy Inference System (ANFIS) are an effective method for tracking the maximum power point (MPP) for the PV systems, but the problems with these approaches that they are less stable, high oscillation around steady state and slower convergence to the MPP. Based on recent research, the purpose of this paper is to introduces a novel MPPT controller based on a modified version of heterogeneous multi swarm PSO algorithm using an adaptive factor selection strategy (FMSPSO), to overcome the previous shortcomings and compared with conventional PSO, ANFIS and classical P&O controllers. Simulation and experimental results revealed that the new FMSPSO algorithm can overcome the previous shortcomings providing the superior performance to track the MPP efficiently with a shorter convergence time and small oscillations compared to other algorithms. The experimental confirmation of the FMSPSO algorithm has been carried out using NI-myRIO-1900 card and shows that with the proposed MPPT approach efficiency can reach a value greater than 99% even in climatic variation of irradiation and temperature. • A classical MPPT controller cannot track the dynamic the MPP, has obvious oscillations, and converges slowly. • A novel MPPT controller based on a modified version of MSPSO algorithm using an adaptive factor selection strategy (FMSPSO) is proposed. • A FMSPSO based MPPT algorithm is compared with conventional PSO, classical P&O and ANFIS controllers for validation. • The proposed FMSPSO algorithm provides the superior performance by boosting the PV system tracking efficiency more than 99%. [ABSTRACT FROM AUTHOR]

The conventional direct torque control (DTC) has high torque and stator flux fluctuation that causes the stator current distortion. This paper presents an efficient control method based on the feedback-linearization direct torque control (FL-DTC) method for an interior permanent magnet synchronous motor (IPMSM) drive by using an improved firefly algorithm. The proposed approach can greatly restrain the poor performance of torque and stator flux. Thus, it is suitable for IPMSM drives in electric vehicles. First, a decoupled linear model is derived to implement the proposed efficient feedback linearization control for the IPMSM. Two phase voltages in d-q axes and two additional control inputs take shape into an isomorphism mapping with the concept of orthogonal transformation. The torque generation is related to the additional control. Second, the Hamiltonian efficient control theory combined with an improved firefly algorithm is applied to obtain an analytical solution. An efficient linearization controller is designed with a cost function considering the maximum voltage of the inverter. Finally, simulation and experiment are carried out to compare the performance of the proposed efficient FL-DTC with the improved firefly algorithm and the conventional direct torque control. The results show that the proposed control method can reduce the torque and flux ripples at a steady state and maintains a good dynamic response with the variations of speed and torque. • To restrain the poor performance of torque and stator flux, this paper presents an optimal control method based on feedback-linearization direct torque control (FL-DTC) method for an interior permanent magnet synchronous motor (IPMSM) drive by using an advanced firefly algorithm. • A decoupled linear model is derived to implement the proposed optimal feedback linearization control for the IPMSM. Two phase voltages in d-q axes and two additional control inputs take shape into an isomorphism mapping with the concept of orthogonal transformation. • The Hamiltonian of optimal control theory based on an advanced firefly algorithm is applied to obtain an analytical solution. • An optimal linearization controller is designed with a cost function considering the maximum voltage of the inverter. • The proposed control method has faster torque response and smaller torque and stator flux fluctuation. [ABSTRACT FROM AUTHOR]

In this paper, a brain-like navigation scheme based on fuzzy kernel C-means (FKCM) clustering assisted pyramid Lucas Kanade (LK) optical flow algorithm is developed to measure the position of vehicle. The Speed Cell and Place Cell in animals' brain are introduced to construct the brain-like navigation mechanism which involves the optical flow method and image template matching to imitate the cells above-mentioned separately. To eliminate the singular values during optical flow calculation, the output of pyramid LK algorithm is clustered by FKCM algorithm firstly. Then, the velocity is calculated and integrated to get the position of the vehicle, and the brain-like navigation scheme is introduced to correct the position measurement errors by eliminating the accumulated errors resulting from velocity integration. The prominent advantages of the presented method are: (i) a pure visual brain-like position measurement method based on the concept of speed cells and place cells is proposed, making visual navigation more accurate and intelligent; (ii) the FKCM algorithm is used to eliminate the singular value of the pyramid LK algorithm, which improves the calculated velocity accuracy. Also, experimental comparison with classical pyramid LK algorithm is given to illustrate the superiority of the proposed method in position measurement. • Analyzed impact of clustering algorithms on optical flow precision. • Achieved notable reduction in optical flow singular value. • Brain-like navigation minimizes velocity integral errors. [ABSTRACT FROM AUTHOR]

This paper firstly established a nonlinear dynamic model of a 330 MW drum boiler unit, which has a clear physical meaning and is conducive to the design of coordinated control system (CCS). On this basis, the boiler energy storage coefficient is calculated, and its relationship with that in direct energy balance (DEB) strategy is analyzed. Secondly, this paper proposes a CCS based on dynamic matrix control (DMC) algorithm and DEB strategy, in which: (1) energy demand and status signals of DEB are taken as the setting point and process value of DMC controller, so as to overcome the internal disturbance of the boiler; (2) a compensation signal is constructed as the feedforward input of DMC controller, so as to overcome the external disturbance of the turbine and realize the timely utilization and supplement of boiler energy. Simulation shows: (1) there is a certain deviation in main steam pressure when the energy demand and status signals are taken as the setting point and process value, but this is an illusion of insufficient energy, and in fact the energy status can already meet the demand; (2) compared with the main steam valve, the constructed compensation signal takes into account more operating parameters of the unit, which greatly improves the accuracy of the compensation. Practical application shows: the grid dispatch order can be well tracked by the unit, the maximum deviation of main steam pressure is ± 0.6 MPa in constant pressure mode, and that is ± 1.2 MPa in sliding pressure mode, thereby improving the control performance of the unit under high, medium, low, and overall load conditions. [Display omitted] • Analyze the physical meaning of boiler energy storage coefficient. • Propose an CCS by integrating DMC algorithm and DEB strategy. • Construct a compensation signal to overcome external disturbances. [ABSTRACT FROM AUTHOR]

Generally there are two methods for the thrust ripple modeling: one is to identify the eigenvalues and the model parameters of thrust ripple sequentially under rigorous operating conditions; another is to treat thrust ripple as a general and inaccurate disturbance. To get rid of the constraints and further enhance the tracking accuracy of the linear servo system, a novel integrated identification and compensation scheme using an improved matching pursuit algorithm is proposed in this paper. First, the dynamics of the linear motor is formulated, and the cause of thrust ripple is analyzed. Then, a time–frequency atomic library of thrust ripple is constructed with the consideration of the frequency characteristics of position command, and meanwhile, the two optimal atoms are obtained successively by maximizing the inner product for the thrust ripple reconstruction. Based on the above, the eigenvalues and model parameters of thrust ripple can be simultaneously identified in an online manner. Finally, the feedforward compensation component is devised by the identification results to suppress thrust ripple. In order to verify the superiority of the proposed scheme, simulations and experiments are conducted compared with the conventional methods. • The eigenvalues and parameters of thrust ripple can be identified simultaneously. • The proposed scheme has higher efficiency compared with the traditional methods. • The proposed scheme has the advantages of convergence speed and identification accuracy. [ABSTRACT FROM AUTHOR]

