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The interleaved double dual boost converter (IDDBC) is considered a promising and reliable approach to interface the low-voltage renewable energy sources (RESs), such as battery packs, fuel cells and photovoltaic (PV) panels, to the DC bus of the DC microgrids. It could give a higher voltage step-up ratio and reduce voltage and current stresses on the power devices. However, the connection of various tightly regulated power converters, which are known as constant power loads (CPLs), would cause negative incremental impedance. Meanwhile, the unmodeled dynamics and load perturbation disturbances are unavoidable in the DC microgrid. These issues would deteriorate the system stability and even lead to system failure. To address these problems, this paper proposes an adaptive passivity-based control (PBC) to handle the DC bus’s large-scale stability issues of the N-phase IDDBC feeding CPL system. A reduced and practical N-phase IDDBC model supplying CPL considering unmodeled dynamics and load perturbation disturbances is proposed to facilitate the controller design. Besides, a nonlinear disturbance observer (NDO) is incorporated with the proposed PBC to improve the robustness against parameter uncertainties and external disturbances. And the global stability of the closed-loop system is proved through the Lyapunov theorem. Finally, simulation and experimental results are demonstrated to verify the feasibility and effectiveness of the proposed controller.

Dynamic wireless power transfer (DWPT), as an emerging technology of coupling the electrified transportation network and power grid, is regarded as a powerful solution for electric vehicles (EVs) promotion. In this article, we propose an intelligent path planning strategy for each individual EV by combining the urban electrified transportation network, power grid, and DWPT. Based on the Floyd algorithm, we ensure the timeliness of path planning by updating the link weights in real time. The EV intelligent path planning strategy comprehensively considers the travel time, charging energy, charging cost, and user convenience. In addition, to ensure that the grid bus voltage has the lowest deviation rate while considering the bus load limit, we propose a multi-stage two-layer optimization algorithm to determine the charging power of each link. The upper layer uses the particle swarm optimization, and the decision variable is the charging power of each link; the lower layer calculates the voltage deviation rate by solving the alternating current optimal power flow model. Finally, the simulation results demonstrate that the intelligent path planning strategy proposed in this article can effectively guarantee a good comprehensive performance in terms of travel time, charging energy, charging cost, and user convenience.

The longitudinal control of the platoon of connected and automated vehicles (CAVs) has gained extensive attention in recent transportation research. A majority of existing results are based on linearized third-order vehicular models, under the premise that a complete priori knowledge of vehicle dynamics is available. This article focuses on a general class of third-order nonlinear CAVs with parametric uncertainty and unknown external disturbance which cannot be linearized. A vehicle-to-vehicle (V2V) communication-based cooperative adaptive backstepping control scheme is proposed, in which unknown parameters and disturbance bounds are estimated on-line. Since the transfer function of linear systems cannot be applied to nonlinear systems to guarantee string stability, asymmetric time-varying constraints are employed to prevent the spacing errors from growing up. A realistic example is considered to verify the feasibility of the control algorithm.

The multilevel connected dc–dc converters system is considered a practical technology to increase the capacity and reduce voltage and current stresses on the power devices. In practice, mismatched parameters are inevitable, which increase the challenge of output voltage regulation and current sharing control. In order to conquer these difficulties, this article investigates the current sharing based on incremental passivity control for multilevel connected dc–dc converters. Combined with passivity-based control, a finite-time estimator is given to improve the robustness against the load variations. The proposed method results in a linear control law despite the bilinear nature of this system. Moreover, analysis exhibits that stability and current sharing are independent of inductor parameters. Furthermore, stability of the closed-loop system has been proved that the proposed approach is valid both for a linear and nonlinear load. After introducing the proposed control scheme, simulation and experiments are offered to validate its fast transit response and antidisturbance performance.

This note investigates the asynchronous control issue for continuous-time Markov jump nonlinear systems using output-based sliding mode approach. A hidden Markov model of interest is used as the mapping mechanism of the asynchronous phenomenon, which further contributes to the asynchronous sliding mode surfacce. In this scenario, sufficient conditions are derived ensuring the mean-square-stability of the sliding motion dynamic and its H ∞ noise attenuation performance. Then the reachability of the sliding surface is guaranteed by a well defined reaching control law. Finally a numerical example is presented to discuss the significance of the proposed asynchronous issue and verify the effectiveness of the designed control approach.

