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A PDE‐constrained model predictive control (MPC) algorithm for open‐channel systems based on the Saint‐Vevant(S‐V) equations is investigated in this paper. The S‐V equations, which precisely model the dynamics of open‐channel systems, are quasi‐linear hyperbolic partial differential equations (PDEs) without analytical solutions. Directly applying the S‐V equations to an MPC controller design becomes sophisticated. In this work, the calculus of variation is used to obtain the adjoint equations and the adjoint analysis method is utilized to deduce the gradients of the MPC optimization problem. Particularly, the physical constraints involving both the state and control variables are also considered. A gradient‐based optimization algorithm in combination with the numerical computation of Preissmann implicit scheme is proposed to solve the constrained MPC optimization problem. The control performances of the developed PDE‐constrained MPC algorithm with respect to the controlled water levels and gate openings are compared with those of the MPC controller designed for the linearized model. All the simulation tests are carried out on an aqueduct reach in Yehe Irrigation District in Hebei Province, China. The results show that the proposed PDE‐constrained MPC algorithm is a promising method in dealing with the constraints in terms of hyperbolic PDEs, control variables and state variables simultaneously. [ABSTRACT FROM AUTHOR]

To promote the widespread use of mobile robots in diverse fields, the performance of trajectory tracking must be ensured. To address the constraints and nonlinear features associated with mobile robot systems, nonlinear model predictive control (MPC) is applied to realize trajectory tracking of mobile robots. Specifically, to alleviate the online computational complexity of nonlinear MPC, this paper devises a lattice piecewise affine (PWA) approximation method that can approximate both the nonlinear system and control law of explicit nonlinear MPC. The kinematic model of the mobile robot is successively linearized along the trajectory to obtain a linear time‐varying description of the system, which is then expressed using a lattice PWA model. Subsequently, the nonlinear MPC problem can be transformed into a series of linear MPC problems. Furthermore, to reduce the complexity of online calculation of multiple linear MPC problems, the optimal solution of the linear MPCs is approximated by using the lattice PWA models. That is, for different sampling states, the optimal control inputs are obtained offline, and lattice PWA approximations are constructed for the state control pairs. Simulations are performed to evaluate the performance of the method in comparison with the state‐of‐the‐art methods, and the results show that the method has a higher online computing speed and can decrease the offline computing time without significantly increasing the tracking error. [ABSTRACT FROM AUTHOR]

This paper studies data‐driven distributed fault diagnosis for large‐scale systems using sensor networks. To be specific, a distributed fault detection scheme based on correlation analysis is first proposed to improve the fault detection performance by minimizing the impact of noise‐induced uncertainty. The core of the method is to implement the correlation of the coupled nodes to reduce the covariance of the residual signal in a distributed manner. Then, a fault localization approach is developed to locate the fault by measuring and comparing the degree of abnormality. A case study on Tennessee Eastman process is given in the end to demonstrate the proposed approach. [ABSTRACT FROM AUTHOR]

This paper studies the active fault tolerant load frequency control of multi‐area power systems with electric vehicles under deception attacks. An integrated design of fault estimation and fault‐tolerant control is proposed to guarantee the stability of the system under sensor faults and deception attacks. Considering the uncertainty caused by the demand of the owner and the state of the battery, a multi‐area power system model is proposed. Then, an active fault tolerant load frequency control scheme is designed. The proportional‐derivative sliding mode observer is used to estimate the fault and system status in real‐time. During the fault occurrence, the estimated value obtained by the observer is utilized to design the controller without any fault diagnosis scheme, which simplifies the controller design process. A sufficient Lyapunov‐Krasovskii criterion is derived to ensure the stability performance of the multi‐area power system. Finally, simulation examples are provided for a three‐area power system contains electric vehicles, and the results prove the correctness and feasibility of the proposed fault‐tolerant control scheme. [ABSTRACT FROM AUTHOR]

The stability control of suspension density is an important means to ensure the quality of fine coal in dense medium separation (DMS) process. This paper focuses on the tracking control issue of the dense medium suspension density in a non‐uniform sampling and networked control environment, and proposes a multi‐rate event trigger control design method. Firstly, lifting technique and multi‐rate identification method are adopted to establish the multi‐rate model. Secondly, a multi‐rate event‐triggered predictive control (MEPC) algorithm is proposed to reduce the waste of communication resources and ensure tracking performance. Furthermore, by using a Kalman filter to eliminate the effects of noise and developing a compensation mechanism to predict the lost data, a robust MEPC (RMEPC) algorithm is further proposed for imperfect data in the actual production process. Finally, the hardware‐in‐the‐loop simulation experiments with actual parameters of coal preparation plant have been carried out, showing the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper investigates an event‐triggered cooperative control policy of moving‐target encirclement and tracking with time‐varying radius commands for unmanned aerial vehicle (UAV) formation. Different from the existing works, this study takes into account the relative motion model between the UAVs and the target in order to design a guidance law specifically tailored for moving‐target encirclement and tracking. Additionally, a time‐varying radius command is introduced, allowing the radius of the circular orbit formed by the UAVs around the target to adjust according to the commands. This flexibility and adaptability enhance the UAV formation's performance in complex work scenes and unstructured geometric spaces. To address the concerns of communication and computing costs associated with time‐triggered guidance laws, an event‐triggered method is proposed for dynamic target encirclement and tracking, eliminating the occurrence of Zeno behaviour. The stability of the proposed guidance laws is mathematically proven using the interconnected systems lemma. Finally, the effectiveness of the controllers is verified by some simulations. [ABSTRACT FROM AUTHOR]