Due to nonstationary operating conditions of wind turbines and surrounding harsh working environments, the impulse features induced by bearing faults are always overwhelmed by heavy noise, which brings challenges to accurately detect rolling bearing faults. Sparse representation exhibits excellent performance in nonstationary signal analysis, but it is closely bound up with the degree of similarity between the atoms in a dictionary and signals. Therefore, this paper investigates an enhanced K-SVD denoising method based on adaptive soft-threshold shrinkage to achieve high-precision extraction of impulse signals, and applies it to fault detection of generator bearing of wind turbines. An adaptive sparse coding shrinkage soft-threshold denoising is first proposed to remove noise and harmonic interference in the residual term of dictionary updating, so that the updated atoms show obvious impact characteristics. Furthermore, a soft-threshold shrinkage function with adaptive threshold is designed to further suppress clutter in atoms of the learned dictionary, so as to obtain an optimized dictionary for recovering impulse signals. Two actual engineering cases are selected for analysis, and the envelope spectrum correlation kurtosis corresponding to the results obtained by the proposed method is significantly higher than that of other comparison methods, thus verifying its superiority in detecting rolling bearing faults. • An enhanced K-SVD denoising algorithm for fault-induced periodic impulse extraction. • Adaptive sparse coding shrinkage is introduced into the dictionary updating. • A soft-threshold shrinkage function is proposed to optimize the dictionary. • Proposed method improves the extraction accuracy of rolling bearing fault features. [ABSTRACT FROM AUTHOR]

In recent years, pumps have become critical components in agriculture, industry, and the military, necessitating extensive development and implementation of the fault diagnosis method. In the majority of existing fault classification models, the connection between performance improvement and the amount of training data remains high, yet real-world samples are difficult to obtain. Combining domain migration theory and sample expansion method, this paper introduces a few-shot learning fault diagnosis method. Employing the T-SNE visualization algorithm, we examine the validity of the self-calibration attention mechanism (SCAM) and distribution edge prediction strategy (DEPS). The accomplishment demonstrated that the proposed algorithm could effectively map the expanded sample space within a separate interval, thereby avoiding the problem of feature aliasing caused by the overlap of sample features among similar categories and significantly enhancing the quality and quantity of training samples. The experimental analysis indicates that the proposed methodology can effectively increase the accuracy of few-shot tasks, especially in the 9way-15shot task, where it maintains a performance of 72 %, which leading the mean accuracy calculated from the others of about 30%. It is believed that much of the work has superior applicability to other few-shot diagnosis cases. • A combination of domain migration theory and sample expansion method. • Map the expanded sample space within a separable interval, avoid feature aliasing. • A few-shot sample augmentation algorithm for pump fault diagnosis. • Performing on 256*256 images transformed from one-dimensional acceleration signal. [ABSTRACT FROM AUTHOR]

The problem of multi-detection multi-target tracking (MDMTT) using over-the-horizon radar in dense clutter environment is studied in this paper. The biggest challenge of MDMTT is the 3-dimensional multipath data association among measurements, detection models and targets. In particular, a lot of clutter measurements are generated in dense clutter environment, which increase the computational burden of 3-dimensional multipath data association greatly. A measurement based dimension descent association (DDA) algorithm is proposed to solve the 3-dimensional multipath data association, which decomposes the 3-dimensional multipath data association into two 2-dimensional data associations. The proposed algorithm can reduce the computational burden compared with the optimal 3-dimensional multipath data association and the computational complexity is analyzed. Besides, a time extension method is designed to detect the new-born targets that appear in the tracking scene, which is based on the sequential measurements. The convergence of the proposed measurement based DDA algorithm is analyzed. The estimation error can convergence to 0 as the number of Gaussian mixtures tends to infinity. The effectiveness and rapidity of the measurement based DDA algorithm are demonstrated by the comparative simulation with the previously proposed algorithms. • Multi-detection multi-target tracking using OTHR is studied by dimension descent. • The detection of new-born targets with unknown prior knowledge is considered. • The convergence of the novel multi-target tracking algorithm is analyzed. • The labeled GMPHD is proposed to reduce the computational burden. [ABSTRACT FROM AUTHOR]

This paper proposes an improved narrowband filtered-x least mean square(FxLMS) algorithm to overcome the thermal failure problems of the active controlled mount(ACM). Firstly, the temperature-rising model and the thermal demagnetization model of the ACM are respectively developed. Combining these two models with the powertrain mounting system model, an analytical method is established for the thermal–magnetic coupling analysis of the ACM. Then a numerical simulation is carried out to obtain the permanent magnet(PM) temperature and the coil current. The ACM failure problem is discussed according to the working point trajectory. Finally, an improved algorithm is proposed. This algorithm is able to overcome the thermal failure problems by sacrificing part of the vibration isolation performance. The effectiveness of this algorithm is validated by numerical simulations and a comparison with conventional algorithms. • Temperature rise and thermal demagnetization are mutual positive feedbacks in the active controlled mount. • Conventional algorithm leads to damage or even thermal failure in some working conditions. • An improved algorithm is able to avoid the irreversible demagnetization and stabilize the active controlled mount. • The improved algorithm maintains most part of the vibration isolation performance. [ABSTRACT FROM AUTHOR]

This paper proposes a nonsmooth funnel transformation function-based discrete-time sliding-mode control strategy for the discrete-time servo systems with unknown frictions and disturbances. For obtaining a more accurate discrete-time system model, a filter-based adaptive identification algorithm (FAIA) is introduced, where the unknown measurement noises are considered. Based on the identified system model and a novel discrete-time nonsmooth funnel transformation function (improving the tracking performances), a discrete-time sliding-mode surface is designed to confine the tracking error to a smaller funnel region compared with the predefined one, which has a deterministic performance margin related to the upper bound of the lumped disturbance. Furthermore, selections of steady-state funnel boundary and sliding-mode surface parameter can be guided theoretically according to the upper bound of the lumped disturbance. Experimental results show that the tracking error is guaranteed in the predefined funnel with a deterministic performance margin by using the proposed control strategy. • Taking advantages of nonsmooth error transformation and funnel error surface, a novel discrete-time nonsmooth funnel transformation function (NFTF) is proposed to confine the tracking error and improve its performances. • An NFTF-based discrete-time sliding-mode control (DSMC) is proposed to constrain the tracking error in a predefined funnel zone with a deterministic performance margin. • The selections of steady-state performance boundary and sliding-mode surface parameter are determined by an inequality involving the upper bound of lumped disturbance, which removes the "trial-and-error" approach. [ABSTRACT FROM AUTHOR]

In this paper, we propose a control scheme to ensure the microgrid control layers are resilient to cyberattacks. The studied microgrid consists of several distributed generation (DG) units and we consider the hierarchical control structure that is common for microgrids. The use of communication channels among DGs has made microgrids more vulnerable and this is where cybersecurity issues arise. In this work we added three algorithms, reputation-based, Weighted Mean Subsequence Reduced (W-MSR) and Resilient Consensus Algorithm with Trusted Nodes (RCA-T), to the secondary control layer of the microgrid and made them resilient to false data injection (FDI) attacks. In reputation-based control, some procedures are used for detecting the attacked DGs and isolating them from the others. W-MSR and RCA-T are Mean Subsequence Reduced (MSR)-based algorithms that fade the effect of attacks without finding them. These algorithms use a simple strategy that ignores some extreme values of neighboring agents, so an attacker can simply get ignored. Our analysis of the reputation-based algorithm is based on scrambling matrices, so the communication graph can switch in a prescribed set. In each of the above cases, to evaluate the performance of the designed controllers, in addition to theoretical analysis, we evaluated and compared the controllers using simulation. • Reputation-based control has a fault detection and isolation mechanism. • W-MSR and RCA-T use a fault-resilient control that make microgrids flexible. • The reputation-based algorithm tolerates switching topologies of communication graph. • The W-MSR and RCA-T algorithms do not require computational complexity on DG level. • The performance is evaluated via simulations in addition to theoretical analysis. [ABSTRACT FROM AUTHOR]