The dynamic wireless charging for electric vehicles (EVs) is considered an efficient and practical choice to extend the driving range and reduce battery pack size. However, the coupling coefficient between transmitter coils and receiver coil varies rapidly on a large scale during the EVs moving, so it deteriorates the charging performance of dynamic wireless power transfer in EVs, such as the discontinuous charging for the lithium-ion batteries in EVs. Moreover, the charging power and efficiency of the system can also be affected. To solve these issues, a dc–dc converter is added to cascade on the receiver side to improve the output power and efficiency of the system. Furthermore, the passivity-based proportional-integral control is designed for the dc–dc on the receiver side of dynamic wireless charging system to improve the performance against the rapidly changing coupling coefficient. Finally, compared with the conventional proportional-integral-derivative controller, simulation and experimental results are given to show the performance and robustness of dynamic wireless charging system by the proposed method.

The conventional LQG based economic performance design has found its difficulty in industrial application and so far, there is still no systematic and effective way to improve economic performance. As learned from the LQG benchmark performance assessment method, the economic performance improvement in MPC systems can be realized through adjusting controller parameters in addition to the well-known setpoints change approach. Therefore, we take advantage of LQG and iterative learning control (ILC) to propose a new two-layer periodical economic performance improvement strategy applicable in industrial MPC systems. By dividing the whole time into multiple intervals called periods and optimize the performance periodically, the economics finally reach its optimal. Promoted twice in a certain period, the performance acquires its first promotion through fixed variance obtained from the lower MPC layer, which transforms the nonlinear economic performance function (EPF) of LQG into a linear one. The ILC-based weight coefficients adjustment algorithm then provides the parameters to the MPC controller in the next period with the updating principle based on the idea of minimizing the tracking error between the current controller economic performance and an optimal one, which realizes the second performance improvement. Room for the second promotion is analyzed and convergence of the algorithm is proved. Finally, the effectiveness and applicability of the strategy are verified via a typical industrial separation process.

In this paper, the event-triggered consensus control problem for nonlinear uncertain multi-agent systems subject to unknown parameters and external disturbances is considered. The dynamics of subsystems are second-order with similar structures, and the nodes are connected by undirected graphs. The event-triggered mechanisms are not only utilized in the transmission of information from the controllers to the actuators, and from the sensors to the controllers within each agent, but also in the communication between agents. Based on the adaptive backstepping method, extra estimators are introduced to handle the unknown parameters, and the measurement errors that occur during the event-triggered communication are well handled by designing compensating terms for the control signals. The presented distributed event-triggered adaptive control laws can guarantee the boundness of the consensus tracking errors and the Zeno behavior is avoided. Meanwhile, the update frequency of the controllers and the load of communication burden are vastly reduced. The obtained control protocol is further applied to a multi-input multi-output second-order nonlinear multi-agent system, and the simulation results show the effectiveness and advantages of our proposed method.

This paper proposes a robust Immersion and Invariance (I&I) adaptive coordinated controller for a class of uncertain linear-motor-driven biaxial gantry system subject to external disturbances for h...

This paper presents a green manufacturing-recycling network design approach for multi-echelon electronic assembly process under a complex product mix scenario which incorporates trade-off between e...

This paper considers the tracking control problem for nonlinear Markov jump systems based on T–S fuzzy model approach with incomplete mode information. It is assumed that the mode transition rate matrix is not a priori knowledge and only partial information is available. Moreover, the mode where the system stays when operating is not fully accessible to the designed controller. In this incomplete mode information scenario, a hidden Markov model based mechanism is modified to simulate the mode deficiency mapping. The incomplete transition rate matrix is well defined in the form of a polynomial. Based on this, by constructing a polynomially parameter-dependent Lyapunov matrices and linear matrix techniques, sufficient conditions are established to ensure the stochastic stability and a prescribed tracking performance. The controller design scheme are presented by solving a series of LMIs. Examples are given in the end to illustrate the effectiveness of our proposed results.

The wireless power transmission (WPT) system for electric vehicle charging has the advantages of being safe and reliable, without plugging and unplugging, but the random misalignment between the energy transmitting coil and the energy receiving coil will cause uncertain disturbance to the transmission power and system performance. Therefore, it is necessary to improve the robustness of the wireless power transmission system. This paper designs a TUBE model predictive control (TMPC) to make the system converge to the desired value along the reference trajectory. To reduce the difficulty of solving the reference trajectory, the idea of segmented optimization is adopted. State feedback is presented to further enhance the system’s anti-disturbance ability to increase the system transmission power. Moreover, the compensation for the sampling delay of the controller is proposed to ensure the stability of the control system. Experimental results demonstrate that, compared with traditional predictive control, the proposed TUBE MPC based on state feedback control strategy can increase the transmission power of the system by about 10% under uncertain misalignment.