This paper is concerned with optimal security problem of cyber‐physical systems with state attacks. Consider a linear physical system, assuming that the control input signal of the network layer is vulnerable to state attack. For a system that cannot measure all states, by combining output integral sliding mode, robust observer, and adaptive methods, an adaptive output integral sliding mode control method is proposed to maintain the safe operation of cyber physical systems under state attacks. Moreover, by optimizing the linear control gain matrix, the control strategy the authors designed can use the minimum cost to compensate the impact on the CPS system. Different from the existing results, (1) It is assumed that the system information of the attack signal is not fully known, we assume that the attacker can only measure a small amount of state information. (2) Not only the stability of ideal sliding mode is proved, but also the upper bound of the augmented state composed of system states and the error dynamic is given. (3) The sliding mode compensator can eliminate the impact of state attack, on the one hand, the damage caused by the state attack has been eliminated by using the upper bound of error, and on the other hand, the cost is reduced by optimizing the linear control gain. Finally, a power system with 3 generators and 6 buses is used to prove the effectiveness of the adaptive output integral sliding mode control scheme. [ABSTRACT FROM AUTHOR]

In this paper, a composite fixed‐time sliding mode controller is proposed to address the problems of finite‐time escape and large initial error inherent in disturbance observer (DOB) based controllers. The proposed hybrid control strategy utilizes a fixed‐time DOB and is designed to stabilize a class of second‐order systems in the presence of both matched and mismatched disturbances, while ensuring fixed‐time convergence. First, a fixed‐time DOB is developed to estimate both types of disturbances. Second, a robust sliding mode variable with variable exponent coefficient and a former finite‐time sliding mode controller are proposed to guarantee state convergence without using DOB estimation, thereby preventing the state from diverging due to the unknown large initial error caused by DOB and ensuring convergence prior to switching. Third, a latter robust fixed‐time DOB‐based controller is designed after exact estimation of disturbances is achieved and an estimation of the overall convergence time is provided. Furthermore, strict Lyapunov analysis is employed to prove that the disturbed system under the fixed‐time DOB and composite sliding mode controller is fixed‐time stable. Simulation results for a standard planar uncertain system and Buck converter with mismatched disturbance demonstrate the effectiveness of the proposed controller. [ABSTRACT FROM AUTHOR]

This paper deals with the optimal control problem for partially observed leader–follower stochastic differential game. By virtue of the classical variational method and Girsanov's theorem, the stochastic maximum principles for the follower under one type of partially observed case and for the leader under the complete information structure are derived. As applications, two partially observed cases are considered for the linear–quadratic models. Then by the stochastic filtering technique, the optimal feedback controls for the follower and the leader are represented by the new stochastic Riccati equations. [ABSTRACT FROM AUTHOR]

This paper concentrates on network congestion and security issue for networked control systems under the hybrid attacks. The hybrid attacks including deception attack, replay attack and denial‐of‐service attack are modelled as Bernoulli random process. A general form adaptive event‐triggered scheme (AETS) under the hybrid attacks is designed to alleviate the network congestion and save communication resources utilizing adaptive threshold and the weighted average of data packets. Meanwhile, the security issue under the hybrid attacks is addressed by a dynamic output feedback controller (DOFC) based on the AETS. Moreover, the sufficient conditions are obtained by a piecewise Lyapunov function to guarantee that the closed‐loop system is exponentially mean‐square stable. A practical experiment on networked motor control system verifies the effectiveness of the proposed scheme. The proposed scheme can not only save communication resources to further alleviate network congestion, but also defense the hybrid attacks in the network. [ABSTRACT FROM AUTHOR]

This paper proposes a tube‐based robust model predictive control (TMPC) scheme with an optimal tube for disturbance‐affected linear systems. In the literature on TMPC, there is no proper methodology to handle the considerable effects of the tube size on the closed‐loop system performance. There is usually a trade‐off between the disturbance rejection level and the amount of control effort available for the MPC problem. In some applications, it is nearly impossible to find a feasible TMPC to have a sufficient amount of states and inputs feasible sets for the MPC optimization problem. It would be a vital contribution to the TMPC designs if an algorithm is demonstrated which can investigate the suitability of TMPC for a specific system. This paper provides a solution for the mentioned challenges by introducing the concept of Quasi‐H∞ criterion and proposing a constrained optimization problem. The optimization problem is then reformulated and simplified to present an efficient methodology for the TMPC designers. The proposed TMPC scheme could benefit from a larger terminal region and result in a larger region of attraction. The achievements in TMPC designs are shown by simulations and comparisons with the previously used techniquesover numerical case studies. [ABSTRACT FROM AUTHOR]

This paper is concerned with parameter estimate of deterministic autoregressive moving average (DARMA) systems with uniform quantized output observations. By designing system input signals, the recursive least‐squares algorithm with designed weights is proved to have convergence properties under the uniform output signal quantizer. The authors analyse the properties of the size of quantization error, which implies that the convergence properties can be achieved when the quantization error satisfies some conditions. A numerical example is supplied to demonstrate the theoretical results. This paper researches the parameter estimate of deterministic autoregressive moving average systems with quantized output data. A recursive least‐squares algorithm with designed weights is proved to have convergence properties under the uniform signal quantizer. [ABSTRACT FROM AUTHOR]

Precise timing plays a key role in the time‐sensitive industrial Internet of Things (IIoT). However, precise time synchronization requires more frequent packet exchange, which consumes more communication bandwidth and energy. This is a particular challenge in battery‐powered wireless nodes, and low communication costs have become an important factor in clock synchronization. To address the challenge of achieving low communication cost clock synchronization in distributed wireless sensor networks, this paper proposes an improved event‐triggered control and synchronization scheme with a novel asynchronous broadcast packet exchange protocol. Unlike the traditional event‐triggered control scheme which is based on synchronous polling packet exchange, this proposed asynchronous broadcast packet exchange is more communication efficient and requires fewer number of packet exchanges. And it is worth noting that the proposed algorithm in this paper is a distributed algorithm and does not require real‐time acquisition of information from neighbouring nodes. Finally, a numerical example is given to illustrate the effectiveness of the proposed event‐triggered control strategy. The efficiency and precision of the proposed clock synchronization method is evaluated by intensive simulations, which show that the number of packet exchange is reduced by 60% for a moderate IIoT network and is particularly useful for large scale network. [ABSTRACT FROM AUTHOR]