This paper proposes a kernel function selection mechanism a support vector machine(SVM) under sparse representation and its application in bearing fault diagnosis. For a given data sample, a total of 125,150 different types of kernel functions and different parameters to classify and obtain the accuracy, root mean square error (RMSE) and mean square correlation coefficient (MSCC) of each training, these three values into an overcomplete redundant sparse dictionary. The OMP algorithm is used to solve the sparse coding, that are nonzero in the sparse coding are function types and parameters corresponding to these nonzero atoms according to the one-to-one correspondence between the sparse coding and the sparse dictionary. The nonzero atoms in the sparse coding and the kernel function types and parameters into the kernel function fitness table. According to the selection mechanism, we select the type of kernel function that is most suitable for the given data. A SVM is then composed of selected kernel function types, and PSO algorithm is used to the relevant parameters for classification of unknown data to Finally, we perform simulations and engineering experiments involving high-speed bearing fault diagnosis to verify the superiority of the selection mechanism. • This paper focuses on an intelligent diagnosis method for the kernel function selection mechanism of a support vector machine • Formulate a set of rules that make the selected kernel function type of support vector machine (SVM) is most suitable for the data. • Perform a simulation experiment and engineering experiment of high-speed bearing fault diagnosis, the experimental results show that the support vector machine model constructed by the kernel function selected by the proposed method is superior to the support vector machine model composed of other kernel functions in terms of accuracy or mean square error, which ensures the generalization ability of the model and reduces the computation time to some extent. [ABSTRACT FROM AUTHOR]

Parameters for defining photovoltaic models using measured voltage–current​ characteristics are essential for simulation, control, and evaluation of photovoltaic-based systems. This paper proposes an enhanced chaotic JAYA algorithm to classify the parameters of various photovoltaic models, such as the single-diode and double-diode models, accurately and reliably. The proposed algorithm introduces a self-adaptive weight to regulate the trend to reach the optimal solution and avoid the worst solution in various phases of the search space. The self-adaptive weight capability also allows the proposed technique to reach the best solution at the earliest phase, and later, the local search process starts, which also increase the ability to explore. A three different chaotic process, including sine, logistics and tent map, is proposed to optimize the consistency of each generation's best solution. The proposed algorithm and its variants proposed are used to solve the parameter estimation problem of various PV models. To show the proficiency of the suggested algorithm and its variants, an extensive simulation is carried out using MATLAB/Simulink software. Two statistical tests are conducted and compared with the latest techniques for validating the performance of the suggested algorithm and its variants. Comprehensive analysis and experimental results display that the suggested algorithm can achieve highly competitive efficiency in terms of accuracy and reliability compared to other algorithms in the literature. This research will be backed up with extra online service and guidance for the paper's source code at https://premkumarmanoharan.wixsite.com/mysite. • A new CJAYA algorithm is proposed to estimate the parameters of SDM and DDM models. • Chaotic and self-adaptive weights are combined with JAYA to get the best solution. • The performance of CJAYA and its variants are tested on 23 benchmark functions. • Four PV models are considered to validate the performance of CJAYA and its variants. • Detailed analysis, results, and statistical tests show that the CJAYA is superior. [ABSTRACT FROM AUTHOR]

The second-order synchrosqueezing S-transform (SSST2) is an important method for instantaneous frequency (IF) estimation of non-stationary signals. Based on the synchrosqueezing S-transform, the instantaneous frequency calculation method is modified using the second-order partial derivatives of time and frequency to achieve higher frequency resolution. However, weak multi-frequency signals with strong background noise are often drowned out during the transformation process. To achieve enhanced extraction of weak fault characteristic signals due to mechanical faults, this paper proposes an optimally weighted sliding window signal segmentation algorithm based on the SSST2. The results of simulations and experiments show that the time–frequency aggregation of the second-order synchrosqueezing S-transform based on the optimally weighted sliding window (OWSW-SSST2) is not only significantly higher than that of commonly used time–frequency transforms, but it also has better operational efficiency than the second-order synchrosqueezing S-transform. In this paper, the proposed algorithm is used to analyze fault signals from actual high-speed railway wheelset bearings. The results show that the OWSW-SSST2 algorithm greatly improves the spectral aggregation of the signal, and crucially, that high-precision IF estimates for signals can be obtained in low signal-to-noise ratio environments. This research is both of academic interest and significant for practical engineering use to ensure safe high-speed rail operations. It helps enable monitoring the status of wheelset bearings, correctly estimating the locations and causes of failures, and providing up-to-date systematic maintenance and system improvement strategies. • This paper proposes a novel method named OWSW-SSST2. • The proposed method overcomes the drawbacks of SSST. • The signal processing performance of OWSW-SSST2 is analyzed comparatively. • Simulation and application show the validity and superiority of OWSW-SSST2. [ABSTRACT FROM AUTHOR]

Firefly algorithm (FA) is a meta-heuristic optimization algorithm inspired by nature. Due to its superior performance, it has been widely used in real life. However, it also has some shortcomings in some optimization cases, such as low solution accuracy and slow solution speed. Therefore, in this paper, distributed parallel firefly algorithm (DPFA) with four communication strategies is presented to improve these shortcomings. The distributed parallel technique is implanted to divide the initial fireflies into several subgroups, and exchange the information based on communication strategies among subgroups after the fixed iteration. The communication strategies include the maximum of the same group, the average of the same group, the maximum of different groups and the average of different groups. For verifying its performance, this paper compared DPFA with famous optimization algorithms, and experimental results show that DPFA has stronger competitiveness under the test suite of CEC2013. Furthermore, the proposed DPFA is also applied to the PID parameter tuning of variable pitch wind turbine, and conducted experiments show that DPFA outperforms other algorithms. It can smooth the power output and reduce the impact on the power grid when the wind speed fluctuates. • Developed distributed parallel mechanism as a method to balances the exploration and exploitation of algorithms. • A proposed distributed parallel firefly algorithm (DPFA) improves the performance of the original FA. • Compared DPFA with other well-known optimization algorithms under the test suite of CEC2013. • DPFA is applied in the control of variable pitch wind turbine to tune PID parameters. [ABSTRACT FROM AUTHOR]

This paper proposes an extension of the KLMS algorithm to the Vector-Valued Reproducing Kernel Hilbert Space (VV-RKHS), enabling the use of the operator-valued kernel (OV-kernel) in the online identification of multi-input multi-output (MIMO) nonlinear systems. The yielded multivariate kernel model offers more design flexibility and involves fewer parameters than other vector-valued KLMS algorithms present in the literature. Conditions ensuring the convergence of the proposed OV-KLMS algorithm are given. Experiments on a multivariate chaotic attractor as well as numerical simulations are carried out and show the effectiveness of the proposed algorithm • A Kernel LMS algorithm in Vector Valued Reproducing Kernel Hilbert Space is proposed. • The proposed algorithm makes use of Operator Valued Kernels. • Proofs of the algorithm convergence are given. • The yielded kernel model is more accurate compared to state-of-the-art algorithms. • It is a low time consumption algorithm and is suitable for online identification of nonlinear Multi Input Multi Output systems. [ABSTRACT FROM AUTHOR]

Penetration path planning for stealth unmanned aerial vehicles (SUAVs) in the integrated air defense system (IADS) has been a hot research topic in recent years. The present study examines penetration path planning in different threat environments. Firstly, for the complex terrain and static radar threats, a modified A-Star algorithm containing the bidirectional sector expansion and variable step search strategy is proposed to elude static threats rapidly. Then, with regard to bandit threats, the minimal radar cross-section (RCS) tactics are presented to achieve path replanning. Furthermore, the combinatorial methodology of the minimum RCS tactics and the modified A-Star algorithm is applied to achieve the dynamic path planning for SUAV. The simulation results indicate that the modified A-Star algorithm and minimal RCS tactics can significantly reduce the probability of radar system, which has better superiority in calculation efficiency, path cost and safety. And the minimal RCS tactics have better real-time performance and are more convenient in dealing with dynamic threats, which enhances the survivability of SUAV and verifies the effectiveness of the proposed methodology. • This paper presents a novel framework to settle dynamic path planning problem for stealth unmanned aerial vehicles. • A modified A-Star algorithm that adopts the bidirectional sector variable step search strategy. • The minimal radar cross-section tactics are convenient to deal with the bandit threats and enhance real-time performance. • The combinatorial methodology has better superiority in calculation efficiency, path cost and safety. [ABSTRACT FROM AUTHOR]