Planning a feasible, and safe trajectory is a crucial procedure for autonomous parking which remains to be fully solved. Generally, a trajectory is depicted by a sequence of configurations which include positions, and orientations. However, existing approaches usually design parking trajectories without considering the evolution from one configuration to the next. Hence, these trajectories are practically infeasible in some scenarios, and safety risks may exist. In this paper, a guaranteed collision‐free trajectory planning method is proposed for autonomous parking. A dynamic optimisation problem is built, which includes vehicle kinematics, collision avoidance constraints, and physical restraints. Then the dynamic optimisation problem is discretised into a finite‐dimensional nonlinear programming problem. To describe collision avoidance constraints, the concepts of the virtual protection frame, and the magnification parameter are introduced. It is highlighted that a novel collision detection criterion is raised from the view of geometry. Tests in three common scenarios, and a complex scenario illustrate the effectiveness of the proposed approach. Moreover, the stability of the algorithm is discussed in this paper. Via the proposed approach, an integrated design of planning, and control is possible.

Deep neural networks (DNNs) have strong fitting ability on a variety of computer vision tasks, but they also require intensive computing power and large storage space, which are not always available in portable smart devices. Although a lot of studies have contributed to the compression of image classification networks, there are few model compression algorithms for object detection models. In this paper, we propose a general compression pipeline for one-stage object detection networks to meet the real-time requirements. Firstly, we propose a softer pruning strategy on the backbone to reduce the number of filters. Compared with original direct pruning, our method can maintain the integrity of network structure and reduce the drop of accuracy. Secondly, we transfer the knowledge of the original model to the small model by knowledge distillation to reduce the accuracy drop caused by pruning. Finally, as edge devices are more suitable for integer operations, we further transform the 32-bit floating point model into the 8-bit integer model through quantization. With this pipeline, the model size and inference time are compressed to 10% or less of the original, while the mAP is only reduced by 2.5% or less. We verified that performance of the compression pipeline on the Pascal VOC dataset.

The analysis of protein coding regions of DNA sequences is one of the most fundamental applications in bioinformatics. A number of model-independent approaches have been developed for differentiating between the protein-coding and non-protein-coding regions of DNA. However, these methods are often based on univariate analysis algorithms, which leads to the loss of joint information among four nucleotides of DNA. In this article, we introduce a method on basis of the noise-assisted multivariate empirical mode decomposition (NA-MEMD) and the modified Gabor-wavelet transform (MGWT). The NA-MEMD algorithm, as a multivariate analysis tool, is utilized to reconstruct the numerical analyzed sequence since it enables a matched-scale decomposition across all variables and eliminates the mode mixing. By virtues of NA-MEMD, the MGWT method achieves a stable improvement on the general identification performance. We compare our method with other Digital Signal Processing (DSP) methods on two representative DNA sequences and three benchmark datasets. The results reveal that our method can enhance the spectra of the analyzed sequences, and improve the robustness of MGWT to different DNA sequences, thus obtaining higher identification accuracies of protein coding regions over other applied methods. In addition, another comparative experiment with the model-dependent method (AUGUSTUS) on the recently proposed benchmark dataset G3PO verifies the superiority of model-independent methods (especially NA-MEMD-MGWT) for identifying coding regions of the poor-quality DNA sequences.

A robust and mixture form of Bayesian latent variable regression with structural sparsity (BLVR-SS) is proposed to deal with an inferential sensing problem for the processes with multiple modes and...

While the existing literature on delay-adaptive control concentrates on uncertain plants with discrete input delays, this article proposes a predictor feedback for stabilization of uncertain linear systems with distributed input delays. A finite-dimensional linear system with distributed actuator delay may come with five types of uncertainties: unknown delay, unknown delay kernel, unknown plant parameters, unmeasurable finite-dimensional plant state, and unmeasurable infinite-dimensional actuator state. For different combinations of uncertainties, distinct control designs are developed in the article, from which the users can make selections to address a vast range of problems they face.