This paper investigates bisimulation relations of delayed switched Boolean control networks (DSBCNs), using the semi‐tensor product (STP) approach. Firstly, the notion of bisimulation in DSBCNs is formalized. Subsequently, based on the skeleton matrix of DSBCN, two necessary and sufficient conditions are derived for checking this notion. Moreover, as an application of bisimulation relations, this paper explores the propagation of controllability for DSBCNs through a bisimulation relation. It shows the possibility that the controllability of a DSBCN can be inferred by studying a potentially simpler DSBCN. At last, two examples are proposed to illustrate the efficiency of the obtained results. [ABSTRACT FROM AUTHOR]

This paper realizes the evaluation and optimal decision of the spoofing effect on the global navigation satellite system (GNSS) under navigation countermeasures based on the game perspective, to overcome the limitations of current relevant research findings and maintain the secure application of the GNSS and maximize its defensive benefit during countering. An evaluation system based on the perspective of the game between spoofing strategies and defense measures is created to build the benefit matrix, which can solve the limitation in building a benefit matrix (the vital element in a game) that conforms to the confrontation‐oriented GNSS spoofing properties. Based on the built benefit matrix, a method integrating the grey expert‐based system with the hybrid evolutionary method is developed to realize quantifying the matrix and further make the optimal decision stably, which can solve the uncertainties of qualitative indices during quantifying and no global optimal solution for benefit equations deduced from the quantified matrix. Finally, based on various judgment principles, simulation and testing results verify the effectiveness of the obtained results and the superiority of the proposed method by comparing the results solved by the method in this paper with the results obtained by other decision‐making processing methods. [ABSTRACT FROM AUTHOR]

This paper investigates the proportional‐integral‐derivative (PID) control of positive systems in the discrete‐time case. First, a tuning parameter is introduced to construct the proportional‐integral‐derivative control framework of positive systems. The proportional‐integral‐derivative control problem is thus transformed into the control synthesis of positive systems with time‐delay. By virtue of a matrix decomposition approach, the gain matrices of proportional‐integral‐derivative controller are designed. Using co‐positive Lyapunov functions and linear programming, the positivity and stability of positive systems are achieved under the designed proportional‐integral‐derivative controller. Then, the proportional‐integral‐derivative control of positive systems with reference signal is solved. The main contribution of this paper lies in that linear programming associated to the matrix decomposition approach is first presented to handle the proportional‐integral‐derivative control of positive systems. Finally, two illustrative examples are provided to verify the effectiveness of the proposed design. [ABSTRACT FROM AUTHOR]

This paper focuses on the global prespecifiable fixed‐time control problem of uncertain strict‐feedback nonlinear system with input quantization. In contrast to the existing fixed‐time control results, the signs of the control gains can be unknown in our paper. Unlike the traditional fixed‐time control schemes, some exponential functions are added into the virtual controllers to accelerate the convergence rate. Besides, some dynamic switching functions are also introduced to compensate the unknown control signs. Based on the novel regulate law, the switching functions can be regulated online to ensure the fixed‐time stability for the considered system. Furthermore, the settling time can be easily adjusted by selecting the controller parameters. Simulation results are provided to illustrate the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

This paper studies the iterative learning control (ILC) for a class of second‐order nonlinear hyperbolic impulsive partial differential systems. Firstly, to follow the discontinuous desired output, a P‐type learning law is adopted, and sufficient conditions for the convergence of the tracking error is established under identified initial state value. The rigorous analysis is also given using the impulsive Gronwall inequality. Secondly, the tracking error of output trajectory is considered in systems with state initial values shifting based on an initial learning algorithm. These results of this paper show that the tracking error on the finite time interval can uniform converge to 0 as the iteration index goes to infinity if impulse number of the systems is only a finite numbers. Finally, two numerical simulation examples are given to verify the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

This paper presents a self‐triggered control approach with the view of risks for discrete‐time linear stochastic systems. More specifically, under the assumption that the first two moments of the disturbance distribution are known, the paper considers the problem of designing a control law that reduces the use of communication and power resources by aperiodically sampling the states and updating control inputs, while achieving a given performance goal at a risk below a specified level. This problem is approached by using the worst‐case Conditional Value‐at‐Risk as a measure of risk, to quantify the tail behavior of the stochastic systems, thus allowing us to suppress large losses. A numerical example is provided to illustrate the performance of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper aims to improve the efficiency of parameter identification of the nonlinear state‐space model (SSM). The commonly used particle Markov chain Monte Carlo (PMCMC) method is time‐consuming. The surrogate model is a useful acceleration strategy, but it is expensive to establish a global high‐precision surrogate model. This paper proposes an efficient algorithm that gradually estimates the unknown parameters in two stages. In the first stage, a reduced region is established based on the latest method. We train a local Gaussian Process regression (GPR) of the likelihood function in the reduced region based on the optimum Latin hypercube design (OLHD). In the second stage, we identify the unknown parameters more accurately based on the MCMC method. When the proposal sample is in the reduced region, we use GPR to estimate the likelihood; otherwise, BPF is used to estimate the likelihood. The reduced region is usually the high probability density region, which is why the algorithm is efficient. It is proved that the acceptance rate of any two samples based on the proposed algorithm is theoretically convergent to that of the PMCMC algorithm. Two examples demonstrate that the proposed method performs well in accuracy and efficiency. [ABSTRACT FROM AUTHOR]