By taking into account sampled-data mechanism and transmission delay, the novel event-triggering load frequency control (LFC) strategy involving random dynamic triggering algorithm (RDTA) is developed for multi-area power systems in this paper. Firstly, an improved multi-area LFC model considering sampling and transmission delay (STD) simultaneously is addressed. Secondly, a modified event-triggering mechanism (ETM) with RDTA is proposed, considering parameter disturbances and a dynamic adjustment mechanism of the triggering threshold. Thirdly, a more advanced Lyapunov–Krasovskii functional (LKF) is constructed, introducing the delay-dependent matrices, more variable cross terms and the two-sided closed functional. Furthermore, two less conservative stability criteria are obtained according to the designed approach. Finally, two multi-area LFC systems are presented to verify the progressiveness of the proposed approach. • An improved multi-area load frequency control model with sampling and transmission delay is established. • A novel event-triggering mechanism with random dynamic triggering algorithm is proposed by considering parameters disturbance and dynamic adjustment mechanism of triggering threshold. • A more advanced Lyapunov–Krasovskii functional is constructed by introducing delay dependent matrix, more cross terms of variables and the two-side closed functional. [ABSTRACT FROM AUTHOR]

A novel parameter identification method for locally linear radial basis function-based autoregressive models in presence of colored noises is proposed in this paper. Taking advantage of the global nonlinear and local linear structural characteristics of the models, two dynamical criterion functions are constructed based on the separated parameters to realize the dynamical acquisition and utilization of the entire process data. Two recursive gradient sub-algorithms are derived for estimating the separated parameters by using the nonlinear gradient optimization. To coordinate the associated variables existing in the sub-algorithms and to estimate the unmeasurable noise terms, we combine the sub-algorithms and propose a two-stage extended recursive gradient (2S-ERG) algorithm. In addition, an extended recursive gradient algorithm is given as a comparison. The feasibility of the 2S-ERG algorithm is validated by numerical simulations. • Two dynamical criterion functions are defined to allow the observations gradually participate. • To separately estimate the parameters, two recursive gradient (RG) sub-algorithms are derived. • A method to deal with the associated terms and unmeasurable noise terms is presented. • Coordinating the two RG sub-algorithms gets a two-stage extended recursive gradient algorithm. [ABSTRACT FROM AUTHOR]

A novel parameter identification method for locally linear radial basis function-based autoregressive models in presence of colored noises is proposed in this paper. Taking advantage of the global nonlinear and local linear structural characteristics of the models, two dynamical criterion functions are constructed based on the separated parameters to realize the dynamical acquisition and utilization of the entire process data. Two recursive gradient sub-algorithms are derived for estimating the separated parameters by using the nonlinear gradient optimization. To coordinate the associated variables existing in the sub-algorithms and to estimate the unmeasurable noise terms, we combine the sub-algorithms and propose a two-stage extended recursive gradient (2S-ERG) algorithm. In addition, an extended recursive gradient algorithm is given as a comparison. The feasibility of the 2S-ERG algorithm is validated by numerical simulations. • Two dynamical criterion functions are defined to allow the observations gradually participate. • To separately estimate the parameters, two recursive gradient (RG) sub-algorithms are derived. • A method to deal with the associated terms and unmeasurable noise terms is presented. • Coordinating the two RG sub-algorithms gets a two-stage extended recursive gradient algorithm. [ABSTRACT FROM AUTHOR]

Automatic Voltage Regulator (AVR) is fabricated to sustain the voltage level of a synchronous generator spontaneously. Several control strategies have been introduced into the AVR system with the aim of gaining a better dynamic response. One of the most universally utilized controllers is the Proportional–Integral–Derivative (PID) controller. Despite the PID controller having a relatively high dynamic response, there are still further possibilities to improve in order to obtain more appropriate responses. This paper designed a sigmoid-based PID (SPID) controller for the AVR system in order to allow for an accelerated settling to rated voltage, as well as increasing the control accuracy. In addition, the parameters of the proposed SPID controller are obtained using an enhanced self-tuning heuristic optimization method called Nonlinear Sine Cosine Algorithm (NSCA), for achieving a better dynamic response, particularly with regards to the steady-state errors and overshoot of the system. A time-response specifications index is used to validate the proposed SPID controller. The obtained simulation results revealed that the proposed method was not only highly effective but also greatly improved the AVR system transient response in comparison to those with the modern heuristic optimization based PID controllers. • A Sigmoid PID (SPID) controller according to Ateş et al. (2015) is first-time designed for the Automatic Voltage Regulator. • The proposed optimization method called Nonlinear Sine Cosine Algorithm (NSCA) is utilized to tune the SPID controller. • The proposed NSCA-SPID controller offers control dynamics and accuracy. • NSCA-SPID is also capable of handling the unpredicted disturbances or the uncertainties in the system. [ABSTRACT FROM AUTHOR]

The accuracy and stability of navigation algorithms are crucial preconditions for underwater gravity matching aided navigation. To improve the matching accuracy and robustness of the matching algorithm, this paper presents a novel gravity matching navigation algorithm based on multiscale search and Hadamard transformed difference. The Hadamard transformation process was first time introduced in the new algorithm for the Walsh–Hadamard kernel function with the property of energy conservation. The gravity measurement sequences could be converted to the Hadamard domain; thus, the difference in numerical values, tendency, and spatial structure of the gravity measurement sequence were also a focus in the new algorithm, whereas only gravity statistical values were considered in classical matching algorithms. Therefore, with the proposed algorithm, the number of measurements necessary for matching can be effectively reduced, while improving the matching accuracy and success rate. In addition, a multiscale neighborhood search strategy based on contour constraints was designed to improve the matching efficiency, whereas a point-by-point global search was widely used in classical matching algorithms. Marine gravity maps of the South China Sea were used to construct the simulation tests. Simulation results show that under various conditions, the new algorithm has lower requirements for the number of measurements, measuring accuracy, and matching areas while exhibiting a higher navigation accuracy, matching success rate, and matching efficiency. Thus, the proposed algorithm could provide a new option for future practical applications of gravity matching aided navigation. • The Hadamard transformation was introduced in the gravity matching aided navigation. • A multiscale neighborhood search strategy based on contour constraints was designed. • The proposed method has better navigation accuracy, robustness, and efficiency in simulation tests. • The new algorithm has lower requirements for the number of measurements, measuring accuracy and matching areas. [ABSTRACT FROM AUTHOR]

In this paper, we propose a unified and simple trajectory planning-based control strategy for the n -link (n ≥ 2 ⋂ n ∈ Z) vertical underactuated manipulator (VUM) with an underactuated joint. The proposed method does not need to partition the entire motion space like the traditional methods, and can quickly realize the control objective of swinging the endpoint of the system up from the vertical downward starting position and stabilizing it at the vertical upward ending position. The trajectory planned for each active link includes two stages. The first stage is to make the first active link and passive link reach the intermediate states, and other active links reach the ending states. The second stage is to move the first active link and the passive link to the ending states, while other active links keep at the ending states. Considering the above targets of two stages, the trajectory of each active link is designed and the trajectory parameters are optimized via utilizing the differential evolution algorithm. Then, the tracking controllers and stabilization controllers are designed to track the two-stage trajectories and to stay the endpoint at the ending position. Finally, the numerical simulations are carried out to show the feasibility and superiority of the proposed method. • A unified method is proposed for a class of n-link underactuated manipulators. • The designed time switching condition is easier to be satisfied. • The proposed method has better control effect without partitioning the motion space. [ABSTRACT FROM AUTHOR]

