Showing papers by "Hao Shen published in 2022"

Extended-State-Observer-Based Adaptive Prescribed Performance Control for Hydraulic Systems With Full-State Constraints

Abstract: In this article, an innovative backstepping controller for hydraulic systems is proposed to handle system uncertainties and accomplish specified performance tracking without violating the full-state constraints. To deal with uncertainties, extended state observers ESOsand an adaptive law are integrated. ESOs are structured to estimate disturbances, whereas adaptive law is applied to approximate unknown parameters. The estimated uncertainties are then incorporated into a constrained controller, ensuring that both the prescribed transient tracking performance and the nonviolation of full-state constraints can be guaranteed. A prescribed performance function (PPF) and the barrier Lyapunov function (BLF) are synthesized to guarantee the transient behavior of the tracking error and all state errors within desirable boundaries. Then, an adaptive prescribed performance controller with uncertainty compensation is constructed by merging the BLF and PPF through back-stepping design to stabilize the closed-loop system. Finally, abundant comparative experimental results validate the proposed controller's tracking performance.

Intermittent Sampled-Data Control for Local Stabilization of Neural Networks Subject to Actuator Saturation: A Work-Interval-Dependent Functional Approach.

Abstract: This article is concerned with the local stabilization of neural networks (NNs) under intermittent sampled-data control (ISC) subject to actuator saturation. The issue is presented for two reasons: 1) the control input and the network bandwidth are always limited in practical engineering applications and 2) the existing analysis methods cannot handle the effect of the saturation nonlinearity and the ISC simultaneously. To overcome these difficulties, a work-interval-dependent Lyapunov functional is developed for the resulting closed-loop system, which is piecewise-defined, time-dependent, and also continuous. The main advantage of the proposed functional is that the information over the work interval is utilized. Based on the developed Lyapunov functional, the constraints on the basin of attraction (BoA) and the Lyapunov matrices are dropped. Then, using the generalized sector condition and the Lyapunov stability theory, two sufficient criteria for local exponential stability of the closed-loop system are developed. Moreover, two optimization strategies are put forward with the aim of enlarging the BoA and minimizing the actuator cost. Finally, two numerical examples are provided to exemplify the feasibility and reliability of the derived theoretical results.

Fixed-time synchronization for inertial Cohen-Grossberg delayed neural networks: An event-triggered approach

Abstract: This paper addresses the fixed-time synchronization problem for inertial Cohen–Grossberg neural networks with external disturbances and time-varying delays. Compared with some existing works about fixed-time synchronization control methods, a novel controller is constructed with a dynamic exponential term, which can contain the two exponents. Moreover, taking into account the increase of network complexity as well as a huge quantity of data transmission, an event-triggered mechanism is introduced to effectively utilize the limited network bandwidth. By employing the variable transformation method, differential mean value theorem, and the fixed-time stability theory, some sufficient conditions ensuring the fixed-time synchronization of inertial Cohen–Grossberg neural networks are established. Finally, two numerical examples are given to illustrate the validity of the obtained results.

Finite-Time H 1 State Estimation for PDT-Switched Genetic Regulatory Networks With Randomly Occurring Uncertainties

Abstract: — This article is concerned with the problem of finite-time H 1 state estimation for switchedgeneticregulatory networks with randomly occurring uncertainties. The persistent dwell-time switching rule, as a more versatile classof switching rules, is considered in this paper. Besides, several random variables that obeythe Bernoulli distribution are used to represent randomly occurring uncertainties. The overriding purpose of this article is to design an estimator to ensure that the estimation errorsystem is stochastically finite-time bounded andsatisfies the H 1 performance. Basedon this, sufficient conditions for theexplicit formof the estimator gainscan be obtained by the Lyapunov method. Finally, a numerical example is givento verify the correctnessandfeasibility of the proposed method.

Disturbance Observer-Based Adaptive Neural Network Output Feedback Control for Uncertain Nonlinear Systems.

Abstract: This article is devoted to the output feedback control of nonlinear system subject to unknown control directions, unknown Bouc-Wen hysteresis and unknown disturbances. During the control design process, the design obstacles caused by unknown control directions and Bouc-Wen hysteresis are eliminated by introducing linear state transformation and a new coordinate transformation, which avoids using the Nussbaum function with high-frequency oscillation to deal with the issue. Besides, to settle the issue caused by the unknown disturbances, a novel nonlinear disturbance observer is designed, which has the characteristics of simple structure, low coupling, and easy implementation. Especially, a compensation item is constructed to offset the redundant items generated in the backstepping design process. Simultaneously, using the neural network and backstepping technology, an output feedback controller is devised. The controller ensures that all closed-loop signals are bounded, and the system output, state observation error, and disturbance observation error converge to a small neighborhood of the origin. Finally, to illustrate the effectiveness of the proposed scheme, simulation verification is carried out based on a numerical example and a Nomoto ship model.

Adaptive NN Fixed-Time Fault-Tolerant Control for Uncertain Stochastic System With Deferred Output Constraint via Self-Triggered Mechanism.