The paper addresses the problem of accelerating predictive control of non‐linear system models augmented with Gaussian processes (GP‐MPC). Due to the non‐linear and stochastic prediction model, predictive control of GP‐based models requires to solve a stochastic optimization problem. Different model simplification methods have to be applied to reformulate this problem to a deterministic, non‐linear optimization task that can be handled by a numerical solver. As these problems are still complex, especially with exact moment calculations, real‐time implementation of GP‐MPC is extremely challenging. The existing solutions accelerate the computations at the solver level by linearizing the non‐linear optimization problem and applying sequential convexification. In contrast, this paper proposes a novel GP‐MPC solution approach that without linearization formulates a series of surrogate quadratic programs (QP‐s) to iteratively obtain the solution of the original non‐linear optimization problem. The first step is embedding the non‐linear mean‐variance dynamics of the GP‐MPC prediction model in a linear parameter‐varying (LPV) structure and rewriting the constraints in parameter‐varying form. By fixing the scheduling trajectory at a known variation (based on previously computed or initial state‐input trajectories), optimization of the input sequence for the remaining varying linear model reduces to a linearly constrained quadratic program. After solving the QP, the non‐linear prediction model is simulated for the new control input sequence and new scheduling trajectories are updated. The procedure is iterated until the convergence of the scheduling, that is, the solution of the QP converges to the solution of the original non‐linear optimization problem. By designing a reference tracking controller for a 4DOF robot arm, we illustrate that the convergence is remarkably fast and the approach is computationally advantageous compared to current solutions. The proposed method enables the application of GP‐MPC algorithms even with exact moment matching on fast dynamical systems and requires only a QP solver. This paper aims to discuss the approach of constrained modified feedback linearization model predictive control (CMFLMPC) for the spacecraft simulator. The simulation and experimental results demonstrate that the proposed hybrid controller has an insignificant calculative cost and facilitates the spacecraft to perform the regulation manoeuvre with sufficient precision in the presence of external torques and actuator saturations. [ABSTRACT FROM AUTHOR]

This paper aims to discuss the approach of constrained modified feedback linearization model predictive control for the spacecraft simulator. By utilizing the high accuracy and constrained properties of model predictive control (MPC), an optimum MPC is designed for the spacecraft feedback linearized system. The composite controller has the ability to control both the attitude and angular velocity of the reaction wheels (i.e. steering the angular momentum to zero at the end of the maneuver). The simulation and experimental results demonstrate that the proposed hybrid controller has an insignificant calculative cost and facilitates the spacecraft to perform regulation maneuver with sufficient precision in the presence of external torques and actuator saturations. This paper aims to discuss the approach of constrained modified feedback linearization model predictive control (CMFLMPC) for the spacecraft simulator. The simulation and experimental results demonstrate that the proposed hybrid controller has an insignificant calculative cost and facilitates the spacecraft to perform the regulation maneuver with sufficient precision in the presence of external torques and actuator saturations. [ABSTRACT FROM AUTHOR]

This paper addresses the development of a set of novel autonomous control laws for the navigation of multiple mini or micro quadrotors in a workspace populated with cylindrical obstacles. To the authors' knowledge, this is the first time that such a set of control inputs for the autonomous control of multiple quadrotors and avoidance of cylindrical obstacles is being derived from a single Lyapunov function. The avoidance of the cylindrical obstacles is achieved via a Minimum Distance Technique that allows a quadrotor to avoid the closest point on the curved surface of a cylinder at every unit time. In addition, the novel controllers ensure a near‐to‐horizontal orientation of a quadrotor at every unit time and the hovering motion exhibited by the quadrotor in a neighborhood of its target. The hovering capability has been solved completely in this paper by solving the underactuatedness of the quadrotor which allows for the design of the maximum translational velocities needed for vertical take‐off and landing, and hovering. This is important in applications where payloads are sensitive to acrobatic orientations. Computer simulations that mimic real‐life scenarios using cylindrical towers as obstacles in a city‐like environment illustrates the effectiveness of the controllers. [ABSTRACT FROM AUTHOR]

Regarding the low accuracy and instability of common online methods for estimating dynamic models in the time domain, in the presence of uncertainty in system dynamics, sensor noise and environmental disturbances, this area is still open for further research. In this paper, a new estimation method is proposed based on a new online robust meta‐heuristic adaptive LSQR (ORALSQR) for simultaneous estimation of a multi input/output linear dynamic model and system state variables. This new adaptive LSQR algorithm is used to solve the output matrix equations of the least squares error problem. The presented algorithm, based on its iterative nature, searches the answer subspace by using a new meta‐heuristic logic. In addition, the algorithm solving steps and the search domain size in each iteration are intelligently determined by the method. In an identification maneuver, this method estimates the state variables using an estimated model in the Kalman filter, then estimates the model online for the next iteration using the state variables. In addition the stability proof of this method is presented. Numerical results show more accuracy and robustness of this method compared to the other methods mentioned in this paper which contain LS and RLS based estimation methods. [ABSTRACT FROM AUTHOR]

This paper proposes an adaptive compensation control algorithm for solving the actuator failures problem of nonlinear stochastic multi‐agent systems (MASs) under the directed communication topology. It should be emphasized that the coexistence of unknown nonlinearities, stochastic perturbations and actuator failures makes the implementation of control protocol very difficult and extremely challenging. To achieve the control objective, fuzzy logic systems (FLSs) are first employed to deal with the unknown nonlinearities of each agent. Then, the threshold‐based event‐triggered mechanism is further considered to reduce the communication burden of the system in the case of limited communication resources. Moreover, the issue of "explosion of complexity" is solved by using dynamic surface control (DSC) technique in the process of backstepping design. With these efforts, the actuator failures are circumvented and the outputs of the followers converge to the convex hull spanned by the multiple leaders' outputs. Finally, the simulation results of multiple single‐link robots show the validity of the proposed design scheme. First, the studied stochastic nonlinear MASs consisting of N leaders is considered, in which the dynamics of the leaders can be completely unknowable. Therefore, the leader models can be more general compared to the previous control results [42–46]. Moreover, we cleverly apply a novel coordinate transformation to deal with the consensus problem of multi‐leader. Second, in this paper, the adaptive compensation controller is constructed to effectively compensate the uncertainties of the actuator failure models. In addition, event‐triggered control mechanism for the input of the actuator failure model is used to address the problem of resources transmission limitation. Third, the proposed communication protocol can ensure that the outputs of the followers converge to the convex hull spanned by the outputs of the multiple leaders. Furthermore, the problem of "explosion of complexity" in the derivation process is solved by fusing the backstepping and DSC technology. [ABSTRACT FROM AUTHOR]