One of the most important part of a stand-alone photovoltaic system is energy management. This part uses several sensors to measure solar radiations, load power and battery energy to determine operation of the system components. Reduction the number of sensors and optimization of energy management system are two key steps to improve the efficiency and reliability of the system without additional cost. Hence, in this paper, a comprehensive energy management algorithm along with a complementary algorithm to estimate radiation under uniform and partial shading conditions is proposed. According to the system state, the energy management algorithm commands to activate/ deactivate a part or entire of photovoltaic system which can increase the system lifetime and thus increase the system reliability. Another feature of the proposed algorithm is a new mode in the absence of radiation for parallelizing and sharing the battery's current to the load by unidirectional and bidirectional converters. Furthermore, a sensor-less complementary algorithm to determine the amount of radiation and also the partial shading condition is proposed. To prove the feasibility of the proposed algorithms, the system was simulated and constructed. The simulation and experimental results are found closely comparable and confirm the effectiveness of the proposed methods. • New energy management algorithm. • New sensorless approach. • New control strategy. [ABSTRACT FROM AUTHOR]

Visual tracking is one of the pre-eminent tasks in several computer vision applications. Particle filter (PF) is extensively used in visual tracking for intelligent surveillance system applications, hugely significant. But the re-sampling procedure of PF will result in sample impoverishment, which will affect the precision of tracking simultaneously. In this paper, a new tracking technique, called Trigonometric Particle Filter (TPF), based on PF optimized by Sine Cosine Algorithm (SCA), which contains trigonometric sine and cosine functions, is proposed. An enhanced method for improving the number of target particles used in a Sine Cosine Algorithm for trigonometric particle filter includes SCA ahead of the re-sampling step. This step ensures a more extensive particle set Achievement of the proposed TPF tracker is inspected and assessed on Visual Tracker Benchmark (VOT) databases. The proposed TPF tracker is compared with evolutionary-based methods like the Spider monkey optimization assisted PF (SMO-PF), Firefly algorithm-based PF (FAPF) method, Particle swarm optimization-based PF (PSO-PF) and Particle filter, recent four correlation filter-based trackers, and also with other ten state-of-the-art tracking methods. We demonstrate that visual tracking using TPF delivers additional consistent and proficient tracking outcomes than compared trackers. • A new tracking technique, called TPF, based on PF optimized by SCA, is proposed. • The SCA replaces the normal re-sampling process and improves the PF. • TPF compared with evolutionary-based approaches and 10 other leading-edge methods. • TPF visual tracking is more consistent and proficient than other trackers. [ABSTRACT FROM AUTHOR]

This paper proposes a new approach for designing stable hybrid L 1 adaptive controller employing lbest topological model of harmony search (HS) algorithm. The proposed design approach guarantees desired stability and simultaneously provides satisfactory tracking performance for a class of non-linear systems. The design methodology for the controller utilizes the meta-heuristic global search feature of HS algorithm and the local search phenomenon of L 1 adaptive control strategy in tandem. The paper also analytically describes the superiority of lbest topological model compared to the conventional HS algorithm in terms of convergence phenomenon, when hybridized with L 1 adaptive control. The proposed hybrid control methodology has been implemented for benchmark simulation case studies and real-time experimentation to demonstrate its usefulness. • Concurrent mode of hybridization of meta-heuristic optimization technique with the L 1 adaptive control structure for process automation. • Choice of L 1 adaption gains according to the merit of the adaptable parameters. • The guaranteed stability of the proposed design with the superior convergence performance of lbest HS algorithm based L 1 adaptive controller. [ABSTRACT FROM AUTHOR]

The multivariable modeling of a servo actuating system consisting of multi-field multi-armature direct current (MFMADC) machine is extracted and a novel discrete time nonlinear algorithm is proposed for the corresponding system. The proposed control algorithm demonstrates robustness against modeling uncertainty and by utilizing its novel mathematical structure, decouples the dynamical interactions of the connected motors. The main contribution of this paper is the proposition of a new decoupling control algorithm that in which, the driving (commanding) voltages of the connected driving motors are extracted separately and independently using the Lyapunov principle in discrete time. In fact, the obtained coupled stabilizing convex inequalities of the controlling voltages, resulting from the evaluation of the Lyapunov functions, are analytically decoupled using elementary matrix operations. Consequently, each motor now has the capability to perform its controlling task (position control or torque control) with asymptotic stability and robustness against uncertainty. To assess the performance of the proposed controlling algorithm and its verification, a MFMADC machine is attached to a harmonic drive reducer (HDR) whose flex spline and circular spline are fabricated using viscoelastic polyesters PLA and thermoplastic PLA, respectively. A number of experiments are conducted where in the first test, the MFMADC is controlled in only-position mode while in the second test, the MFMADC is controlled in simultaneous position-torque control mode. Comparative assessments confirm that the MFMADC technology is needed when a high precision tracking of position, under high frequency disturbances, is desired. • Offering a New Affordable Solution for Precision Servo Applications of PMDC Machines. • Proposing a New Nonlinear Decoupling Control for Simultaneous Position-Torque Control. • Analytical Robust Stability of Feedback System versus Broad-Band Impedance Disturbance. • Applicability of Proposed High-Precision Servo Actuator Using Regular Micro-Processors. [ABSTRACT FROM AUTHOR]

Schedule-based fieldbus is applied widely in industrial control. Building schedule table (ST) can affect the time-critical performance for communication greatly. Communication jitters can cause some unexpected results in bus traffic scheduling and longer jitters can even cause severe delay in time-critical application. Communication jitter and evenness in arrangement of variables to be scheduled in micro-cycles in ST were two important issues dealing with the performance. An algorithm to build schedule table based on numerical co-prime method was proposed in this paper. Communication jitter in ST was investigated. Concept dealing with rank of ST was introduced. Numerical co-prime method to evaluate rank of ST was proposed. The algorithm and procedure to build schedule table based on the proposed numerical co-prime method were presented and exemplified in detail. Performance analysis and comparison of the proposed algorithm to HCF/LCM method was presented. It was proved that the proposed algorithm can reduce communication jitter to its minimum value and the longest time consumed to schedule variable in one micro-cycle, and greatly reduce the possibility the time consumed to schedule variables exceeded micro-cycle. • Communication jitter in schedule table (ST) with its impact was investigated. • Concept dealing with rank of ST was introduced. • Numerical co-prime method to evaluate rank of ST was proposed. • The algorithm and procedure to build schedule table based on the proposed method was presented • Performance analysis and comparison of the proposed method was presented [ABSTRACT FROM AUTHOR]

In this paper, the event-triggered adaptive fault tolerant tracking control for the strict-feedback nonlinear system with mismatched unknown parameters, external disturbances and actuator faults is addressed. A novel adaptive fault tolerant mechanism is presented to overcome the difficulty arising from the actuator faults and mismatched parameters, where the parameters estimators are generated from discrete state governed by the state dependent event-triggered mechanism. Furthermore, by introducing the auxiliary dynamic, it is shown that all the signals of the system are ultimately bounded and the asymptotical output tracking error is within a small residual set including origin which can be modified arbitrarily small by tuning the control coefficients. Based on the presented adaptive fault tolerant mechanism, the robust event-triggered mechanism and adaptive algorithm are further developed and the boundedness of all the variables of the system is shown. What is more, it is proved that both of the presented event-triggered mechanisms are Zeno-free. Finally, the control effectiveness of the proposed fault tolerant strategy is discussed and verified by a numerical example and the single-link manipulator. • The fault tolerant scheme over the event-triggered network is proposed. • The adaptive estimators for mismatched parameters are continuous and event-triggered. • The auxiliary dynamic is proposed to compensate the measurement error without ISS assumption. [ABSTRACT FROM AUTHOR]