A consensus criterion for multiple complex networks is proposed in the paper. Based on event-triggered samplers, a projection is employed to select communication instant for various complex systems with the same natural attributes, with that the transmission of information between networks is synchronous. To reduce redundant data in sampling, by utilizing the generalized free-weighting matrix approach, the expression form of consensus criterion for multiple networks is simplified. Available information could be fully utilized, and the advantages of self-triggered scheme are retained. A numerical example of multiple unmanned aerial vehicles is offered to show effectiveness.

The detection of plant-wide oscillation in an industrial process is of great significance. Recently, indirect multivariate intrinsic time-scale decomposition (MITD) (IMITD) has been pioneered for the adaptive processing of multi-loop data, which is restricted by the problem of projection sensitivity. To solve the challenge, the direct MITD (DMITD) algorithm is proposed and featured by the following contributions: 1) Three novel concepts including the multivariate extremum, multivariate baseline-node, and baseline-operator are defined for the purpose of developing DMITD; 2) Compared with IMITD, the implementation of DMITD is more robust to the selection of projection directions; and 3) DMITD outperforms traditional techniques in capturing both the regularity and evolution of the plant-wide oscillation from noisy signals in the nonlinear and nonstationary process. The proposed method is demonstrated by simulation as well as one industrial case.

It has been an active research area to develop robust supervisory control policies for production systems with unreliable resources. So far, most methods for robust deadlock resolution apply only to systems without flexible routes, where each processing step of any part type requires a unique prespecified resource. In this paper, we address deadlock avoidance control problem in production systems with both failure-prone resources and flexible routings, which allow that a part has options when deciding the resource acquisition at each step. This paper presents properties that a controller with robustness must satisfy. Specifically, at any system reachable state, neither the failed resources nor part instances trapped in these resources should have too much detrimental effect on the other portions of the system. Thus, the full range of part types’ production could be assured at all time. After defining the notions of reduced system and reduced state with respect to unreliable resources, we identify and prove conditions for determining whether or not the state resulting from the occurrence of an event is feasible. Subsequently, we develop a method for robust deadlock avoidance, which uses the solutions to state safety checking problem for the reduced production system with only reliable resources. An illustrative example shows the effectiveness of this method. Finally, we conduct a comparison investigation of some representative approaches in the literature about robust supervisory control for deadlock resolution in resource allocation systems with routing flexibility.

In this article, the problem of energy-to-peak filtering problem is studied for a class of continuous-time semi-Markov jump systems with mismatched modes. We consider the scenario where the mode of the system is not always instantaneously accessible and mismatches the mode of the filter. To cope with this asynchronous phenomenon, we introduce a hidden semi-Markov model and use a stochastic process as a switching signal of the filter to observe the system modes. By the Lyapunov function approach and linear matrix inequality method, sufficient conditions are developed to ensure the filtering error dynamics is stable with a prescribed energy-to-peak performance index. Two examples are given to illustrate the effectiveness of the proposed design technique.

This article investigates the robust cooperative output regulation problem for heterogeneous uncertain linear multiagent systems, with time-varying communication topologies. Suppose that the considered systems are detectable by all measurements, rather than the conventional regulated output alone. Under this weaker detectability condition, a new distributed regulator via partial state feedback is first established with the addition of an integrator to the input channel. This is then extended to obtain the practical cooperative output regulation via measurement feedback with the employment of an extended high-gain observer.

Electric vehicles are beneficial to the environment owing to its nonproduction of emissions and excessive noise; however, they have their own limitations with respect to charging difficulties and mileage anxiety. In order to address these problems, dynamic wireless power transfer (DWPT) technology has been developed. In this article, we replace a conventional open-loop method without dc–dc converter with the proposed dc–dc converter in the energy receiver in order to improve output power. Moreover, to make sure that the DWPT system is reliable when the coupling coefficient changes rapidly over a wide range of values, we propose model predictive control (MPC) to ensure optimal dynamic-tracking performance. Considering the fact that sampling delay will cause an error in the controller output, the system is modeled accurately in order to improve the MPC performance. In addition, the MPC and double closed-loop proportional–integral–differential control are compared with simulations and experiments, the results of which demonstrate the effectiveness of MPC-based DWPT.