This paper concerns the improvement on Proportional‐Integration‐Derivative (PID) control for standard second‐order plus time‐delay systems (SOPTD). To achieve higher tracking precision and stronger disturbance rejection, a PID type‐ii and type‐iii control‐loops design method based on the combination of the linear quadratic regulator (LQR) and dominant pole configuration technology is proposed in this paper. The PID type‐ii control loop is capable of achieving perfect tracking of step and ramp reference signals with zero steady‐state position and velocity error. The PID type‐iii control loop is capable of achieving perfect tracking of step, ramp and parabolic reference signals with zero steady‐state position, velocity and acceleration error. The effectiveness of the proposed PID type‐ii and type‐iii controller parameters tuning rules has been demonstrated via simulation of over‐damped, critical‐damped and under‐damped systems. To be specific, compared with the PID type‐i control loop, rise time, settling time and disturbance rejection property of the system have been significantly improved while realizing faster reference signal tracking. Finally, the effects of non‐dominant pole on the stability and robustness of PID type‐ii and PID type‐iii closed loop systems have also been discussed. [ABSTRACT FROM AUTHOR]

In this work, the consensus problem of multi‐agent systems is addressed whose agent dynamics are described by the switched linear system, and the interaction topology is a directed graph. Two cases are considered: For completely controllable agent dynamics, a novel construct method of control law and switching signal are proposed to derive sufficient conditions to solve the finite‐time consensus problem. When the controllability of the agent system is incomplete, it is proven that the uncontrollable sub‐mode of agent dynamics is consistently asymptotically stabilizable is a key condition to achieve consistency for multi‐agent systems. Finally, numerical examples are provided to demonstrate the effectiveness of the theoretical results obtained in this paper. [ABSTRACT FROM AUTHOR]

This paper deals with parameter estimation problem of large‐scale systems. A recursive distributed parameter estimation algorithm, based on the minimization of the prediction estimation error method, is developed. Specifically, the class of large‐scale systems that are composed of several interconnected sub‐systems is considered. Each interconnected sub‐system is modelled by a linear discrete‐time state space mathematical model with unknown parameters. The convergence analysis is then achieved using the Lyapunov approach. The theoretical analysis and simulation results prove the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper investigates the resilient annular finite‐time synchronization and boundedness problems for master‐slave systems under dynamic event‐triggered scheme (ETS), actuator faults, and scaling attacks. A comprehensive model for characterizing scaling attacks is presented. The switched dynamic ETS is developed to reduce the bandwidth pressure. Two different internal variables are designed, each of which changes based on the switching manifold. Simultaneously, the resilient problem of switched master‐slave systems with respect to stochastic scaling attacks is considered, where there are limited malicious signals introduced by the adversary. Based on the Lyapunov‐like function involving the different internal dynamic variables, a set of criteria is established in the form of linear matrix inequalities (LMIs), which guarantee the annular finite‐time boundedness. Finally, two examples are presented to verify the theoretical findings. [ABSTRACT FROM AUTHOR]

The paper proposes the method of uniform flow loading the improved consensus algorithm to achieve obstacle avoidance for multi‐UAV with 3‐D formation. In view of that the flow function is generally only applicable to two dimensional flow field, dimension reduction is to treat multi‐UAV formation as each UAV flows around along the circular surface of the projected cylinder of spherical obstacle at its different heights. To formation guaranteeing the smooth realization of obstacle avoidance of multi‐UAV formation centered on the leader, it is used the tactics of virtually expanding the radius of these static or moving equivalent cylindrical obstacles to reserve a certain space. The innovation of this strategy is to further reduce the possibility of collision between UAV formation and the edge of obstacles, meanwhile the problem of multi‐UAV formation avoiding obstacles is transformed into the problem of collision avoidance navigation for the leader treated as a particle. It is considered collision avoidance for multi‐UAV formation under three scenarios assumed are the most common situations, and the simulation experimental results of these scenarios are demonstrated on the validity of the designed experiments based on these algorithms. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of distributed finite‐time (FT) event‐triggered consensus of multi‐agent systems (MASs) under fixed and switching topologies. To conserve the limited communication resources, a new distributed event‐triggered control protocol is proposed, in which the agents exchange information via discontinuous communication among the follower agents. Then, to solve the FT consensus problem, norm‐normalized sign functions are employed, and sufficient Lyapunov conditions are provided. On this basis, the MASs proves capable of reaching consensus within finite time under fixed and switching topologies. Finally, the effectiveness of the developed algorithm is verified via numerical simulation. [ABSTRACT FROM AUTHOR]

In this paper, a consensus based fully distributed optimization algorithm is proposed for solving economic dispatch problem (EDP) in smart grid. Since the incremental cost of all buses reach consensus when the optimal solution is achieved, it is selected as a consensus variable. An additional variable at each bus, called "surplus" is added to record the local power mismatch, which is used as a feedback variable to purse the balance between power supply and demand. Different from most of the existing distributed methods which require the communication network to be balanced, the algorithm uses a row random matrix and a column random matrix to precisely steer all the agents to asymptotically converge to a global optimal solution over a time‐varying directed communication network. Due to the use of a fixed step size, the proposed algorithm also outperforms other algorithms in terms of convergence speed. The graph and eigenvalue perturbation theories are employed for the algorithm convergence analysis, and the upper bound of the parameters required for convergence is given theoretically. Finally, the performance and scalability of the proposed distributed algorithm are verified by several case studies conducted on the IEEE 14‐bus power system and a 200‐node test system. [ABSTRACT FROM AUTHOR]