In virtual power plants (VPPs), consensus-based distributed optimal dispatch algorithms aim to collectively minimize the operating cost. As ubiquitous latency on communication networks may lead to divergence, convergence to a nonoptimal solution, or a longer convergence time, mitigating the impacts of arbitrarily large but bounded time-varying delays is significant both in theory and in practice. To modify a typical consensus-based optimal dispatch algorithm under time-varying delays, this paper designs new update rules and introduces a reduction approach to evaluate the performance of the algorithm. The results reveal that the modified algorithm can always converge to the optimal solution with a tactical initial setup in a distributed manner if the undirected interaction topology is connected and the gain parameter is sufficiently small. The analytical expression of the gain is also given. Furthermore, we show that the convergence time is determined by the maximum time delays, the number of generators, and the convergence accuracy. Several numerical simulation studies validate our theory. • The mechanism that delays lead to nonoptimality in consensus-based algorithms is given. • The modified dispatch algorithm converges to optimal solution under bounded delays. • The relationship between the convergence time and the maximum delays is pinpointed. • The maximum delay can be estimated if the algorithm converges in a given time. [ABSTRACT FROM AUTHOR]

The meta-heuristic algorithms have aroused great attention for controller optimization. However, most of them are inseparable from the explicit system models when addressing a constrained optimization problem (COP). In this paper, we propose a data-driven constrained bat algorithm via a gradient-based depth-first search (GDFS) strategy. In the proposed scheme, the GDFS strategy can predetermine a search space that satisfies some strict constraints (e.g., stability requirements) of the optimized system. Meanwhile, an improved boundary constraint handling method is proposed to limit the exploration process to the predetermined space. In this way, the proposed algorithm can solve the COP by utilizing experimental data from real scenes, thereby relieving the dependence on precisely modeling the complex system. Together with an ɛ -constraint-handling method, the bat algorithm is employed to seek the global optimum of the COP. The search performance is enhanced by the designed linear-varying elite layer-based local search and a social learning-based walk mechanism to dynamically balance exploration and exploitation. The convergence is ensured based on the criteria of the stochastic optimization algorithm. Experimental results on a servo drive system and benchmark test functions verify the effectiveness of the proposed algorithm. • Design a data-driven optimization method for the constrained controller tuning. • Design a gradient-based depth-first search strategy for strict constraints. • BA integrates a novel search mechanism to prevent premature convergence. • An improved boundary constraint handling scheme is proposed. [ABSTRACT FROM AUTHOR]

An accurate and efficient control of the Maximum Power Point Tracking (MPPT) and pitch angle for the Wind Turbine (WT) system can provide more energy while also protecting the material. Thus, considering these economic aspects, many controllers are proposed to guarantees their performances. The most studied strategy is the classical PID controller. However, its performance is limited due to the nonlinear model and the difficult wind speed form, which inflict many uncertainties. To solve these drawbacks, an advanced controller named Type-2 Fractional Order Fuzzy PID (T2-FOPID) controller is suggested in this paper to improve the exigence controls. Meanwhile, an efficient optimization technique named the Salps Swarm Algorithm (SSA) has been introduced in order obtaining the best controller parameters. The efficiency of the proposed controller is tested to the 10 kW-WT under various wind speed scenarios. The derived results explicitly indicate that the proposed strategy outperforms the PID controller and other controllers, in terms. the minimum of error, the overshoot and the settling time. • The pricipal controls of the wind turbine system are are the MPPT and the pitch angle. • The authers proposed the Type-2 Fractional order PID controller based on salps swarm algorithm. • The results proved the superiority of proposed controller than the classical PID controller and others are largely suggested in the literature. [ABSTRACT FROM AUTHOR]

Radio Frequency Identification (RFID) has been one of the critical technologies of the Internet of Things (IoT). With the rapid development of the IoT, the RFID systems are required to be more efficient and with high throughput capacity. In the widespread IoT application scenes, the collision problem of the RFID tags has become the increasingly remarkable problem in RFID systems. Traditionally, the anti-collision algorithms of RFID systems are always based on time division multiple access (TDMA). Although the TDMA based anti-collision algorithms are simple and easy to implement, it often misses tags and costs high time. Afterwards, the anti-collision algorithms based on blind source separation (BSS) have been introduced. These BSS based anti-collision algorithms are more efficient and stable, but they are mostly suitable for the determined or overdetermined case, i.e. , the number of tags is less than that of the readers in RFID systems. Only a few anti-collision algorithms are taken into account of the underdetermined collision model. Because this underdetermined RFID collision model will give rise to more difficult solution but with very meaningfully practical IoT applications. Therefore, to investigate high quality underdetermined anti-collision algorithm for RFID system plays an important role in improving the efficiency of RFID system, and enable RFID implement more wide applications in future IoT systems. As a motivation, this paper proposes a new anti-collision algorithm for underdetermined RFID mixed system for performance improvement. In this work, the nonnegative matrix factorization (NMF) with minimum correlation and minimum volume constrains, i.e. , the new MCV_NMF algorithm is proposed for anti-collision application in underdetermined RFID systems. This algorithm combines the independent principle of the tag signals with the NMF mechanism to achieve performance enhancement. The experimental results and analysis corroborate that this new algorithm can implement the underdetermined collision problem well and enhance the throughput capacity of RFID system. • A new underdetermined NMF with minimum correlation coefficient and minimum volume constrains is proposed, i.e., MCV_NMF algorithm. • This new algorithm mechanism fuses the independence feature of the RFID tag signals into NMF constituting new cost function. • The proposed algorithm acquires the improved performance of anti-collision for underdetermined RFID systems. [ABSTRACT FROM AUTHOR]

This paper presents a voltage control mode (VCM)-based super-twisting algorithm-sliding mode controller (STA-SMC) and current control mode (CCM)-based STA-SMC for islanded and grid-connected operations of single-phase inverters interfaced with distributed energy resources (DERs), respectively. The external disturbances are modeled by considering the effects of load currents or load voltages depending on operational modes whiles parametric uncertainties are modeled based on their variations from nominal values. Therefore, the proposed controllers are robust against external disturbances, parametric uncertainties, and variations in loading conditions. Simulation studies are conducted in the MATLAB/Simulink platform for both single and multiple DERs which are interfaced through single-phase inverters to the main grid or loads. The results are also analyzed through comparative studies with different controllers and it is found that the proposed controllers perform better than existing controllers. • A VCM-based STA-SMC is designed for islanded operations of DERs. • A CCM- STA-SMC is designed for grid-connected operations of DERs. • The robustness is analyzed against external disturbances and parametric uncertainties. • Performances are evaluated under different loading conditions. [ABSTRACT FROM AUTHOR]

The energy-efficient train operation methodology is the focus of this paper, and a Q-Learning-based eco-driving approach is proposed. Firstly, the core idea of energy-distribution-based method (EDBM) that converts the eco-driving problem to the finite Markov decision process is presented. Secondly, Q-Learning approach is proposed to determine the optimal energy distribution policy. Specifically, two different state definitions, i.e., trip-time-relevant (TT) and energy-distribution-relevant (ED) state definitions, are introduced. Finally, the effectiveness of the proposed approach is verified in a deterministic and a stochastic environment. It is also illustrated that TT-state approach takes about 20 times more computation time compared with ED-state approach while the space complexity of TT-state approach is nearly constant. The hyperparameter sensitivity analysis demonstrates the robustness of the proposed approach. • The inverse problem of energy-efficient train control problem is formulated. • A data-driven method based on Q-Learning approach is proposed. • Two state definitions based on trip time and energy distribution are introduced. [ABSTRACT FROM AUTHOR]