In this work, output-feedback control problems for a class of discrete-time non-affine nonlinear systems with unknown control directions and input constraints are considered by using reinforcement learning (RL) method. Two neural networks (NNs) implement the control: 1) a critic NN that estimates a non-quadratic strategic utility function (SUF) and 2) an action NN that generates optimized control input and minimizes the SUF. The implicit function theorem is applied to obtain the optimal control law since the control is appeared in a non-affine form. For the first time, the discrete Nussbaum gain is introduced to overcome the difficulty that the control directions are unknown and a non-quadratic SUF is used to deal with the control constraints in the RL-based control. The theoretical derivation of the uniformly ultimately boundedness of the NN weights and the closed-loop output tracking error is given. And two numerical examples have been supplied to valid the proposed method.

The problem of reliable controller design for two-dimensional (2-D) systems subject to extended dissipativity is investigated in this paper. Considering the wide usage of Roesser state-space model, a discrete-time Roesser model is introduced to describe 2-D systems. To reinforce the reliability of the system under consideration, actuator-failure model which may severely degrade performance, or even destabilize the system is introduced. Finally, the sufficient condition for the existence of reliable controller is presented to guarantee the mean square asymptotic stability and 2-D extended dissipativity of the closed-loop system under admissible actuator failures. The gains of 2-D controller are derived by means of the convex optimization method. A simulation result is exploited to verify the effectiveness and merits of the theoretical findings.

This paper investigates the ${H_{\infty }}$ output consensus problem for multiagent systems with Markov jump and external disturbance in both continuous-time and discrete-time domains. The communication network is directed and fixed with uncertainties. Based on the hidden Markov model, an output feedback controller is constructed. Then, the original system is transformed into a reduced-order system, which features the error dynamics. By using a Lyapunov function, sufficient conditions are developed to ensure that all agents can reach the consensus with the desired ${H_\infty }$ performance in the mean-square sense. Finally, simulation results are presented to illustrate the efficiency of the proposed approaches.

Industrial signal measurement and processing are increasingly being deployed in control performance assessment, particularly in oscillation monitoring applications. In this paper, we present a nove...

In this paper, the problems of nonfragile and nonsynchronous synthesis for Bernoulli switched systems are studied. The disturbances in the systems are assumed to have bounded peak in the sense of mean square. With the interval additive gain variations consideration, a nonfragile and nonsynchronous controller is designed subject to a nonsynchronous assumption that the switching signals of the system are not accurately gained for synthesis. A sufficient condition that is dependent on the known sojourn probabilities of switching signals is proposed to ascertain the reachable set of the closed-loop system, which is a hidden Bernoulli model, mean square contained in an ellipsoid-like set. Several nonfragile and nonsynchronous controllers are parameterized to guarantee the corresponding requirements to be satisfied for the closed-loop systems. The effectiveness and advantages of the proposed new design techniques are illustrated by a numerical simulation example.

Fault diagnosis technology is crucial to ensure the long-term reliability of the industrial process control system. With the increase of industrial data availability, conventional monitoring approaches may not function well under the assumption that the training and the application data come from the same distribution. Following the intuition that industrial data exhibit time dependency and inherent complex characteristics, this paper proposes an adaptive sequential fault diagnosis method based on a tensor factorization layer merged with deep neural network model (TF-DNN). Tensor representation is firstly applied to preserve the number of the raw data entries and their sequential dependence between observations. Multilinear mapping with tensor-to-tensor projection is then to transform the input and hidden tensor to the low-dimensional tensors, which makes the learned representations sparser with the grid-like structures. With the tensor factorization layers, the proposed deep network shares efficient knowledge across the spatiotemporal features of fault data. The outstanding performance of this method is demonstrated and compared to the existing models in the benchmark Tennessee Eastman process and a real industrial methanol plant.

Based on the P-type composition of Petri nets (PNs) defined in this paper, a framework for a structural control of discrete event systems (DESs) is constructed such that a closed-loop PN is obtained by composing a plant PN and a controller. As for a disjunction or conjunctive normal form (CNF) of linear constraints, a new approach is proposed to design a structural controller in this framework. First, a switching-net is defined for a disjunction of constraints, and an extended plant is obtained through the P-type composition of a plant PN and switching-net. Second, the disjunction of bounded constraints is transformed into a conjunction of switching-constraints on the extended plant. Third, a controller is synthesized by designing monitors for conjunctive switching-constraints according to a supervision-based-on-place-invariant method. Fourth, in a similar manner, a controller is also designed for a CNF of bounded constraints. The resulting controller is maximally permissive if each disjunction of constraints meets the jump-free condition, and its size grows polynomially with the number of constraints. Another advantage is that the closed-loop system is still a PN for many real DES since a CNF can describe not only convex but also nonconvex state regions.

