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In this paper we explore fuzzy string matching in an automatic ticket classification and processing system. We compare performance of the following string similarity algorithms: Longest Common Subsequence (LCS), Dice coefficient, Cosine Similarity, Levenshtein (edit) distance and Damerau distance. Through optimisation, we accomplished a 15% improvement in the ratio of false positives to true positive classifications over the existing approach used by a customer support system for free customers. To introduce greater safety; we compliment fuzzy string matching algorithms with a second layer Convolutional Neural Network (CNN) binary classifier, achieving an improved keyword classification ratio for two ticket categories by a relative 69% and 78%. Such an approach allows for classification to only be applied where a desired level of safety achieved, such as in instances where automated answers. [ABSTRACT FROM AUTHOR]

This paper introduces a modified second order sliding mode algorithm with fixed-time stability analysis based on the Lyapunov function approach. An existing second order sliding mode algorithm is generalized, which provides superior features on convergence rate, accuracy, and robustness against a class of perturbations. The performance of the proposed algorithm is compared with existing algorithms through designing observers first. Then, the proposed algorithm-based controller which displays the fixed-time convergence property is designed to validate its effectiveness and to confirm the theoretical analysis. • A new second order sliding mode algorithm is developed based on the Lyapunov approach. • The algorithm could be applied for designing a controller, observer, or differentiator. • The theoretical analysis shows fixed-time stability of the dynamic system. • A simple step to estimate the upper bound of the convergence time is introduced. [ABSTRACT FROM AUTHOR]

In this paper, a novel grey decision-differential evolution (GD-DE) optimized internal model controller is proposed for the control of nonlinear aeroelastic blade system (ABS) under time-varying uncertainties and control saturations. Two-port internal model control structure is presented for saturation compensation. The filter parameter of the controller is optimally tuned by proposed GD-DE algorithm, which is designed based on the grey decision incidence theory and differential evolution algorithm. The superiority of GD-DE optimized tuning technique is verified, compared to conventional IMC tuning method and other evolutionary algorithm based techniques. The robustness is analysed by comprehensive simulations from time-invariant uncertainties to large periodic or random time-varying uncertainties. The realistic condition with input/output disturbances is also involved. Simulation results show that the proposed controller outperforms existing adaptive IMC controller and other advanced controllers with greatly improved dynamic performance, stronger robustness and better saturation compensation on various conditions. • Nonlinear aeroelastic blade system with large periodic/random time-varying uncertainties. • Design of grey decision modified differential evolution (GD-DE) algorithm. • GD-DE optimized two-port IMC vibration controller is proposed. • A comparative performance analysis with conventional adaptive IMC controller. [ABSTRACT FROM AUTHOR]

In this paper, robust tracking control for a class of nonlinear systems with external disturbance in the presence of input saturation and collision avoidance constraints is investigated. Both of these constraints are considered in a non-quadratic cost function. To obtain optimal control laws, a technique is proposed based on adaptive dynamic programming (ADP) assuming the worst case of disturbance, and without requiring any knowledge of the system dynamics. The proposed algorithm uses neural networks to estimate the optimal cost functions, control policies, and the worst case of disturbance. The Lyapunov theory is used to analyze the convergence of the estimations. Simulation results show the effectiveness of the developed ADP method. • An ADP-based method to design control laws for a nonlinear multiagent system. • Considering external disturbance, saturation constraints and collision avoidance. • NN to estimate the optimal cost function, and the worst case of disturbance. • Integral Reinforcement Learning method to find the optimal control law. • Lyapunov theory is used to show that the NNs weights and tracking errors remain UUB. [ABSTRACT FROM AUTHOR]

This paper presents a novel vibration signal fusion algorithm using improved empirical wavelet transform and variance contribution rate to fuse three-channel vibration signals for weak fault detection of hydraulic pumps. Firstly, empirical wavelet transform (EWT) is utilized to decompose the three-channel signals into several AM–FM components. Then in accordance with the statistical characteristics of these component data, variance contribution rate is defined to measure the weight of component data points. A series of fusion coefficients are computed and assigned to every component point. Finally, these component points are fused into one single signal and Hilbert transform is conducted to demodulate the fault characteristic frequency for weak fault detection. Moreover, to address the issue of improper EWT spectrum segmentation, we introduce Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to improve EWT in the full space and the frequencies corresponding to outlier points are taken as the boundaries of spectrum segmentation. Therefore, the number of boundaries is more reasonable and the AM–FM components are more consistent with inherent components existing in the vibration signals of pumps. Results of simulation and experiment analysis demonstrate the good performance of the exhibited fusion algorithm in weak fault detection of hydraulic pumps. • An improved EWT based on the full scale space and DBSCAN is proposed. • Variance contribution rate is applied to the fusion of vibration signals of pumps. • A novel signal fusion framework is proposed for weak fault detection of pumps. [ABSTRACT FROM AUTHOR]

In this paper, we consider a fixed-time autonomous ship landing control design for helicopters subject to asymmetric output constraints, model uncertainties and external perturbations. By incorporating a universal barrier function into the backstepping design, an output-constrained fixed-time control algorithm is proposed, where a new adaptive estimation method is introduced to compensate the effects resulting from uncertainties and disturbances. To avoid crash and overcome the limitation of the helicopter's under-actuated property, the whole landing operation is completed in a dual-phase landing sequence with two controllers, which are both designed based on the output-constrained fixed-time control algorithm. The proposed control strategy guarantees that the tracking and landing errors converge into a small neighborhood of zero in a fixed settling time without violating the constraint requirement. Numerical comparative simulations are executed to further verify the prominent control performance. • We present an output-constrained fixed-time control method for autonomous ship landing of the helicopter with model uncertainties and disturbances. • A universal barrier function is incorporated into the backstepping design to guarantee the fixedtime convergence of tracking errors under asymmetric constraint requirements. • A modified adaptation algorithm is proposed to estimate and compensate the lump disturbances within fixed time. • Comparative simulations show that the method has an excellent performance. [ABSTRACT FROM AUTHOR]