This paper investigates the adaptive robust control problem based on reinforcement learning for an affine nonlinear system with unknown time‐varying uncertainty. Inspired by the ability to estimate uncertainty of neural network, a novel policy iteration algorithm is proposed which alternates between the value evaluation, uncertainty estimation, and policy update steps until the adaptive robust control law is obtained. Especially during the step of uncertainty estimation, the unknown time‐varying uncertainty is approximated by a radial basis function neural network and introduce it into the reinforcement learning framework. By designing an appropriate utility function, the algorithm improves both convergence rate and final approximate error comparing with existing reinforcement learning algorithm. The Lyapunov stability theorem provides theoretical demonstrations of the stability and convergence. Furthermore, the uniformly ultimately bounded stability of the affine nonlinear system is demonstrated with unknown time‐varying uncertainty. Finally, the performance of the proposed algorithm is demonstrated through a torsion pendulum system. [ABSTRACT FROM AUTHOR]

This paper introduces a distributed adaptive formation control for large‐scale multi‐agent systems (LS‐MAS) that addresses the heavy computational complexity and communication traffic challenges while directly extending conventional distributed control from small scale to large scale. Specifically, a novel hierarchical game theoretic algorithm is developed to provide a feasible theory foundation for solving LS‐MAS distributed optimal formation problem by effectively integrating the mean‐field game (MFG), the Stackelberg game, and the cooperative game. In particular, LS‐MAS is divided into multiple groups geographically with each having one group leader and a significant amount of followers. Then, a cooperative game is used among multi‐group leaders to formulate distributed inter‐group formation control for leaders. Meanwhile, an MFG is adopted for a large number of intra‐group followers to achieve the collective intra‐group formation while a Stackelberg game is connecting the followers with their corresponding leader within the same group to achieve the overall LS‐MAS multi‐group formation behavior. Moreover, a hybrid actor–critic‐based reinforcement learning algorithm is constructed to learn the solution of the hierarchical game‐based optimal distributed formation control. Finally, to show the effectiveness of the presented schemes, numerical simulations and Lyapunov analysis is performed. [ABSTRACT FROM AUTHOR]

Feature selection is a fundamental pre‐processing step in machine learning that aims to reduce the dimensionality of a dataset by selecting the most effective features from the original features. This process is regarded as a combinatorial optimization problem, and the grey wolf optimizer (GWO), a novel meta‐heuristic algorithm, has gained popularity in feature selection due to its fast convergence speed and easy implementation. In this paper, an improved binary GWO algorithm incorporating a novel Population Adaptation strategy called PA‐BGWO is proposed. The PA‐BGWO takes into account the characteristics of the feature selection problem and designs three strategies. The proposed strategy includes an adaptive individual update procedure to enhance the exploitation ability and accelerate convergence speed, a head wolf fine‐tuned mechanism to exert the impact on each independent feature of the objective function, and a filter‐based method ReliefF for calculating feature weights with dynamically adjusted mutation probabilities based on the ranking features to effectively escape from local optima. Experimental comparisons with several state‐of‐the‐art feature selection methods on 15 classification problems demonstrate that the proposed approach can select a small feature subset with higher classification accuracy in most cases. [ABSTRACT FROM AUTHOR]

Robotic 3D bin packing (R‐3dBPP), aiming to place deformed cases in various sizes in the container without fences, is a comprehensive application that includes perception, planning, execution, and hardware design. Traditional studies assume that the context of the real space must be accurately perceived and represented. However, disjunctions between the planner and reality are unavoidable in R‐3dBPP, especially with low‐cost sensors. As far as the author knows, there is no practical solution. In this paper, the above assumption is discarded and the typical types of uncertainties prevalent to guide the design of the algorithms are formulated. A new online bin‐packing algorithm is proposed, keeping deformed boxes stacked in close contact with each other, so that the whole pallet is kept stable by friction between the boxes and the container. In order to meet the requirement of close contact under the non‐negligible errors from sensors and planners, a compliant‐based motion planning system is introduced. It replaces the precision‐based feedback with a compliant end‐effector and accompanying motion strategies. Last but not least, a complete online bin‐packing robot system is developed and the system's performance under the influence of the uncertainties mentioned above through simulation and physical experiments is evaluated. [ABSTRACT FROM AUTHOR]

Concerning the problems that the traditional Convolutional Neural Network (CNN) ignores contextual semantic information, and the traditional Recurrent Neural Network (RNN) has information memory loss and vanishing gradient, this paper proposes a Bi‐directional Encoder Representations from Transformers (BERT)‐based dual‐channel parallel hybrid neural network model for text sentiment analysis. The BERT model is used to convert text into word vectors; the dual‐channel parallel hybrid neural network model constructed by CNN and Bi‐directional Long Short‐Term Memory (BiLSTM) extracts local and global semantic features of the text, which can obtain more comprehensive sentiment features; the attention mechanism enables some words to get more attention that highlights important words and improves the model's sentiment classification ability. Finally, the dual‐channel output features are fused for sentiment classification. The experimental results on the hotel review datasets show that the Accuracy of the proposed model in sentiment classification reaches 92.35% and the F1 score reaches 91.59%. [ABSTRACT FROM AUTHOR]