Automatic guided vehicles (AGVs) recently have gained increasing attentions and applications, however, frequently stopping to recharge largely reduces service efficiency. Wireless power transfer (WPT) is considered as a practice energization way to solve this problem. In this paper, a passivity-based controller (PBC) and parameter designing method for compensation topology are proposed for AGVs WPT system. The PBC based on port-controlled Hamiltonian system (PCHS) is designed to achieve desired constant systematic working power by regulating the output voltage of DC/DC converter. The LCC-LCC resonant network is analyzed in the principle of the impedance matching method, and a proportional integral (PI) controller is implemented to realize zero steady-state error. Simulation are carried out in PLECS to verify analysis, and results show that proposed controller scheme and compensation designing method ensure the stability of the charging system against load variations, and the fast response performance of the control algorithm is also validated.

Following the intuition that not all latent variables in probabilistic principal component analysis method shifts simultaneously, this paper proposes a spike-and-slab regularization technique for nonlinear fault detection and isolation. Different from the existing probabilistic latent variable models, a spike-and-slab prior is introduced to downweight the irrelevant information of latent variables for the discriminative model. The resulting latent subspace supported by regularization parameters is not only sensitive to the informative variables, but it also eliminates the influence of the non-informative ones. The feasibility and efficiency of the proposed approach will be tested on an industrial methanol distillation dataset. Moreover, the performance will be compared with conventional probabilistic latent variables methods.

An event-based consensus filtering control scheme for multi-agents with multiple mixing delays is proposed in the paper. Firstly, a piece-wise sampling model with transmission delay defined from sensors to controllers is built, and the effect of time-varying delay on sampling is analyzed. Secondly, a self-triggered scheme is proposed to take into consideration of reducing redundant data and complexity. Thirdly, to fully utilize the available information, by employing an improved generalized free-weighting matrix inequality, a novel Lyapunov-Krasovskii functional approach is proposed to achieve global asymptotically synchronization. At last, an example of multiple unmanned aerial vehicles is offered to show the effectiveness of proposed method.

Industrial oscillation recordings are often contaminated with random noise, process disturbances and underlying nonstationarity, which obscure the useful information of the signal and complicate subsequent oscillation detection and diagnosis. This paper proposes a novel denoising technique to improve the quality of oscillation data, by jointly employing ensemble empirical mode decomposition (EEMD) with canonical correlation analysis (CCA). The proposed method first utilizes EEMD to decompose the single-loop data into a set of intrinsic mode functions (IMFs). Then CCA is applied to isolate the oscillation-dependent components from the decomposed IMFs. We evaluated the performance of the method through both numerical and industrial examples. The results demonstrate that this work is a promising tool for oscillation data preprocessing in the single control-loops.

This paper illustrates a control strategy for voltage regulation problem of DC-DC converter in dynamic wireless charging applications. The control goal is to compensate for the disturbances on the input voltage of the DC-DC converter that changes periodically due to the motion of the electric vehicle assumed to have a constant speed. The paper aims at comparing two different control solutions based on continuous and hybrid internal model-based regulators. According to the relative distance of the transmitter coils the fluctuation of the input voltage is approximated as a continuous-time or a spline-based periodic signal and internal model-based solutions, which are continuous time or hybrid according to the fluctuation modelling, are proposed to compensate for such a disturbance. The control performance of the proposed control schemes are tested through simulation by showing how, as long as the relative distance between the coils increases, the more involved hybrid solutions improve the performances when compared with the more simple continuous-time ones.

The receiving coil of dynamic wireless charging (DWC) systems of electric vehicles will be offset relative to the transmitting coil, which will cause a decrease in mutual inductance and thus a decrease in output power. The conventional solution is to add a buck converter on the energy receiving side to compensate for the power drop. Indeed, the receiving-side buck converter is a nonlinear structure, and the corresponding nonlinear control strategy study is required. In addition, the rapid and large-range changes in the mutual inductance of the DWC system will also increase the control difficulty. To solve these problems, we propose a feedback linearization control (FLC) algorithm for the buck converter on the receiving side of the DWC system. We carry out detailed modeling of the receiving-side buck converter, linearize the relationship between input and output, and analyze the stability of the controlled system. The results show that the adjustment speed of the proposed FLC is fast, and it is suitable for the DWC system. Moreover, when the mutual inductance changes rapidly in a wide range, the controlled output of the receiving-side buck converter can still track the reference value stably, demonstrating the effectiveness and superiority of the proposed FLC algorithm.