Operational Technology (OT) systems are merging towards a conjoint architecture with the advances in communication networks and emerging standards such as IEC/IEEE 60802 for industrial automation, automotive, power and energy and other areas. In this paper, we present a Dependable Control System (DepCS) with Model Predictive Control (MPC) algorithm that works in such architectures using multiple MPC controllers (of a feedback control loop) to enhance the operational reliability. We termed this as Dependable Model Predictive Control (DepMPC) system. The reliability enhancement of a DepMPC system is achievable thanks to the fault-tolerance of multiple MPC controllers and the tractable information flows with Time-Sensitive Networking (TSN). Here, our discussion was focused only on the logical connectivity and not the hardware architecture. The numerical simulations are studied with three multi-variable plants that have control constraints. In this study, we introduced a Replacement Controller (RC) to improve the control performance of the DepMPC system. The combination of both the Replacement Controller and Dependable Model Predictive Control (RC-DepMPC) system proves a promising solution for actual implementations. • Dependable Model Predictive Control (DepMPC) with a software-defined architecture for Operational Technology (OT) systems compatible with the emerging IEC/IEEE 60802 standards. • Self-management of redundant controllers for smooth transferring between the standby and duty modes within a deterministic time thanks to time-sensitive networking. • Replacement Controller (RC) in companion with the redundant controllers for improving the control performance of a DepMPC. • Numerical simulations with three multi-variable plants and different choices of performance variables for illustration of the merits of the proposed framework. [ABSTRACT FROM AUTHOR]

In this paper, a fault diagnosis (FD) and fault tolerant control (FTC) scheme is proposed for the leader–follower based multiple manipulator system with sensor fault under network communication topology. The learning observer is designed to obtain the fault information when the time-varying sensor fault occurs in follower manipulators. The synchronization error between the leader manipulator and the follower manipulator is defined based on the graph theory. The distributed multiple manipulator synchronization controller is designed for each follower manipulator which contains the value of fault estimation and RBF neural network based approximator. Correspondingly, the stability analysis of the error system is analyzed by the Lyapunov stability theorem. Finally, a simulation example is given to prove the effectiveness of the algorithm. • The learning observer is designed to obtain the sensor fault information. • The manipulator synchronization FTC scheme is designed for the follower manipulator. • The FTC controller can reduce the impact of the sensor fault. [ABSTRACT FROM AUTHOR]

In this paper, accuracy of the existing disturbance observer (DO) for disturbance estimation is increased by increasing its order. Stability of the proposed higher order disturbance observer (HODO) is analysed. Furthermore, a new HODO based super-twisting control (STW) algorithm is presented for the system with the bounded matched disturbances. A HODO based sliding mode control (SMC) algorithm with the integral sliding surface is proposed where the controller gains depend on the disturbance estimation error bound instead of the disturbance magnitude. Therefore, controller gains required in the proposed approach for the disturbance rejection are lesser as compared to the STW. As a result of which, with the proposed control, substantial chattering attenuation better than the STW is achieved. The proposed control technique is successfully applied to the single-input single-output (SISO) Industrial Emulator system with matched disturbance and the multi-input multi-output (MIMO) coupled 2-DOF Helicopter system with complex disturbances. The simulation and experimental outcomes illustrate the effective implementation of the proposed control strategy for tracking desired input, achieving stabilization and robustness of the system in the existence of bounded complex matched disturbances. • Accuracy of the existing disturbance observer is increased by increasing its order. • Stability of the proposed higher order disturbance observer is analysed. • Super-twisting control using higher order disturbance observer has been presented. • With the proposed control, substantial chattering attenuation is achieved. • The proposed control is applied to SISO and MIMO ststems. [ABSTRACT FROM AUTHOR]

This paper presents an adaptive model predictive control (MPC) method based on disturbance observer (DOB) to improve the disturbance rejection performance of the image-based visual servoing (IBVS) system. The proposed control method is developed based on the depth-independent interaction matrix, which can simultaneously handle unknown camera intrinsic and extrinsic parameters, unknown depth parameters, system constraints, as well as external disturbances. The proposed control scheme includes two parts which are the feedback regulation part based on the adaptive MPC and the feedforward compensation part based on the modified DOB. Unlike the traditional DOB that is based on the fixed nominal plant model, the modified DOB here is based on the estimated plant model. The adaptive MPC controller consists of an iterative identification algorithm, which not only can provide the model parameters for both the controller and the modified DOB, but also can be used to control plant dynamics and to minimize the effects of DOB. Simulations for both the eye-in-hand and eye-to-hand camera configurations are conducted to illustrate the effectiveness of the proposed method. • The compound control method based on adaptive model predictive control and modified disturbance observer is proposed. • The proposed control method is proposed to improve the disturbance rejection performance of the uncalibrated and constrained image-based visual servoing system. • Unlike the traditional disturbance observer, the modified disturbance observer here is designed based on the estimated plant model. • The identification algorithm is incorporated into the adaptive model predictive control to minimize the model uncertainty and the influences of the disturbance observer. [ABSTRACT FROM AUTHOR]

Information fusion of the GPS/INS integrated system is always related to characteristics of the inertial system and the sensor feature, yet prior knowledge is still difficult to obtain in real applications. To deal with the uncertainty of error covariance and state noise in vehicle navigation, this paper presents a novel approach, wherein the integration of Square-root Cubature Kalman Filters (SCKF) and Interacting Multiple Model (IMM) are also introduced. In the framework of IMM, the SCKFs with different covariance are designed to reflect various vehicle dynamics. Besides, since the IMM-SCKF can switch flexibly among the filters, the transition probability matrix is computed with maximum likelihood method to adapt to different noise characteristics. The performance of the proposed algorithm is guaranteed by theoretical analyses, and a series of vehicular experiments with different maneuvers are carried out in an urban environment. The results indicate that, in comparison with the CKF and the IMM-CKF, the accuracy of velocity and attitude are increased by the proposed strategy. • In the framework of IMM, a novel approach is proposed to deal with the uncertainty of error covariance and state noise. • The transition probability matrix is computed with recursive maximum likelihood method to adapt to different noise characteristics. • The Square-root CKF based on inertial navigation error model and measurement model is introduced. • The vehicle navigation experiments with different maneuvers are designed and implemented. [ABSTRACT FROM AUTHOR]

A methodology for evolving fuzzy Kalman filter identification, is proposed in this paper. The mathematical formulation contemplates the following aspects: for initial estimation, an offline GK clustering algorithm and an offline fuzzy version of OKID algorithm are used to estimate antecedent and consequent parameters, respectively. From each new sample of input–output experimental data from dynamical system, the evolving eTS algorithm and an evolving fuzzy version of OKID algorithm are used to estimate the antecedent and consequent parameters of the evolving fuzzy Kalman filter, respectively. Computational and experimental results considering the estimation of states and outputs of a nonlinear dynamic system and a 2DoF helicopter, respectively, show the efficiency and applicability of the proposed methodology. • Adaptive fully data-driven method suitable to online and real-time applications. • Fast processing of data streams, minimizing the memory allocation. • Robustness to the uncertainties on the experimental data. [ABSTRACT FROM AUTHOR]