Both electric vehicles and Tokamak power supply use multiple parallel converters to realize high current operation in four quadrants. A finite‐time current sharing control algorithm based on disturbance compensation is proposed to improve the converter system's dynamic and steady‐state performance so that the system can achieve the required load current and branch‐current sharing in a finite time. In the proposed algorithm, this paper adopts a finite‐time observer for unknown disturbances in the converter system and load circuit due to disturbances, such as unknown inductance and resistance in the converter system. The results show that the load current control overshoot is minimal, and the dynamic performance of each branch current is higher than the traditional control algorithm. A finite‐time current sharing control algorithm based on disturbance compensation is proposed so that the converter system can achieve the required load current and branch current sharing in a finite time. [ABSTRACT FROM AUTHOR]

A tropical cyclone (TC) is extreme weather severely threatening coastal wind turbines. However, the non‐extreme wind speed period (nEWSP) before and after it can also bring considerable economic benefits to wind farms. Aiming at the working conditions of nEWSP‐TC, this paper analyzes the characteristics of the wind field based on historical actual TC data and constructs a pseudo‐Monte Carlo experiment, uses the experimental results to construct a knowledge base and an inference engine, and forms an expert system to guide the yaw control of wind turbines in nEWSP‐TC. The simulation results show that the yaw error has different effects on the fatigue load of the wind turbine under different working conditions of nEWSP‐TC, and the proposed improved yaw strategy can reduce the fatigue load of the wind turbine under the premise of limited power loss and improve safe operation capability of wind turbines under nEWSP‐TC conditions. [ABSTRACT FROM AUTHOR]

The battery management system (BMS) is a crucial component of electric vehicles (EVs) owing to its sustainable operation. To ensure optimal performance of the BMS, state of charge (SOC) of the equipped battery is required to be effectively and accurately estimated. In this paper, the authors consider high‐order equivalent circuit model (ECM) to capture the dynamic characteristics of lithium‐ion batteries, which are connected in series with internal resistance by 2‐RC networks. The parameters of the RC network are determined by mathematically solving the working conditions of the two states. Moreover, the parameters of the battery can be derived by hybrid pulse power characterization (HPPC) tests. Then, based on the open‐circuit voltage, the proposed feedback‐based extended Kalman filtering (FEKF) algorithm is established. The parameters from the simulation have shown that the highest error is 0.0306 V, the optimal knowledge of which can improve the SOC estimation approach remarkably and can provide a reference value. Afterwards, the non‐linear predicting and corrective techniques are applied to the experiment in the extended calculation process. The original error is reduced by the FEKF algorithm, where the maximum and average errors are 0.0298 and 0.0240 V, respectively. Consequently, the established high‐order ECM utilizing the FEKF algorithm may provide SOC estimation with an error of 1.5% or less, resulting in superb performance from the lithium‐ion battery pack. [ABSTRACT FROM AUTHOR]

This paper proposes a wheeled mobile robot (WMR) robust control scheme. The feasible strategy is developed to achieve an efficient and robust autonomous mobile robot motion. To realize kinematic autonomous planning and control of the WMR, a novel controller is designed based on control Lyapunov function. This part can be divided into the following two aspects: 1) considering the nonholonomic constraints in the autonomous mobile robot trajectory tracking, a dynamic feedback‐linearization is adopted by utilizing differential flatness‐based integrated control framework to achieve full‐state controllability; 2) to compensate the structured uncertainties and slippage disturbances related to the robot kinematic model, a robust controller is designed based on control Lyapunov function with quadratic programming. Such a strategy can achieve autonomous motion even with unknown slippage disturbances subject to various constraints. Moreover, the sufficient condition is also analyzed to ensure the WMR system exponential stability. The effectiveness and performance of the proposed method are verified by numerical simulation. [ABSTRACT FROM AUTHOR]

This paper is concerned with tele‐presence and tele‐operation of autonomous vehicles over the Additive White Gaussian Noise (AWGN) channel. This wireless communication channel is subject to transmission noise and transmission power constraint. An encoder, decoder and controller are proposed by implementing a novel linearization method for linearizing the nonlinear dynamic systems at operating points. For the linearized systems, the encoder, decoder and controller are implemented that were previously proposed for output tracking as well as stability of linear dynamic systems. Two novel linearization methods are proposed and their satisfactory performances are proved for output tracking as well as stability of nonlinear dynamic system. The first method is based on the fixed linearization rate and the second one is based on the variable (optimal) linearization rate. The decoder that is based on the second method is in fact the extended Kalman filter with the optimal linearization rate. The performances of these two methods are compared with each other and the other proposed methods by implementing them to the problems of the tele‐presence and tele‐operation of autonomous vehicles over the AWGN channel. Their satisfactory performances are illustrated. It is illustrated that the second method has higher computational complexity with slightly better performance and is applicable to a larger class of reference trajectories. [ABSTRACT FROM AUTHOR]

Random jump of network communication topology challenges the tracking accuracy of linear switched reluctance motor (LSRM) system. This paper studies the pilot‐following consensus control of the multiple direct‐drive motion system (MDDMS) with semi‐Markov jump topology. The mathematical model of LSRM is constructed and the multi‐phase excitation strategy is studied. The directed graph is used to model the information transmission among the leaders and follower, and a new mechanism consensus based on previous states is provided. A consensus system based on semi‐Markov jump topology is constructed and the problem of consensus is shifted into a problem of stability analysis. A sufficient condition for consensus problem is obtained by using semi‐Markov switching system theory and graph theory. The MDDMS achieves consensus in the form of exponential convergence under the designed consensus controller. Several groups of simulation results verify the effectiveness of the proposed consensus control method. [ABSTRACT FROM AUTHOR]