Abstract: For a class of nonstrict-feedback stochastic nonlinear systems with the injection and deception attacks, this article explores the problem of adaptive neural network (NN) fixed-time control ground on the self-triggered mechanism in a pioneering way. After developing the self-triggered mechanism and the delay-error-dependence function, a neural adaptive delay-constrained fault-tolerant controller is proposed by employing the backstepping technique. The self-triggered mechanism does not require an additional observer to determine the time of the data transmission, which reduces the consumption of the system resources more efficiently. In addition, the whole Lyapunov function with the delay-error-dependence term is developed to solve the deferred output constraint problem. Under the proposed controller, it can be proven that all the signals within the closed-loop system are semiglobally uniformly bounded in probability, while the convergence time is independent of the initial state and the deferred output constraint control performance is achieved. The feasibility and the superiority of the proposed control strategy are shown by some simulations.

Dynamic Event-Triggered H∞ Load Frequency Control for Multi-Area Power Systems Subject to Hybrid Cyber Attacks

Abstract: This article aims at designing a dynamic event-triggered $\mathcal {H}_{\infty }$ load frequency controller for multi-area power systems affected by false data-injection attacks and denial-of-service attacks. A dynamic event-triggered scheme, whose threshold parameter varies with objective system states, is employed to make rational use of limited network bandwidth resources and improve the efficiency of the data utilization. Then, taking the impacts of the aforementioned hybrid cyber attacks into consideration, an attractive system model is established. Whereafter, several sufficient conditions, which can guarantee the exponential mean-square stability with a preset $\mathcal {H} _{\infty }$ performance index of the studied system, are obtained through utilizing Lyapunov stability theory. Additionally, the desired controller is designed via handling convex optimization problems. Finally, a simulation example is displayed to explain the validity of the proposed method.

H∞ Load Frequency Control for Power Systems Under Communication Delays: An Event-Triggered Dynamic Output Feedback Scheme

Abstract: In this brief, the problem of the load frequency control for power systems suffered from communication delays is discussed based on a dynamic output feedback scheme. An output-based event detection mechanism, where the error signal is defined as the difference between the latest transmitted data and the average value of the currently sampled data and the latest transmitted data, is introduced to reduce the triggering rate while ensuring the control performance. Combining such an event-triggered control scheme with dynamic output feedback control strategy, the concerned closed-loop system is established. After that, with the aid of Lyapunov-Krasovskii functional and appropriate matrix decoupling technique, some sufficient criteria for the performance analysis and controller design are deduced. Finally, the availability of the developed control strategy is verified through a simulation example with two cases.

H∞ static output feedback control for singularly perturbed persistent dwell‐time switched systems with time‐varying delay and deception attacks

Abstract: This paper studies the static output feedback control issue for discrete‐time singularly perturbed switched time‐delay systems subject to randomly occurring deception attacks, in which the sequence for activated subsystems is governed by the persistent dwell‐time switching strategy. The practical present phenomena that parameters alternately switch in fast or slow frequencies are characterized suitably with such strategy. Meanwhile, the deception attacks are assumed to occur in a random way that obeys the Bernoulli distribution. Moreover, some sufficient conditions are established by means of the Lyapunov stability theory, which guarantee the closed‐loop system is mean‐square exponentially stable with a prescribed H∞$$ {\mathcal{H}}_{\infty } $$ performance. Furthermore, based on the matrix processing technology, the desired controller gains are obtained. Finally, the rationality and effectiveness of the proposed method are verified by a numerical example.

Fuzzy H∞ Control of Discrete-Time Nonlinear Markov Jump Systems via a Novel Hybrid Reinforcement Q -Learning Method.

Abstract: In this article, a novel hybrid reinforcement Q -learning control method is proposed to solve the adaptive fuzzy H∞ control problem of discrete-time nonlinear Markov jump systems based on the Takagi-Sugeno fuzzy model. First, the core problem of adaptive fuzzy H∞ control is converted to solving fuzzy game coupled algebraic Riccati equation, which can hardly be solved by mathematical methods directly. To solve this problem, an offline parallel hybrid learning algorithm is first designed, where system dynamics should be known as a prior. Furthermore, an online parallel Q -learning hybrid learning algorithm is developed. The main characteristics of the proposed online hybrid learning algorithms are threefold: 1) system dynamics are avoided during the learning process; 2) compared with the policy iteration method, the restriction of the initial stable control policy is removed; and 3) compared with the value iteration method, a faster convergence rate can be obtained. Finally, we provide a tunnel diode circuit system model to validate the effectiveness of the present learning algorithm.