In this paper, the issue of position tracking control of servo system with external disturbance is studied. A novel fractional-order sliding mode control (FOSMC) strategy is proposed. To be specific, firstly, the motor parameter drift, additional unknown disturbance and the uncertainty of load torque are considered as lumped disturbance, and a nonlinear disturbance observer (NDO) is designed to estimate the disturbance. Secondly, fractional-order terminal sliding mode surfaces (FOTSMS) are designed for the current loop and the position–velocity loop, respectively, as well as the FOSMC is presented based on NDO, in which FOSMC of position–velocity loop introduces second-order super-twisting term to reduce the chattering of the system. Furthermore, the stability of the FOSMC is analyzed. Finally, the effectiveness of the proposed algorithm is verified by simulations and experiments. • A novel fractional-order sliding mode control (FOSMC) strategy is proposed. • A nonlinear disturbance observer (NDO) is designed to estimate the disturbance. • A fractional-order terminal sliding mode surfaces (FOTSMS) are designed for the current loop and the position–velocity​ loop. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of event-triggered active disturbance rejection control of nonlinear network control system in simultaneous presence of unknown dynamics, physical attacks and denial-of-service attacks. Considering the network congestion, an event-triggered communication mechanism is adopted, where the measurements are transmitted only when an 'event' occurs. An indicator obeying the Markov jump rule is introduced to characterize the randomly occurring denial-of-service attacks. In virtue of the extended state observer (ESO), the states and the total disturbance which involves both unknown dynamics and physical attacks are estimated. Then, the composite controller is designed which could stabilize the overall controlled system and eliminate the so-called total disturbance. Sufficient conditions are established to guarantee the stability of the observer system and overall controlled system. The cone-complementarity linearization algorithm is adopted to obtain the parameters of the extended state observer and the controller. Finally, both numerical and application design examples demonstrate the feasibility and efficacy of the proposed method. Simulation results show that the steady-state error within 0.02 can be achieved within 3 s. • A comprehensive control model which is subjected to DoS attack, physical attack and unmodeled nonlinearities is put forward. • An event-triggered ADRC protocol is design for the nonlinear system with physical attack and Markovian jump DoS attack. • The sufficient conditions are provided such that the corresponding augmented system is exponentially mean square bounded stable. [ABSTRACT FROM AUTHOR]

This paper proposes a new identification method based on an exponential modulation scheme for the determination of the coefficients and exponents of a fractional-order transfer function. The proposed approach has a broader scope of application compared to a previous method based on step response data, in that it allows for the use of arbitrary input signals. Moreover, it dispenses with the need for repeated simulations during the search for the best fractional exponents, which significantly reduces the computational workload involved in the identification process. Two examples involving measurement noise at the observed system output are presented to illustrate the effectiveness of the proposed method when compared to a conventional output-error optimization approach based on the polytope algorithm. In both examples, the proposed method is found to provide a better trade-off between computational workload and accuracy of the parameter estimates for different realizations of the noise. • Time-domain identification of coefficients and exponents of fractional-order transfer functions. • General method allowing for the use of arbitrary input waveforms. • New evaluation index for searching the best fractional exponents. • 100-fold reduction in computational workload compared to a previous method. [ABSTRACT FROM AUTHOR]

The framework of locally weighted learning (LWL) has established itself as a popular tool for developing nonlinear soft sensors in process industries. For LWL-based soft sensors, the key factor for achieving high performance is to construct accurate localized models. To this end, in this paper a nonlinear local model training algorithm called nonlinear Bayesian weighted regression (NBWR) is proposed. In the NBWR, the nonlinear features of process data are first extracted by the autoencoder; then, given a query sample a local dataset is selected on the feature space and a fully Bayesian regression model with differentiated sample weights is developed. The benefits of this approach, which include better consistency of correlation, stronger abilities to deal with process nonlinearities and uncertainties, overfitting and numerical issues, lead to superior performance. The NBWR is used for developing a soft sensor under the LWL framework, and a real-world industrial process is used to evaluate the performance of the NBWR-based soft sensor. The experimental results demonstrate that the proposed method outperforms several benchmarking soft sensing approaches. • A nonlinear locally weighted approach (NBWR) is proposed for soft sensor development. • Fully Bayesian local models are constructed on the feature space. • Extensive comparisons are carried out on benchmark industrial dataset. [ABSTRACT FROM AUTHOR]

This paper proposes a novel similarity-based algorithm for Remaining Useful Life (RUL) prediction and a methodology for machine prognostics. In the proposed RUL prediction algorithm, a Similarity Matching Procedure including the Kernel Two Sample Test (KTST) is developed to query similar run-to-failure (R2F) profiles from historical data library. Next, the preliminary predictions of RUL are obtained as remaining time-to-failure from the similar R2F records. In the last step, Weibull analysis is performed to fuse the preliminary predictions and to obtain the probability distribution of RUL. Moreover, a methodology for machine prognostics is developed based on the RUL prediction algorithm. Compared with existing similarity-based methods for RUL prediction, the proposed method holds several advantages: 1) the similarities between sensor readings or feature matrices are directly measured without extra health assessment procedure; 2) the proposed method presents good probabilistic interpretations of the prediction uncertainties; 3) the estimated RUL distribution is statistically sound by applying KTST to prescreening the historical R2F records. The effectiveness and the superiority of the proposed method are justified based on the public aero-engine dataset. • A similarity-based method is proposed for remaining useful life prediction. • The method combines kernel two sample test and maximum mean discrepancy. • The method improves accuracy and presents prediction uncertainties. • The result is validated on public aero-engine data for prognostics. [ABSTRACT FROM AUTHOR]

The Pareto game for the model-free continuous-time stochastic system is studied through approximate/adaptive dynamic programming (ADP) in this paper. Firstly, the model-based online iterative algorithm is proposed, and it is proved that the control iterative sequence converges to the Pareto efficient solution, but the algorithm requires complete system parameters. Then, we derive the model-free iterative equation and develop the ADP algorithm to calculate the equation by collecting updated states and input information online. From the derivation of the ADP algorithm, the model-free iterative equation and the model-based iterative equation have the same solution, which means that the ADP algorithm can approximate the Pareto optimal solution. Next, the convergence analysis shows that the Pareto optimal strategy is uniquely determined by the ADP algorithm. Finally, two simulation examples confirm the feasibility of the ADP algorithm. • Solving the Pareto optimal control problem usually needs full system information. • The Pareto game problem is studied for model-free mean-field system. • Firstly, the Pareto optimal control is given by an iterative algorithm. • Next, an adaptive dynamic programming (ADP) method is used to get the Pareto game. • Lastly, the effectiveness of the ADP method is verified by two examples. [ABSTRACT FROM AUTHOR]