Solving many‐objective optimization problems (MaOPs) by means of evolutionary algorithms obtains considerable attention in the community of evolutionary computation. However, it is a difficult task to effectively handle MaOPs with both regular and irregular Pareto front (PF) shapes. In this paper, a novel two reference vector sets based evolutionary algorithm, referred to as TVEA, is proposed to solve both types of MaOPs. TVEA can be featured as i) two independent reference vector sets are used, where the regular reference vector set is used for the regular PF MaOPs and the irregular one is used for the irregular PF MaOPs; ii) according to the two reference vector sets, TVEA automatically detects the PF shape of a MaOP at hand; iii) a new delete‐and‐add strategy is proposed to adaptively update the irregular reference vector set; iv) a novel environmental selection strategy is developed to handle the irregular PF MaOPs. TVEA is extensively evaluated on 23 benchmark MaOPs with both regular and irregular PF shapes. The results indicate that TVEA can correctly detect the PF shape of MaOPs to be solved. Compared with other state‐of‐the‐art algorithms, TVEA obtains better or at least competitive results on both types of MaOPs. [ABSTRACT FROM AUTHOR]

The behaviour analysis of farmers is very important to gradually improve the sustainable production capacity of the land and promote the economics reasonable increasement. How to effectively extract video features and identify human behaviour is a current research hotspot. In this paper, the authors propose a convolutional neural network model based on the deformable convolution including a residual module and an attention module. The model fuses and encodes video spatial features on the time axis and extracts semantic information. The authors combine the attention mechanism to extract local and global features in the video, which are used to calculate spatial features and micro‐motion features, respectively. Temporal attention mechanism fuses each micro‐motion feature of video to extract higher‐level semantic features of motion. Next, the authors describe their proposed model based on deep learning. First, the authors introduce a video frame sampling method based on the TRN algorithm. Second, they design a feature extraction and fusion model based on the ResNet model and the attention mechanism that extract local and global features in videos. Finally, the authors classify and identify the extracted video features through the classifier. The authors' approach shows comparable performance on publicly available datasets. [ABSTRACT FROM AUTHOR]

In accordance with the requirements of unmanned robot driving power supply for power supply reliability, strong power output, and portability, this paper introduces a design scheme for unmanned robot driving power supply on the basis of a flyback converter, establishes the mathematical model of driving power supply (flyback converter), designs the transformer part of a flyback converter, and recommends a straightforward and efficient clamping circuit to enhance it. The controller of the flyback converter is designed in detail. Ultimately, the feasibility and effectiveness of the design method in the driving power supply of unmanned robots are demonstrated by simulation and experiments. [ABSTRACT FROM AUTHOR]

Many systems in the real world are periodic due to periodic phenomena in nature. Periodic hybrid stochastic differential equations are often used to model them. In many situations, it is inappropriate to study whether the solutions of periodic hybrid stochastic differential equations will converge to an equilibrium state (say, 0 or the trivial solution) but more appropriate to discuss whether the probability distributions of the solutions will converge to a stationary distribution, known as stability in distribution. This paper aims to determine whether or not a periodic stochastic state feedback control can make a given nonlinear periodic hybrid differential equation, which is not stable in distribution, to become stable in distribution. This problem will be referred to as stabilisation in distribution by periodic noise. There is little known on this problem so far. This paper initiates the study in this direction. [ABSTRACT FROM AUTHOR]

Advances in image processing technology have made it possible to measure the surface shape of the road ahead while driving. A new semi‐active suspension control method considering the forward road surface shape is proposed. A vehicle model equipped with a semi‐active suspension can be expressed as an mixed logical dynamical model. When the shape of the road ahead can be measured, the information on future disturbances is available before the vehicle undergoes. In this paper, the finite time optimization problem for the mixed logical dynamical model is formulated to consider the future disturbances as a mixed integer quadratic programming problem in the same way as the conventional control problem without future disturbance. However, the mixed integer quadratic programming problem is hard to obtain the control action within the control cycle period required in the real‐time vibration control with general computers for vehicles. In this paper, the reduction of the computational load is achieved by constructing an approximation function of the designed controller. A neural network is adopted for the approximation. The performance evaluation of the proposed method is evaluated by simulations. In the simulation study, the proposed method achieves better ride comfort with the equivalent suspension stroke compared to the traditional methods. [ABSTRACT FROM AUTHOR]

This paper considers an optimal control problem of nonlinear Markov jump systems with continuous state inequality constraints. Due to the presence of continuous‐time Markov chain, no existing computation method is available to solve such an optimal control problem. In this paper, a derandomisation technique is introduced to transform the nonlinear Markov jump system into a deterministic system, which simultaneously gives rise to an equivalent deterministic dynamic optimisation problem. The control parametrisation technique is then used to partition the time horizon into a sequence of subintervals such that the control function is approximated by a piecewise constant function consistent with the partition. The heights of the piecewise constant function on the corresponding subintervals are taken as decision variables to be optimised. In this way, the approximate dynamic optimisation problem is an optimal parameter selection problem, which can be viewed as a finite dimensional optimisation problem. To solve it using a gradient‐based optimisation method, the gradient formulas of the cost function and the constraint functions are derived. Finally, a real‐world practical problem involving a bioreactor tank model is solved using the method proposed. [ABSTRACT FROM AUTHOR]

This paper investigates the appointed‐time attitude stabilization problem of a rigid spacecraft under disturbance and parametric uncertainty. A continuous appointed‐time sliding mode disturbance observer is presented to observe the synthetical disturbance term. To circumvent the singularity problem in terminal sliding mode control, a new nonsingular appointed‐time sliding mode surface is designed by employing the concept of switching control. Based on the observation information deriving from the proposed disturbance observer, an observer‐based nonsingular sliding mode control scheme is developed to stabilize the attitude control system within appointed time which is given by the user. And also, the undesired control chattering phenomenon can be attenuated. The appointed‐time stability of the controlled attitude system is proved by the Lyapunov criteria. The simulation results show that the proposed attitude stabilization control scheme is valid. [ABSTRACT FROM AUTHOR]