Abstract This paper presents a speed sensorless control scheme named as finite control set-model predictive current control (FCS-MPCC) using a modified fictitious ohmic quantity (R) based model reference adaptive system (MRAS) for grid-connected doubly-fed induction machine (DFIM) drive. The variables of the reference model of this speed sensorless scheme (R-MRAS) are represented in stationary reference frame while those for the adaptive model are denoted in synchronously rotating reference frame. The sensorless formulation thus obtained is completely independent of any stator/rotor resistance terms. The scheme is also devoid of any stator/rotor flux estimation. Moreover, the intuitiveness of FCS-MPCC brings in additional flexibility in comparison to the conventional control techniques like field oriented control (FOC) and direct torque control (DTC). The overall scheme demonstrates faster execution time than FOC/DTC based control of DFIM drive. The proposed control algorithm is simulated and tested for limited speed range application in MATLAB/Simulink. The validation of simulation results are further done by experimentation on a dSPACE-1103 based DFIM laboratory setup. Highlights • In this paper, MRAS based sensorless speed estimation of DFIM drive by FCS-MPCC is reported. • The estimation scheme insensitive to the changes in machine parameters. • The overall scheme demonstrates faster execution time than FOC/DTC based control of DFIM drive. [ABSTRACT FROM AUTHOR]

Abstract To improve the concurrency of leaders' formation and followers' containment, a difficult problem of designing the formation controller and the containment controller simultaneously should be addressed for networked systems. Motivated by this, this paper presents an even-triggered control framework for networked Euler–Lagrange systems to achieve formation-containment control even in the presence of uncertain parameters. An event-triggered formation controller is firstly designed for leaders to achieve the desired configuration. An event-triggered containment control law is then developed to guarantee that all the followers can converge to the convex hull formed by leaders. The key feature of the containment control law is that it does not necessitate any relative velocity information with respect to neighbor followers. Each controller's gains are adaptively tuned using only local information. The parametric uncertainties are accommodated by using the adaptive updating law. Zeno behaviors of the triggering time sequences are also excluded. As a result, the communication burden of formation-containment system can be reduced. Numerical simulation is finally presented to verify the effectiveness of the proposed event-triggered formation-containment control framework. Highlights • To improve the concurrency of leaders' formation and followers' containment, this paper presents an even-triggered control framework for networked Euler-Lagrange systems with parametric uncertainties to achieve the formation-containment control. • An event-triggered formation controller is firstly designed for leaders to achieve the desired formation configuration. An event-triggered containment control law is then developed to guarantee that all the followers can converge to the convex hull formed by leaders. The key feature of the containment control law is that it does not necessitate any relative velocity information with respect to neighbor followers. • Each designed controller's gains are adaptively tuned using only local information. The parametric uncertainties are accommodated by using the adaptive updating law. The communication burden of the formation-containment system is reduced. [ABSTRACT FROM AUTHOR]

Abstract The paper discusses a niche area problem — the design of reduced-order multiple observers which can achieve the finite-time state reconstruction for Takagi–Sugeno multiple models with unknown inputs. The new reduced-order multiple observer obtained in this paper is only the second ever designed, the author of this paper continuing his work started one year ago with the introduction of the reduced-order multiple observer concept. The obtaining of the new reduced-order multiple observer is achieved by combining two classical reduced-order observers for linear time-invariant multivariable systems with unknown inputs and a full-order multiple observer for Takagi–Sugeno systems. The main innovative idea behind the design of the reduced-order multiple observer for Takagi–Sugeno systems described by the multiple models is the split of the multiple model into two subsystems: an unknown-input-free subsystem and an unknown-input-depending subsystem. The unknown-input-free subsystem is brought to the form of a standard multiple model and, in this situation, any algorithm to design full-order multiple observers can be used in the design of reduced-order multiple observers. The steps of the design procedure have been summarized and software implemented; then, the validation of the suggested algorithm has been done for two concrete cases associated to the motion of an aircraft during its landing Highlights • Paper discusses a niche area problem-design of reduced-order multiple observers. • The reduced-order observer designed is only the second one ever obtained. • The steps of the design procedure have been summarized and software implemented. • The observer's performances are compared with the ones of other observers. [ABSTRACT FROM AUTHOR]

This paper presents a chaos-opposition-enhanced slime mould algorithm (CO-SMA) to minimize energy (COE) cost for the wind turbines on high-altitude sites. The COE model is established based on rotor radius, rated power, and hub height needed to achieve an optimal design model. In this context, an improved variant of SMA, named CO-SMA, is proposed based on a chaotic search strategy (CSS) and crossover-opposition strategy (COS) to cope with the potential weaknesses classical SMA while dealing with nonlinear tasks. First, the COS is introduced to enhance the diversity of solutions and thus improves the exploratory tendencies. The CSS is incorporated into the basic SMA to improve the exploitative abilities and thus avoids the premature convergence dilemma. The proposed CO-SMA is validated on the design of wind turbines with high-altitude sites. Furthermore, the sensitivity analysis based on the Taguchi method is developed to exhibit the impact of the COE model's optimized parameters. The influence of uncertainty based on the fuzziness scheme of wind resource statistics is also explored to depict a real scheme for the changes that occurred by seasonal time, atmospheric conditions, and topographic conditions. The proposed CO-SMA is compared with the PSO, WOA, GWO, MDWA, and SMA, where the COE values are recorded as 0.052408, 0.052462, 0.052435, 0.052409, 0.052413, and 0.052915, respectively. Furthermore, the proposed CO-SMA records the faster convergence than the others. On the other hand, the Taguchi method reveals that the rated power is the most significant parameter on the COE model. Also, the impact of the fuzziness scheme on COE is exhibited, where the increasing interval of vagueness can increase the value of COE. [Display omitted] • Emended slime mould algorithm to minimize the cost of energy. • Opposition and chaos strategies to arrive the promising regions. • Impact of the optimized parameters is analysed using Taguchi method. • Uncertainty of environmental wind statistics is modelled by fuzziness scheme. • Efficacy of the proposed method is affirmed by comparisons with other methods. [ABSTRACT FROM AUTHOR]

The Perturb and Observe (P&O) Maximum Power Point Tracking (MPPT) algorithm in solar Photovoltaics (PV) is popular owing to its simplicity. However, its drawbacks, (i) operating point divergence and (ii) tradeoff between fast convergence and balanced state oscillations decelerate the usage. Most of the developments in the literature to overcome these drawbacks increase complexity. To retain simplicity and also to improve tracking efficiency, this paper proposes a Coarse and fine control algorithm. This proposal has distinct aspects, having three control modes. Mode 1 and 2 enhance fast convergence and mode 3 controls stable state oscillations. The comparative analysis from simulation proves that the proposed technique has fast convergence, reduced balanced state oscillations, better tracking efficiency, and minimum transient power loss than the other techniques. • Coarse and fine control MPPT algorithm is proposed which is unique in its approach. • It splits tracking process into three facets with three control modes. • Selection of apt mode is through nonstop intelligent checks. • Mode 1 and 2 for coarse control, Mode 3 for fine control. • Results confirm the superiorities of proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper studies the robust stability of the fractional-order (FO) LTI systems with polytopic uncertainty. Generally, the characteristic polynomial of the system dynamic matrix is not an affine function of the uncertain parameters. Consequently, the robust stability of the uncertain system cannot be evaluated by well-known approaches including LMIs or exposed edges theorem. Here, an over-parameterization technique is developed to convert the main characteristic polynomial into a set of local over-parameterized characteristic polynomials (LOPCPs). It is proved that the robust stability of LOPCPs implies the robust stability of the uncertain system. Then, an algorithm is proposed to explore system's robust stability through investigating the robust stability of these LOPCPs based on the exposed edges idea. For the sake of feasibility comparison, extensive examples are elaborated that reveal the superiority of the proposed algorithm. • The stability of fractional-order systems with polytopic uncertainty is assessed. • The design space is divided to simplexes and the proposed algorithm searches them. • The direct searching method checks the robust stability of each simplex. • In each simplex, a local over-parameterized characteristic polynomial is generated. • The robust stability (RS) of local simplexes guarantees the RS of the main system. [ABSTRACT FROM AUTHOR]