This paper presents a new method for recognition of nine control chart patterns (CCPs) based on the intelligent use of shape and statistical features and optimized fuzzy system. The proposed technique contains three levels of separation. In each level of separation, an effective set of shape and statistical features are utilized as the input of classifier for recognizing a part of patterns. Due to the good performance of the adaptive neuro-fuzzy inference system (ANFIS) in pattern recognition problems, in the proposed method an ANFIS is used as a classifier at each level of separation which is trained by chaotic whale optimization algorithm (CWOA). Intelligent utilization of new extracted features, improving robustness of ANFIS and considering nine patterns in CCP recognition problem are the main contribution of the proposed method. The simulation results showed that the proposed method performs better than other similar methods and can recognize the type of pattern with 99.77% accuracy. • Intelligent application of features has high effect on classifier's performance. • Learning algorithm of ANFIS is an important issue that affects its accuracy. • The proposed method can recognize nine type of CCPs. • The results showed that the proposed method performs better than other methods. [ABSTRACT FROM AUTHOR]

Early bearing fault detection is crucial to avoid catastrophic accidents. However, the repetitive defect impulses indicating bearing fault are buried in heavy background noise. In the paper, a novel periodical sparse low-rank (PSLR) matrix estimation algorithm is proposed for extracting repetitive transients from noisy signal. Concretely, periodical group sparsity and low-lank property of fault transients in time–frequency domain are first revealed, and then an optimization problem is proposed for simultaneously promoting these two properties. Meanwhile, to further highlight the sparsity of fault features, the non-convex penalty functions are incorporated into the optimization problem. Then, for solving the proposed optimization problem, an iterative algorithm is derived based on alternating direction method of multipliers (ADMM) and majorization–minimization (MM), in which the traditional soft-thresholding operation is replaced by the proposed Gini-guided fault information thresholding (FIT) scheme to enhance fault transient extraction. Finally, simulated and real signals confirm the performance of proposed PSLR in extracting defect impulses from noisy vibration signal. • A novel periodical sparse low-rank (PSLR) matrix estimation algorithm is proposed. • The Gini-guided fault information thresholding (FIT) scheme is proposed. • The effectiveness of proposed PSLR is verified by simulated and real signals. [ABSTRACT FROM AUTHOR]

Passivity-based control (PBC) is model-based, and as a result, it is affected by uncertainties. Besides, for some PBC designs, partial differential equations (PDEs) should be solved in order to obtain the parameters of the controller. Actor–critic (AC) algorithm has been used to regulate PBC parameters instantaneously and solve PDEs online. Despite the benefits of this algorithm, it cannot handle disturbance. The main purpose of this paper is to modify the AC algorithm with an online wavelet function approximation to improve its performance. The proposed method uses PBC and the modified AC algorithm to attenuate the effects of uncertainties. Moreover, the stability analysis is investigated for a nonlinear system. The results show that the new method can handle disturbance and uncertainties more proper than other methods. • A realistic model of the boost converter with time-varying load and disturbance is considered. • A new nonlinear control strategy is used for designing the controller. • The new method utilizes passivity based control (PBC) and the modified actor–critic (AC) algorithm. • Online wavelet function approximation (OWFA) is applied as an approximator. [ABSTRACT FROM AUTHOR]

Efficient energy management for the solid rocket is very important to exhaust excess energy and to meet the various terminal constraints. Based on the online planning of the velocity capability curve, a Double-arcs Energy Management (DAEM) guidance algorithm is proposed in this paper. In the DAEM, the velocity capability curve is designed with two tangential arcs. By using the elegant geometric characteristic of the arcs, the closed-loop guidance problem is transformed as a problem of solving a system of nonlinear equations about the parameters (radius and center position) of the two arcs, and the nonlinear equations can be solved in real time. The main advantages of this algorithm are that the height increment constraint, which is difficult to be satisfied in traditional energy management methods, can be satisfied theoretically, and the convergence of the solution of the DAEM is quadratic. The effectiveness and advantages of the proposed DAEM algorithm are demonstrated by simulations and comparisons with other energy management algorithms. • A Double-arcs Energy Management (DAEM) algorithm is presented by using the geometric characteristic of the double-arcs velocity capability curve. • The solution of the DAEM algorithm is analyzed to be at least quadratic convergence, which means that the guidance command can be obtained in real time. • The feasibility and effectiveness of the proposed DAEM algorithm are illustrated by carefully designed tests and the comparisons with other traditional energy management algorithms. [ABSTRACT FROM AUTHOR]

In the field of vision-based attitude estimation, camera model and attitude solving algorithm are the key technologies, which determine the measurement accuracy, effectiveness and applicability. Aiming at this issue, in this paper we probe into the generic imaging model and then develop a corresponding generic camera calibration method using two auxiliary calibration planes. The camera model is named as imaging ray tracking model. Based on the imaging ray tracking camera model and with the knowledge of the calibration parameters, an advanced attitude solving algorithm, imaging ray tracking model and attitude from orthographic projection with iterations algorithm, is deeply investigated, which is inspired by the classical POSIT algorithm. The initial attitude value is provided by the orthographic projection of the object on the two calibration planes and then refined by iteration to approximate the true object attitude. Experimental platform is setup to conduct the imaging ray tracking camera calibration procedure and further evaluate our attitude estimation algorithm. We show the effectiveness and superiority of our proposed attitude estimation algorithm by thorough testing on real-data and by comparison with the POSIT algorithm. • The mapping model is expressed by non-linear high order polynomial. • AOPIT is based on IRT model associating image coordinates and space projection ray. • The specific implementation procedure of the proposed IRT-AOPIT is given in detail. [ABSTRACT FROM AUTHOR]

This paper presents a human indoor location design on improved credit and dynamic pruning algorithm in passive binary pyroelectric infrared (PIR) sensor tracking system. Compared with previous credit location and anti-logic location, the improved credit method can keep all effective measurement points of different human targets without extra logical judgment. It is more intuitive and less complex than the way of anti-logic with same accuracy. The dynamic pruning algorithm can separate different targets into different two-dimensional planes to reduce the number of measurement points and assign effective measurement points to correct targets according to the predicted position of targets. Multiple human location is then transformed into single human location. A small and flexible PIR sensor system is used to test the improved location algorithm in multiple human tracking. The simulation and experimental results indicate the algorithm works well. [ABSTRACT FROM AUTHOR]

Abstract: Complexity of industrial plants and their stringent environmental and safety regulations have necessitated early detection and isolation of process faults. All the existing fault isolation methods can be categorized into two general groups: model-based and data-based. Transfer entropy is a data-based method for measuring propagation direction of disturbance and finding its root cause. In this paper, a new transfer entropy-based method is proposed to isolate different process faults. The novelty of this paper lies in using the transfer entropy idea to generate distinct patterns of information flow among process variables, recognize their correlations in the context of the transferred information in any abnormal condition, and finally isolate different process faults. The experimental results clearly demonstrate the superiority of the proposed method to the conventional methods. [Copyright &y& Elsevier]