Nonfragile Output Feedback Tracking Control for Markov Jump Fuzzy Systems Based on Integral Reinforcement Learning Scheme

Abstract: In this article, a novel integral reinforcement learning (RL)-based nonfragile output feedback tracking control algorithm is proposed for uncertain Markov jump nonlinear systems presented by the Takagi–Sugeno fuzzy model. The problem of nonfragile control is converted into solving the zero-sum games, where the control input and uncertain disturbance input can be regarded as two rival players. Based on the RL architecture, an offline parallel output feedback tracking learning algorithm is first designed to solve fuzzy stochastic coupled algebraic Riccati equations for Markov jump fuzzy systems. Furthermore, to overcome the requirement of a precise system information and transition probability, an online parallel integral RL-based algorithm is designed. Besides, the tracking object is achieved and the stochastically asymptotic stability, and expected $\mathcal {H}_{\infty }$ performance for considered systems is ensured via the Lyapunov stability theory and stochastic analysis method. Furthermore, the effectiveness of the proposed control algorithm is verified by a robot arm system.

Anti-disturbance synchronization of fuzzy genetic regulatory networks with reaction-diffusion

Abstract: This paper intends to focus on the anti-disturbance synchronization issue for genetic regulatory networks subject to reaction-diffusion terms based on the Takagi-Sugeno fuzzy model. In view of the fact that disturbances are widespread in actual control engineering, the stability of the aforementioned systems would be affected, therefore, ensuring the stability of closed-loop genetic regulatory networks is the main goal of this paper. The unknown disturbances are supposed to be generated by an exogenous system, which can be estimated by developing disturbance observers. Furthermore, integrating the disturbance observers with fuzzy rule-based conventional control laws, a new anti-disturbance control strategy is proposed to reject the disturbances and guarantee the desired dynamic performances. Then, by constructing a proper Lyapunov function and using advanced decoupling techniques, some sufficient conditions in the form of linear matrix inequalities , to guarantee the asymptotic stability of the error system, are obtained. Finally, an illustrated example is presented to demonstrate the effectiveness and superiority of the proposed method.

Generalized dissipative state estimation for discrete-time nonhomogeneous semi-Markov jump nonlinear systems

Abstract: The problem of generalized dissipative state estimation for discrete-time nonhomogeneous semi-Markov jump nonlinear systems is concerned in this paper. In this paper, we consider the semi-Markov renewal chain is nonhomogeneous and the states of the system are inaccessible. The aim of this paper is to propose the estimator-designed method to ensure that the system is σ-mean-square stable and satisfy extended dissipative performance. By using the semi-Markov kernel method and polytopic approach, and constructing a new type of Lyapunov function, which not only depends on the sojourn-time but also on the stochastic switching rules, the state estimator gains can be obtained. At last, a numerical example is adopted to verify the superiority of the presented control strategy.

H∞ Bipartite Synchronization Control of Markov Jump Cooperation-Competition Networks With Reaction-Diffusions.

Abstract: This article is concerned with the bipartite synchronization problem of coupled switching neural networks with cooperative-competitive interactions and reaction-diffusion terms. Different from the existing literature, the networked systems under investigation possess the relationship of cooperation and competition among nodes. Notably, the switching topology is described by a signed graph subject to the Markov jump process with the coexistence of positive and negative interaction weights. Specifically, a positive weight indicates an alliance relationship between two nodes and a negative one shows an adversary relationship. This article aims to design a bipartite synchronization controller for the aforementioned networks with the switching topology such that a prescribed H∞ bipartite synchronization is satisfied. Then, some sufficient criteria to ensure the stochastic stability of bipartite synchronization error systems are established in view of an appropriate Lyapunov function. Finally, two simulation examples are presented to verify the validity of the proposed bipartite synchronization control method.

Fault-Tolerant Event-Triggered $\mathcal {H}_{\infty }$ Load Frequency Control for Multiarea Power Systems With Communication Delay

Abstract: This article addresses the $\mathcal {H}_{\infty }$ load frequency control problem for multiarea power systems. In order to reduce the heavy burden of the communication channel and relieve the impact caused by the actuator failures, an event-triggered mechanism and a fault-tolerant strategy are adopted, simultaneously. The main purpose is to design a controller so that the multiarea power systems are asymptotically stable in the case of actuator failures and communication delays. On the basis of the Lyapunov stability theory, some sufficient criteria for ensuring the stability of the synthesized system with an expected $\mathcal {H}_{\infty }$ performance are obtained. Finally, the effectiveness and feasibility of the proposed method are demonstrated via an illustrative example.

H ∞ Synchronization of Fuzzy Neural Networks Based on a Dynamic Event-triggered Sliding Mode Control Method

Abstract: : This paper focuses on the H ∞ synchronization issue for fuzzy neural networks via a dynamic event-triggered sliding mode control scheme. In order to relieve the congestion phenomenon in the communication chan-nel, a dynamic event-triggered mechanism is introduced into the sliding mode control design, in which an internal dynamical variable is adopted to fit the event-triggered condition suitably. Moreover, some results with less conservatism are obtained by considering the asynchronous premise variable problem. Then, sufficient criteria are established through the Lyapunov stability theory, which can guarantee that the sliding mode dynamics is asymptotically stable with a given H ∞ performance. In this case, a dynamic event-triggered sliding mode control law is constructed to drive the trajectories of the fuzzy neural networks onto the designed sliding surface. Finally, the effectiveness and superiority of the presented method is verified by an illustrative example.