Two adaptive control strategies are presented in this paper for two types of quadrotors to cope with potential uncertainties and faults in the actuator system. The four actuators of the considered quadrotors are distinct and suffered from loss of effectiveness (LOE) as well. To accomandate unknown actuator parameters, a filter-based immersion and invariance (I&I) adaptive controllers are designed for attitude and altitude subsystems, respectively, and an integral backstepping controller is developed for the horizontal subsystem to achieve null steady-state error. Both simulation and experiment results are carried out to illustrate the effectiveness of trajectory tracking performance and fault-tolerant accomondation ability of the proposed control schemes.

In a rally competition, professional drivers usually adopt a very aggressive strategy. Agile maneuvering such as ‘drifting’ often occurs during the cornering process. In this paper, a controller framework is present for the vehicle's agile maneuver based on Model Predictive Control (MPC). We introduce the 3-state vehicle model and the brush tire model and analyze the trajectory characteristics of drift equilibrium. The proposed control system can track the variable drift state and the reference path simultaneously and is applicable for both regular driving and drift cornering. Different from the previous studies on drift stability or drift cornering, the system can not only realize lane keeping in complex scenarios but also achieve autonomous drift maneuvering during the cornering process. The effectiveness of the system is validated via simulations on the Matlab-Carsim platform.

Intermittent renewable energy sources are being increasingly integrated into modern power networks. This leads to severe frequency fluctuations in the networks. Energy storage systems can be used for frequency restoration due to their capability to provide in-time active power compensations. This paper examines the frequency control problem for power systems with multiple distributed battery energy storage systems (BESSs). A dual-consensus-based approach is presented for distributed frequency control. It consists of three main components: 1) tuning of the BESS control gain; 2) design of control signals as inputs to BESSs for proportional use of the preserved energy; and 3) estimation of BESS parameters for control implementation. A static parameter and a time-varying parameter are defined, and their average values are estimated through static average consensus (SAC) and dynamic average consensus (DAC) algorithms, respectively. Case studies are conducted to demonstrate our dual-consensus control approach.

In this paper, a robust decision-making strategy for integration of design and control under uncertainty is developed for multiple steady-state processes. The major challenge in the integration of design and control is the presence of disturbances and process uncertainty, particularly in the nonlinear process with multiple steady states. A fuzzy model is used as a surrogate model to reduce computational loading in a large operating scope in this work. Based on fuzzy model rules, a robust PI controller design using linear matrix inequalities is developed to guarantee the disturbance rejection performance, stability, and robustness. Finally, through an illustrative process with multiplicity behaviors, the features of the proposed method are demonstrated. Comparisons between the proposed method and the conventional sequential approach are made. The resulting design shows that the proposed method under the designed process with uncertainty would gain more profits than the past design approaches.

This paper addresses the problem of quantized feedback control of nonlinear Markov jump systems (MJSs). The nonlinear plant is represented by a class of fuzzy MJSs with time-varying delay based on a Takagi–Sugeno fuzzy model. The quantized signal is utilized for control purpose and the sector bound approach is exploited to deal with quantization errors. By constructing a Lyapunov function which depends both on mode information and fuzzy basis functions, the reciprocally convex approach is used to derive the criterion which is able to ensure the stochastic stability with a predefined ${l_{2}-l_\infty }$ performance of the resulting closed-loop system. The design of the quantized feedback controller is then converted to a convex optimization problem, which can be handled through the linear matrix inequality technique. Finally, a simulation example is presented to verify the effectiveness and practicability of the proposed new design techniques.

Progressive hedging (PH) is a classical decomposition algorithm for solving multistage stochastic problems. However, due to the exponentially growing model size of real-world enterprise-wide optimi...

The problem of asynchronous and resilient filtering for discrete-time Markov jump neural networks subject to extended dissipativity is investigated in this paper. The modes of the designed resilient filter are assumed to run asynchronously with the modes of original Markov jump neural networks, which accord well with practical applications and are described through a hidden Markov model. Due to the fluctuation of the filter parameters, a resilient filter taking into account parameter uncertainty is adopted. Being different from the norm-bound type of uncertainty which has been studied in a considerable number of the existing literatures, the interval type of uncertainty is introduced so as to describe uncertain phenomenon more accurately. By means of convex optimal method, the gains of filter are derived to guarantee the stochastic stability and extended dissipativity of the filtering error system under the wave of the filter parameters. Considering the limited computing power of MATLAB solver, a relatively simple simulation is exploited to verify the effectiveness and merits of the theoretical findings where the relationships among optimal performance index, uncertain parameter $\sigma $ , and asynchronous rate are revealed.