This paper presents a novel control strategy to improve the damping capability of sub-synchronous oscillations by tuning of Linear Quadratic Regulator (LQR) optimally in order to reduce the fluctuations in the power system. The proposed model includes the coordination of Power System Stabilizer (PSS) and Thyristor-Controlled Series Capacitors (TCSC) in combination with LQR controllers which is formulated as an optimal control problem. The problem is formulated as a linear regulator problem and then the Differential Evolution (DE) algorithm is utilized to optimize the proposed controlling parameters. Several cases have been studied to show the efficiency of the proposed strategy. Obtained results from a case study on a typical generator demonstrated that the proposed method has the best response and quickest function among conventional controller systems. Moreover, the coordination of the LQR with the other control systems, as an optimal linear regulator problem in order to damp system oscillations provides robust stability for optimizing system performance index. Besides, the flexibility and usability of the LQR controller guarantee the stability of the system to cope with the oscillations. • Improving the damping sub-synchronous oscillations by the coordination of PSS and TCSC. • Using optimal control theory for power system damping. • Designing a robust PSS and TCSC using DE in a single-machine environment. • Improving dynamics and Control of a power System. [ABSTRACT FROM AUTHOR]

Abstract Bilinear systems are considered as a particular class of nonlinear systems including the state variables which are typically used for online identification. By using a recursive identification method and the maximum likelihood principle, this paper presents two recursive-based algorithms to identify the parameters of bilinear in parameter systems with ARMA noise. In this regard, recursive generalized extended least squares ( RGELS ) and recursive Maximum Likelihood ( RML ) algorithms have been proposed for identification of bilinear systems. These algorithms can be used as an alternative choice in system identification with acceptable performance. The proposed algorithms estimate the correlated noise parameters with high accuracy by making full use of the measurement data. Simulation results indicate that the proposed algorithms are effective for online identification of bilinear in parameter systems with high convergence speed. Highlights • Two recursive methods are presented for parameter identification of bilinear systems. • The proposed algorithms have less computational complexity and good performance. • These algorithms are proposed to improve convergence speed and accuracy. [ABSTRACT FROM AUTHOR]

Abstract Rolling element bearings (REBs) play an essential role in modern machinery and their condition monitoring is significant in predictive maintenance. Due to the harsh operating conditions, multi-fault may co-exist in one bearing and vibration signal always exhibits low signal-to-noise ratio (SNR), which causes difficulties in detecting fault. In the previous studies, maximum correlated kurtosis deconvolution (MCKD) has been validated as an efficient method to extract fault feature in the fault signals. Nonetheless, there are still some challenges when MCKD is applied to fault detection owing to the rigorous requirements of multiple input parameters. To overcome limitation, a multi-objective iterative optimization algorithm (MOIOA) for multi-fault diagnosis is proposed. In this method, correlated kurtosis (CK) is taken as a criterion to select optimal Morlet wavelet filter using the whale optimization algorithm (WOA). Meanwhile, to further eliminate the effect of the inaccurate period on CK, the update process of period is incorporated. After that, the simulated and experimental signals are utilized to testify the validity and superiority of the MOIOA for multiple faults detection by the comparison with MCKD. The results indicate that MOIOA is efficient to extract weak fault features even with heavy noise and harmonic interferences. Highlights • The paper proposes a multi-objective iterative optimization algorithm (MOIOA) which can simultaneously accomplish the optimization of the Morlet wavelet filter and fault period. • Correlated kurtosis is selected as objective function and the Morlet wavelet filter is optimized by whale optimization algorithm (WOA). • MOIOA is robust to inaccurate predetermined period and can make period gradually approach the real value. [ABSTRACT FROM AUTHOR]

Abstract As the fault shock component in vibration signals is extremely sparse and weak, it is difficult to extract the fault features when large-scale, low-speed and heavy-duty mechanical equipment is in the early stage of failure. To solve this problem, an early fault feature extraction method based on the Teager energy operator, combined with optimal variational mode decomposition (VMD) is presented in this study. First, the Teager energy operator was used to strengthen the weak shock component of the original signal. Next, a logistic–sine complex chaotic mapping with variable dimensions was constructed to enhance the global search ability and convergence speed of the pigeon-inspired optimization (PIO) algorithm, which is named the variable dimension chaotic pigeon-inspired optimization (VDCPIO) algorithm. Then, the VDCPIO algorithm is used to search for the optimal combination value of key parameters of VMD. The enhanced vibration signal is decomposed into a set of intrinsic mode functions (IMFs) by the optimized VMD, and then kurtosis for every IMF and mean kurtosis of all IMFs are extracted. According to the average kurtosis, several IMFs, whose kurtosis value is greater than the average kurtosis value, are selected to reconstruct a new signal. Then, envelope spectrum analysis of the reconstructed signal is carried out to extract the early fault features. Finally, experimental verification of the method was performed using the simulated signal and measured signal from a rolling bearing; the experimental results indicate that the method presented in this paper is more effective to extract the early fault features of this kind of mechanical equipment. Highlights • A method of early fault feature extraction of bearings using Teager-VMD is proposed. • VDCPIO has better global searching ability and convergence speed. • Optimal VMD is obtained by using the VDCPIO algorithm. • Teager operator is used to enhance the shock components in the original signal. • Results demonstrate that the proposed method has better performances than VMD. [ABSTRACT FROM AUTHOR]

Abstract This paper proposes a novel adaptive super-twisting fractional-order nonsingular terminal sliding mode (AST–FONTSM) control scheme using time delay estimation (TDE) for the cable-driven manipulators. The designed control scheme utilizes TDE to obtain the estimation of system dynamics, and therefore no system dynamic model information will be required. Afterwards, AST and FONTSM schemes are applied to ensure good control performance in both reaching and sliding mode phases. Due to the adoption of AST scheme, good robustness and high control precision are obtained in the reaching phase, while the boundary information of the lumped uncertainties will be no longer required. Thanks to the utilization of FONTSM error dynamics, fast convergence and accurate tracking and strong robustness can be simultaneously ensured in the sliding mode phase. Corresponding comparative simulation and experimental results demonstrate the effectiveness and superiorities of our proposed method over the existing control methods. Highlights • Propose a novel AST scheme using a newly designed adaptive algorithm. • Propose a novel AST–FONTSM control scheme with TDE technique. • Perform the stability analysis considering the AST and FONTSM error dynamics. • Demonstrate the superiorities of proposed method through both simulations and experiments. [ABSTRACT FROM AUTHOR]