For the terminal phase guidance problem of the missile intercepting weaving maneuvering target, an adaptive internal model guidance laws in the three-dimensional (3-D) engagement space is proposed in this paper. The guidance law adopts the disturbance rejection theory by treating the target weaving maneuvering accelerations as external disturbance, which comprises of nominal part and adaptive part. The nominal part based on feedback linearization method ensures the whole guidance system stable and the adaptive part based on internal model principle is used to recover the disturbance signals on-line to reject the target maneuver asymptotically. The algorithm guarantees the whole guidance system with satisfying performance both in transient and steady state on the effect of target maneuver on guidance system. The stability analyses and theory proof are provided in this paper. At last, numerical simulations are carried out to illustrate the effectiveness of the proposed guidance law. [ABSTRACT FROM AUTHOR]

This paper investigates a trajectory tracking control scheme for electro-optical tracking systems subject to friction and other nonlinear disturbances. The proposed approach is based on a super-twisting extended state observer (ESO) and a fractional-order nonsingular terminal sliding mode (FONTSM) with a switching-type reaching law. The novel hybrid control scheme exhibits the following advantageous characteristics. First, the extended state observer injected with a super-twisting algorithm is capable of simultaneously estimating the friction and other nonlinear disturbances without detailed knowledge of the precise friction and disturbance models. Second, the FONTSM surface enhances the control accuracy and robustness, and provides a more flexible controller structure than its integer counterpart. Third, a novel switching-type reaching law is utilized to achieve fast response, constraining chattering in the system. Additionally, the finite-time convergence of the adopted ESO and the finite-time stability of the control system are demonstrated based on the rigorous Lyapunov criteria. Finally, the effectiveness of the proposed hybrid control scheme is demonstrated for an electro-optical tracking system via trajectory tracking experiments. • Propose a novel super-twisting ESO to estimate friction and other nonlinear disturbances. • Propose a novel FONTSM controller with a switching-type reaching law. • Validate the finite-time convergence of the super-twisting ESO and the closed-loop system. • Demonstrate the superiorities of the novel hybrid control scheme via experiments. [ABSTRACT FROM AUTHOR]

Model-based methods lose their performance in confronting with model uncertainties and disturbances. Accordingly, some degrees of adaptation to the involved conditions are required. In this paper, a novel robust adaptive scheme is proposed which guarantees the simultaneous identification and control of a system in the presence of external disturbances. Thereafter, the suggested algorithm is implemented on a 2-DOf spherical parallel robot as a stabilizer device. By identifying unknown parameters of Jacobian matrix, the relative identification error is obtained as 0.0207. Applying external excitations to the base, the ratio of end-effector to base orientation is acquired as 0.091, demonstrating proper stabilization in comparison with other two well-known methods. The proposed structure also reveals a reliable performance in tracking desired paths for the end-effector Euler angles. • Introducing a robust adaptive scheme in the presence of external disturbances. • Guaranteeing the simultaneous identification and control of MIMO systems. • Online identification of Jacobian matrix unknown parameters. • Implementation on a 2-DOf spherical parallel robot as a stabilizer. • Demonstrating a reliable performance in tracking desired end-effector path. [Display omitted] [ABSTRACT FROM AUTHOR]

The reliability assessment of train control and management system (TCMS) is essential for the condition monitoring of high-speed train. Different from other general complex systems, the TCMS has the characteristics of multi-system unit, strong coupling and multiple factors. Considering the special system operating environment and high safety requirements of high-speed train. In this paper, for the reliability assessment of TCMS, we propose a new quantitative model based on the evidential reasoning rule and covariance matrix adaptation evolution strategy algorithm, the proposed model offers the following advantages: it has a strong modeling capability for the TCMS reliability, it has an interpretable model assessment process, it can describe the assessment result under probabilistic uncertainty and ignorance uncertainty, and it possesses considerable robustness. To make the model interpretable, an assessment hierarchy is established for the TCMS; to improve model robustness, weights interval is applied to replace the trained weights as the model weights. Several traditional methods are compared with the proposed model to demonstrate its performance, the results show that the proposed model has a better training accuracy. Moreover, a case study is conducted to verify the model's functional feasibility. • An assessment model based on ER rule and CMA-ES is established for TCMS. • Compared with traditional methods, the model has stronger modeling ability. • The model has an interpretable assessment process, and possesses robustness. • The model can describes the assessment result under uncertainty. • A dataset extension method is proposed to enhance the scale of dataset. • The weights interval is applied to replace the optimal weights offered by CMA-ES. [ABSTRACT FROM AUTHOR]

Obtaining a control algorithm capable of navigating the system both in forward and backward motions is one of the control objectives for tractor-trailer wheeled robots (TTWRs). In this paper, a relatively general structure is presented for both forward and backward control of an n-trailer wheeled mobile robot (NTWMR) in the presence of wheel slip effects. To keep better overall performance and track the reference trajectories in forward and backward motions, the NTWMR is intended to be controlled in the presence of slip effects. A control algorithm accompanied by a slip compensation procedure is proposed for the system simultaneously. First, the mathematical model of the system in the presence of slip effects is obtained. A novel physically motivated algorithm is proposed for the tracking control in the presence of unknown uncertainties (longitudinal and lateral slips) for both forward and backward motions. By estimating the slip effects at any instant, the control inputs are produced to compensate for their destructive effects on tracking control of the NTWMR. Then the stability of the closed-loop system is evaluated using the Lyapunov theory. The potential of the proposed controller was verified through several case studies, including comparative results and experimental validation in various motion control manoeuvers for a vehicle with trailers. The proposed method is the first algorithm that can cover a broad range of TTWR motion tasks (forward and backward trajectory tracking, slip attenuation, and global stability), which are required to be developed in NTWMRs. • Proposing a control algorithm for forward and backward trajectory tracking. • Designing the controller in the configuration space without using transformations. • Investigating a slip estimator to attenuate the effects of unknown uncertainties. • Analyzing the stability of the control algorithm using the Lyapunov method. • Presenting various case studies and comparative results to show the effectiveness. [ABSTRACT FROM AUTHOR]

This paper proposes a data-driven method for the detection and isolation of open-circuit faults in multi-phase inverters using measurements of the motor currents. First, feature variables are formulated in terms of the averages of the phase currents and their absolute values. Next, by using an AUto-adaptive and Dynamical Clustering (AUDyC) based on Gaussian Mixture Models, feature data is clustered into different classes characterizing normal and faulty operation modes. Afterwards, these classes are used for deriving appropriate conditions for detecting and labelling faults. The proposed method requires minimal knowledge about the system operation. Furthermore, it allows us to update our knowledge of existing faults online, thus making it possible to detect unknown faults. Moreover, conditions are formulated to describe the influence of the method parameters on the detection time. Once parameters are tuned, the accuracy of the proposed method is illustrated on various experimental data sets, where single and double faults are detected with detection times in the order of the fundamental signal period. • Single and double open-circuit faults of multiphase inverters are investigated. • Unsupervised clustering is used for data-driven fault detection. • Influence of the algorithm parameters on the detection time is formulated. [ABSTRACT FROM AUTHOR]