A new globally exponentially synchronization criterion for complex dynamical networks is proposed in this paper taking account of discrete time communications units and time delay is considered, in which globally exponential convergence is achieved. By introducing larger decision matrices to gain time-delay synchronization criterion, it not only inherits the benefits of full utilization of available information, but also has less conservatism. At the same time, except for original system parameters, no more new decision variables are increased. A time-dependent Lyapunov functional approach is proposed to prove the effectiveness of the criterion. Some simulation results hold the feasibility of the theory.

The closed-loop network design of a hybrid manufacturing and recovering system plays an important role on improving both the production efficiency and environmental quality, especially under a multi-product multi-stage scenario. Besides, with the growing awareness of environmental concerns by legislators, decision makers have been forced not only to enhance utilization of the returned products or components but also to control carbon emission of the whole hybrid system. To optimize the closed-loop logistics superstructure design, a multi-objective model integrating the manufacturing and recovering process in a multi-product multi-stage environment is proposed in this research. Both operations cost and environmental influence are regarded as two objective categories. In addition, the carbon emission regulation is considered in the model by integrating and characterizing the energy input and greenhouse output throughout the hybrid system. Finally, a numerical case originated from an electronic assembly plant is presented under different scenarios to illustrate the effectiveness of the proposed approach. A sensitivity analysis is given to compare the impacts of carbon emission and return rate on each sub-objective of the logistics and environmental issues.

In this study, the problem of event-triggered-based adaptive control (ETAC) for a class of discrete-time nonlinear systems with unknown parameters and nonlinear uncertainties is considered. Both ne...

This note analyzes the synchronization problem for a class of complex networks. The considered network topology, symbolized by adjacent matrices, is assumed to be time-varying and subjected to a Markov process. When designing controllers to synchronize the network, we consider the asynchronous phenomenon occurred between the mode of the adjacent matrices and that of the controllers. A properly defined stochastic process, which is related to the Markov process by a conditional probability is then introduced to indicate the controller mode. Base on this connection, a sufficient condition is derived by using Lyapunov–Krasoviskii theorem to ensure the global synchronization of the considered complex network with a predefined noise attenuation level. The design scheme is presented by solving a series of LMIs. A numerical example is finally provided to verify the effectiveness of the proposed method.

This paper addresses the asynchronous H ∞ control problem for a class of continuous-time semi-Markov jump linear systems. Under the assumption that the system mode is not accessible, we introduce a discrete-state and continuous-time process which is related to the system mode by a conditional probability as a switching signal of the controller. By constructing a semi-Markov-based Lyapunov funcational, a sufficient condition to ensure the stability of the system with a prescribed H ∞ performance is established. In addition, an equivalent statement is derived by introducing a slack variable, and the asynchronous controller is designed. Two examples are presented to illustrate the effectiveness of the proposed new design method.

Principal component analysis (PCA) has been widely applied in process monitoring of modern industrial systems. PCA performs fault detection by mapping the process data into a low dimensional subspace and tracking the process behavior using T2 and SPE statistics, whilst in fault isolation, it heavily relies on contribution plot or reconstruction based approaches. However, conventional methods based on contribution plot and reconstruction suffer from insufficient fault isolation capabilities. In order to improve the fault isolation performance, this article proposes a novel fault detection and isolation approach based on the Structured Joint Sparse PCA (SJSPCA). The objective function of SJSPCA involves two regularization terms: l 2,1 norm and the graph Laplacian. By imposing the l 2,1 norm term, SJSPCA is able to achieve row-wise sparsity, introducing the graph Laplacian regularization term can incorporate structured variable correlation information. In fault detection, conventional T2 and SPE statistics are constructed to detect abnormal situations. Once a fault is detected, a two stage fault isolation strategy is considered and a score index is calculated for each variable. The row-sparsity property of l 2,1 norm ensures that the score indices associated to normal variables approach zero and the graph Laplacian constraint helps isolation of correlated faulty variables. The validity of SJSPCA in fault detection and isolation is illustrated by a process fault observed in an industrial blast furnace iron-making process.