Abstract An improved robust cubature Kalman filter (RCKF) based on variational Bayesian (VB) and transformed posterior sigma points error is proposed in this paper, which not only retains the robustness of RCKF, but also exhibits adaptivity in the presence of time-varying noise. First, a novel sigma-point update framework with uncertainties reduction is developed by employing the transformed posterior sigma points error. Then the VB is used to estimate the time-varying measurement noise, where the state-dependent noise is addressed in the iteratively parameter estimation. The new filter not only reduces the uncertainty on sigma points generation but also accelerates the convergence of VB-based noise estimation. The effectiveness of the proposed filter is verified on integrated navigation, and numerical simulations demonstrate that VB-RCKF outperforms VB-CKF and RCKF. Highlights • An improved CKF with both adaptivity and robustness is proposed and verified. • The novel sigma points update framework accelerates the convergence of VB. • The distribution dependence consisted in VB algorithm is considered. • Performance of different filters are compared in statistics by integrated navigation. [ABSTRACT FROM AUTHOR]

Abstract High-frequency resonant modes appearing in the flexure-based motion systems are the key factors that limit the motion/positioning performance. This paper presents an approach to design the fixed-order (low-order) controller for the flexure-based motion system, which is used for industrial optical fibre transceiver alignment and assembly. The uniqueness of the proposed algorithm is that one single controller can simultaneously stabilize the uncertain high-frequency resonant modes for both x and y -axis motion. Especially, the resulted controller can significantly enhance the tracking and disturbance rejection ability in low frequency. The novelty of the proposed method is mainly twofold. First, the finite-frequency specifications are introduced to robust control of the polytopic systems. As the entire-frequency specifications have the trend to place the dominant poles towards the central polynomial, the finite-frequency specifications largely relax the conservatism for the performance optimization. Second, a new and practical method is proposed to choose the central polynomial by designing a nominal controller, where two different central polynomials are utilized for x and y -axis separately for the practical applications, so that the stability and performance are guaranteed but the conservatism is reduced. Experiments are evaluated on the 2 DOF flexure-based motion system, and the results show that the tracking error using designed fixed-order controller has a more than 50% reduction compared with the same-order notch filter based nominal controller, with respect to both the sinusoidal and triangular references. Highlights • An approach to design the fixed-order (low-order) controller for the flexure-based motion system. • The finite-frequency specifications are introduced to relax the conservatism. • A practical method is proposed to choose the central polynomial by designing a nominal controller. [ABSTRACT FROM AUTHOR]

Abstract In this paper, a novel interval type-2 fuzzy fractional order super twisting algorithm (IT2FFOSTA) which is essentially a second order sliding mode controller is presented. The proposed IT2FFOSTA enhances fractional order super twisting algorithm (FOSTA) by taking advantage of an interval type-2 fuzzy fractional order sliding surface (IT2FFOSS) for some classes of fully-actuated and under-actuated nonlinear systems in presence of uncertainty. The FOSTA significantly reduces the amount of chattering and the IT2FFOSS results in decreasing the tracking error, control effort, and chattering level. In order to control under-actuated systems, a hierarchical sliding surface is employed. The multi-tracker optimization algorithm is utilized to adjust the controller's parameters; this leads to an optimal performance for the IT2FFOSTA. To examine the performance of the IT2FFOSTA, some simulation and experimental tests on three examples of different classes of fully-actuated and under-actuated systems, including ball and plate, inverted pendulum, and ball and beam systems are carried out. The simulation and experimental results demonstrate the superiority of the IT2FFOSTA in reducing the amount of chattering, tracking error, and control effort compared to those of the other control methods. Highlights • Introducing a novel fractional order super twisting algorithm (FOSTA) with an interval type-2 fuzzy fractional order sliding surface (IT2FFOSS). • Reducing chattering level and tracking error with less control effort. • Applicable to both fully-actuated and under-actuated uncertain systems. • Tuning the controller parameters using multi-tracker optimization algorithm in order to achieve an optimal performance. • Evaluating the performance of the proposed control method by performing several simulation and experimental tests on three types of benchmark systems such as ball and plate, ball and beam, and inverted pendulum. [ABSTRACT FROM AUTHOR]

Abstract This paper proposes an event-triggered distributed receding horizon control (DRHC) approach for the formation and tracking problems of homogeneous multi-agent systems. For each agent, an event-triggering condition, based on assumed predictive information of the neighbours, is derived from stability analysis. Considering the uncertain deviation between the assumed and true predictive information, we design a time-varying compatibility constraint for the individual optimization problem. In the event-triggered DRHC algorithm, each agent solves the optimization problem and communicates with its neighbours only when the event-triggering condition is satisfied, so the communication and computation burden are reduced. Moreover, guarantees for the recursive feasibility and asymptotic stability of the overall system are proved. A simulation example is provided to illustrate effectiveness of the proposed approach. Highlights • An event-triggered distributed receding horizon control (DRHC) approach aimed at the formation and tracking problems of the homogeneous multi-agent systems is proposed. • The event-triggering condition for each agent is designed based on stability analysis. • Guarantees for the recursive feasibility and asymptotic stability of the overall system are proved. [ABSTRACT FROM AUTHOR]

Abstract This paper mainly investigates the numerical implementation issue of fractional order systems. First, a pattern of fixed pole schemes are developed to approximate fractional integrator/differentiator, whose common is that the poles keep constant for different α. Then, two solutions are proposed to improve the approximation performance around α = 0. Afterwards, the simulation schemes are introduced for two kinds of fractional order systems. In those schemes, the configuration problem of nonzero initial value is considered. Finally, a fair and solid comparison to the classical approximation methods is presented, demonstrating the effectiveness and efficiency of the elaborated algorithms. Highlights • A universal fixed pole approximation algorithm is derived for fractional integrator/differentiator. • The improvement is made to achieve a better performance with smaller order. • Two approximation schemes are presented for implementing fractional transfer function model. • Three approximation schemes are proposed for implementing fractional state space model. • The configuration of nonzero initial system state is discussed for fractional order system. [ABSTRACT FROM AUTHOR]

Abstract Recently, LCL filters have been widely used in the output of single phase inverters. Since, the grid side inductor in these filters is in series with the grid impedance at the Point of Common Coupling (PCC), it may create new resonances. This phenomena may take the control loop toward instability. In this case, in order to have a reliable operation, the current controller should be insensitive to the grid impedance variation. In order to damp these resonances, researchers have presented some methods using active or passive damping. These methods added an extra loop to the control loop, an extra passive component in the filter or extra sensor in the control process. But in most of them, the complexity and the cost of controller have been increased. Therefore, presenting a simple control method without extra sensor, passive component or extra arrangement can be a promising approach. This paper presents an MPC-based current controller, which is simple and robust against the grid impedance variation and even the variation of the LCL filter parameters. In contrast to classical multi-loop controller like Proportional-Resonant (PR) controllers, the proposed control method does not need any parameter tuning. In the proposed controller, the switching plan and duty cycles are determined by a cost function and a switching table. Therefore, at the same time with any variation in grid impedance, the proposed controller changes the next switching state and duty cycle. Operating performance like look-up table, searching in all possible switching states to find the best state for the next switching period, makes the controller adaptive and robust against the variation of LCL filter parameters. In order to confirm the effectiveness of the proposed controller, simulations and experimental results of the proposed controller are compared with a classical PR controller. Highlights • An MPC-based current controlled for single phase grid connected inverters has been proposed. • The grid impedance variation and its effect on resonance of LCL filters has been discussed. • The results of the proposed controller have been compared with a classical PR controller. • The results showed that the proposed MPC method, in contrast with the PR controller, is robust when grid impedance changes. [ABSTRACT FROM AUTHOR]

Abstract The optimal modified performance of the multi-input multi-output (MIMO) networked control systems (NCSs) with encoding–decoding, channel noise in the forward channel and packet dropouts, quantization in the feedback channel is investigated in this paper. A new and efficient tracking performance index for the NCSs is presented which prevents variations in the tracking error where there is no integrator in the plant. The optimal modified performance is obtained by the method of coprime factorization and partial fraction. The results demonstrate that the optimal modified performance is related to the locations of the non-minimum phase (NMP) zeros, unstable poles of the given plant as well as their directions. In addition, the modified factor, packet dropouts probability, channel noise and encoding–decoding are also closely related to optimal modified performance of the NCSs. Finally, we present some particular examples to illustrate the theoretical results. Highlights • A new and efficient tracking performance index for the NCSs is presented. • The communication channal is characterized by coding-decoding, channel noise, data dropout and quantization. • The optimal modified performance of MIMO NCS is attained by applying co-prime factorization technique. • The optimal modified tracking performance is constrained by plant internal structure and the multi-communication parameter. [ABSTRACT FROM AUTHOR]

Abstract For process monitoring, it's of significance to pay more attention to some performance indexes related to quality, economy or security. To this end, a novel performance monitoring method based on the prediction of the performance indexes is proposed in this paper to promote the efficiency of process monitoring. In this study, firstly, the process variables are classified into two categories according to the correlation with the performance indexes. Based on the two categories of variables, a balanced Partial Least Square algorithm is proposed by constructing an enhanced objective function to predict the performance indexes which cannot be measured online. Then, the prediction residual is modeled for monitoring via the Statistics Pattern Analysis to capture the variation in the performance indexes. Finally, two Simulink examples and a practical example are utilized for illustration and validation. Highlights • BPLS-RSPA is proposed to distinguish the performance relevant or irrelevant faults and simplify the monitoring logic. • An enhanced objective function is proposed for information extraction based on the two categories of variables. [ABSTRACT FROM AUTHOR]

Abstract This paper considers the problem of data driven control (DDC) for a class of non-affine nonlinear systems with output saturation. A time varying linear data model for such nonlinear system is first established by using the dynamic linearization technique, then a DDC algorithm is constructed only depending on the control input data and the saturated output data. The convergence of the proposed algorithm is strictly proved and the effect of output saturation on system performance is also analyzed. It is shown that output saturation does not change the convergence property of DDC systems, thus it causes the convergence rate to slow down. Meanwhile, the ultimate tracking error is determined by the change of desired trajectory. If the desired trajectory is a constant, then the tracking error converges to zero. Two examples are exploited to verify the theoretical results. Highlights • The problem of output saturation is considered for data driven control systems. • A novel DDC algorithm is constructed using the saturated output data. • The convergence of the proposed design is proved and the effect of output saturation is also analyzed. • Output saturation causes the convergence rate of DDC systems to slow down. [ABSTRACT FROM AUTHOR]

Abstract Regarding to the growing trend of photovoltaic (PV) energy as a clean energy source in electrical networks and its uncertain nature, PV energy prediction has been proposed by researchers in recent decades. This problem is directly effects on operation in power network while, due to high volatility of this signal, an accurate prediction model is demanded. A new prediction model based on Hilbert Huang transform (HHT) and integration of improved empirical mode decomposition (IEMD) with feature selection and forecast engine is presented in this paper. The proposed approach is divided into three main sections. In the first section, the signal is decomposed by the proposed IEMD as an accurate decomposition tool. To increase the accuracy of the proposed method, a new interpolation method has been used instead of cubic spline curve (CSC) fitting in EMD. Then the obtained output is entered into the new feature selection procedure to choose the best candidate inputs. Finally, the signal is predicted by a hybrid forecast engine composed of support vector regression (SVR) based on an intelligent algorithm. The effectiveness of the proposed approach has been verified over a number of real-world engineering test cases in comparison with other well-known models. The obtained results prove the validity of the proposed method. Highlights • An improved EMD based HHT to decompose the solar generation output signal. • A new feature selection algorithm to choose the best candidate inputs. • A hybrid forecast engine based support vector regression. • An effective optimization algorithm based on chaotic PSO and Fuzzy mechanism. [ABSTRACT FROM AUTHOR]

Abstract: Based on the universal approximation property of the fuzzy-neural networks, an adaptive fuzzy-neural observer design algorithm is studied for a class of nonlinear SISO systems with both a completely unknown function and an unknown dead-zone input. The fuzzy-neural networks are used to approximate the unknown nonlinear function. Because it is assumed that the system states are unmeasured, an observer needs to be designed to estimate those unmeasured states. In the previous works with the observer design based on the universal approximator, when the dead-zone input appears it is ignored and the stability of the closed-loop system will be affected. In this paper, the proposed algorithm overcomes the affections of dead-zone input for the stability of the systems. Moreover, the dead-zone parameters are assumed to be unknown and will be adjusted adaptively as well as the sign function being introduced to compensate the dead-zone. With the aid of the Lyapunov analysis method, the stability of the closed-loop system is proven. A simulation example is provided to illustrate the feasibility of the control algorithm presented in this paper. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a support vector machine (SVM) approach to generalized predictive control (GPC) of multiple-input multiple-output (MIMO) nonlinear systems. The possession of higher generalization potential and at the same time avoidance of getting stuck into the local minima have motivated us to employ SVM algorithms for modeling MIMO systems. Based on the SVM model, detailed and compact formulations for calculating predictions and gradient information, which are used in the computation of the optimal control action, are given in the paper. The proposed MIMO SVM-based GPC method has been verified on an experimental three-tank liquid level control system. Experimental results have shown that the proposed method can handle the control task successfully for different reference trajectories. Moreover, a detailed discussion on data gathering, model selection and effects of the control parameters have been given in this paper. [Copyright &y& Elsevier]

Abstract: The paper deals with the robust stability analysis of a Fractional Order Interval Polynomial (FOIP) family. Some new results are presented for testing the Bounded Input Bounded Output (BIBO) stability of dynamical control systems whose characteristic polynomials are fractional order polynomials with interval uncertainty structure. It is shown that the Kharitonov theorem is not applicable for this type of polynomial. A procedure is given for computation of the value set of FOIP. Based on the value set, an algorithm is presented for testing the stability of FOIP. The results presented in the paper are useful for the analysis and design of Fractional Order Interval Control Systems (FOICS). Examples are given to show how the proposed method can be used to assess the effects of parametric variations on the stability in feedback loops with fractional order interval transfer functions. [Copyright &y& Elsevier]

In this paper, we propose a new adaptive internal model control scheme based on adaptive finite impulse response filters. This scheme provides the same design procedure for both minimum and nonminimum phase plants. The plants under consideration may contain time delay. The tracking objective is accomplished for both invertible and noninvertible stable plants. The internal model of the plant and its inverse are estimated by recursive least-squares and least-mean- squares algorithms, respectively. The closed loop is designed such that the system from the reference input to the plant output can be approximately represented by a pure delay. The effect of the process zeros on the output is compensated by using adaptive finite impulse response filters. This avoids the cancellation of noncancellable zeros of the plant and forces the plant output to track the reference input with a delay. The stability of the closed loop for both minimum and nonminimum phase systems is guaranteed. Computer simulation and laboratory scale experimental results are included in the paper to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper investigates the design of low order robust controllers based on an H∞ performance index using a real-code genetic algorithm. In H∞ controller design, the major disadvantage of the existing methods is that they lead to high-order controllers. This is the gap between theory and practice. Therefore the purpose of this paper is to design a low order controller with similar performance to the H∞ optimal controllers, which can find sufficiently wide use in engineering practice. We first design the H∞ optimal controller using Glover and Doyle's results, and obtain the corresponding performance index γ. Second, the desired low order controller with several parameters is chosen, e.g., a first-order controller, or a PID controller. Finally, we use the real-code genetic algorithm to find the optimal controller parameters that preserve the performance index γ. Computational simulations illustrate the effectiveness of the pro posed approach. [ABSTRACT FROM AUTHOR]

Abstract This paper addresses the time-delay estimation problem of SISO systems using uniformly sampled input-output data. A dependence measure known as Schweizer and Wolff's σ (SWσ) from the copula theory is employed to measure the dependence of the input-output data. The quantitative relation between the time-delay and SWσ is studied, and the time-delay is estimated given that the SWσ of the input-output data reaches its maximum value when the time-delay is removed. The major advantage of our work is that no other parameters of the system must be estimated before or during the time-delay estimation process. Experiments and comparisons are used to verify the feasibility and effectiveness of the proposed method. Highlights • Copula theory is firstly applied to the time-delay estimation problem. • An algorithm to estimate the time-delays for a system based on SWσ is proposed. • The dependence between the input and output of a SISO system is measured. • The conditions under which SWσ reaches its maximum are obtained. [ABSTRACT FROM AUTHOR]

Fractional order systems become increasingly popular due to their versatility in modelling and control applications across various disciplines. However, the bottleneck in deploying these tools in practice is related to their implementation on real-life systems. Numerical approximations are employed but their complexity no longer match the attractive simplicity of the original fractional order systems. This paper proposes a low-order, computationally stable and efficient method for direct approximation of general order (fractional order) systems in the form of discrete-time rational transfer functions, e.g. processes, controllers. A fair comparison to other direct discretization methods is presented, demonstrating its added value with respect to the state of art. [ABSTRACT FROM AUTHOR]

This paper investigates the flocking problem of quad-rotor UAVs. Considering the actual situations, we derived a new simplified quad-rotor UAV model which is more reasonable. Based on the model, the T-S fuzzy model of attitude dynamic equation and the corresponding T-S fuzzy feedback controller are discussed. By introducing a double-loop control construction, we adjust its attitude to realize the position control. Then a flocking algorithm is proposed to achieve the flocking of the quad-rotor UAVs. Compared with the flocking algorithm of the mass point model, we dealt with the collision problem of the quad-rotor UAVs. In order to improve the airspace utilization, a more compact configuration called quasi e-lattice is constructed to guarantee the compact flight of the quad-rotor UAVs. Finally, numerical simulations are provided to illustrate the effectiveness of the obtained theoretical results. [ABSTRACT FROM AUTHOR]

The 2-degree of freedom (DOF) helicopter system is a typical higher-order, multi-variable, nonlinear and strong coupled control system. The helicopter dynamics also includes parametric uncertainties and is subject to unknown external disturbances. Such complicated system requires designing a sophisticated control algorithm that can handle these difficulties. This paper presents a new robust control algorithm which is a combination of two continuous control techniques, composite nonlinear feedback (CNF) and super-twisting control (STC) methods. In the existing integral sliding mode (ISM) based CNF control law, the discontinuous term exhibits chattering which is not desirable for many practical applications. As the continuity of well known STC reduces chattering in the system, the proposed strategy is beneficial over the current ISM based CNF control law which has a discontinuous term. Two controllers with integral sliding surface are designed to control the position of the pitch and the yaw angles of the 2- DOF helicopter. The adequacy of this specific combination has been exhibited through general analysis, simulation and experimental results of 2-DOF helicopter setup. The acquired results demonstrate the good execution of the proposed controller regarding stabilization, following reference input without overshoot against actuator saturation and robustness concerning to the limited matched disturbances. [ABSTRACT FROM AUTHOR]

The thermal power plant, especially the ultra-supercritical unit is featured with severe nonlinearity, strong multivariable coupling. In order to deal with these difficulties, it is of great importance to build an accurate and simple model of the coordinated control system (CCS) in the ultra-supercritical unit. In this paper, an improved T-S fuzzy model identification approach is proposed. First of all, the k-means++ algorithm is employed to identify the premise parameters so as to guarantee the number of fuzzy rules. Then, the local linearized models are determined by using the incremental historical data around the cluster centers, which are obtained via the stochastic gradient descent algorithm with momentum and variable learning rate. Finally, with the proposed method, the CCS model of a 1000 MW USC unit in Tai Zhou power plant is developed. The effectiveness of the proposed approach is validated by the given extensive simulation results, and it can be further employed to design the overall advanced controllers for the CCS in an USC unit. [ABSTRACT FROM AUTHOR]