Showing papers by "Hao Shen published in 2023"

Memory-Based Event-Triggered Control for Global Synchronization of Chaotic Lur’e Systems and Its Application

Abstract: In this article, a memory-based event-trigger (MBET) scheme is designed to investigate the global synchronization problem of a class of Lur’e systems. There are two reasons for presenting this issue: 1) most of the existing event-trigger schemes do not take the historical state information into account and, therefore, may have some conservatism in reducing the number of triggering times and 2) the existing Lyapunov functionals cannot be directly utilized in the stability analysis of the resulting closed-loop system in this article. Motivated by the above-mentioned considerations, an MBET scheme, in which a time-memory term and an exponential decaying term are incorporated into the threshold function, is newly designed. It is beneficial to enlarging the intertrigger intervals and, thus, can further reduce the transmission of sampled data packets. Taking the features of MBET into consideration, a novel piecewise but continuous functional is constructed. On this basis, Lyapunov stability theory and some inequality estimation techniques are used to develop three linear matrix inequalities (LMIs)-based synchronization criteria and, meanwhile, a co-design for the control gain and the triggering matrix is carried out. Finally, some comparative analyses are provided to demonstrate the advantages of the MBET through an example of Chua’s circuit. Also, the application to information safety is given to verify the effectiveness of the synchronization results by using a 3-neuron neural network.

Improved Reachable Set Estimation and Aperiodic Sampled-Data for T–S Fuzzy Markovian Jump Systems

Abstract: In this article, the problem of reachable set estimation (RSE) and the design of aperiodic sampled-data controller for Takagi–Sugeno fuzzy Markovian jump systems (T–S FMJSs) with unit-energy bounded disturbance (UEBD) and unit-peak bounded disturbance (UPBD) inputs are taken into consideration. First, sufficient conditions that all states of the T–S FMJSs are encompassed by ellipsoids under zero initial conditions are acquired via constructing a mode-dependent two-sided loop-based Lyapunov function and applying a linear matrix inequality approach. Second, the RSE is taken into account in the design of the state feedback aperiodic sampled-data controller with the aim that the resulting ellipsoid encompasses the reachable set of the closed-loop system. Finally, a nonlinear mass-spring model and a tunnel diode circuit model demonstrate the efficiency of the presented approach. In addition, this method is able to obtain a larger sampled-data period than other literature, thus saving bandwidth and reducing communication resources.

Asynchronous Sampled-Data Controller Design for Switched Markov Jump Systems and Its Applications

Abstract: This article is dedicated to design asynchronous aperiodic sampled-data controller for a class of Markov jump systems with switching transition rate. The sampling controller corresponding switching transition rate matrix mode is not synchronized with the system corresponding switching transition rate matrix mode due to the possible switching of the transfer rate matrix in the sampling interval. With the help of T–S fuzzy models, the state-space representation of the system is turned into a fuzzy Itô stochastic switched Markov jump systems. By constructing novel Lyapunov functionals, mean square exponential stability criteria, and dissipativity criteria are presented. Furthermore, an asynchronous aperiodic sampled-data controller is designed for fuzzy Itô stochastic Markov jump systems. At last, by fuzzing the nonlinear tunnel diode circuit model and nonlinear mass-spring-damper mechanical model, the effectiveness and lower conservativeness of the proposed method are illustrated.

Resilient Sampled-Data Control for Stabilization of T--S Fuzzy Systems via Interval-Dependent Function Method: Handling DoS Attacks

Abstract: This article is concerned with the resilient sampled-data control for stabilization of Tahaki--Sugeno (T--S) fuzzy systems in the presence of denial-of-service (DoS) attacks. To describe the effects of DoS attacks, an appropriate mathematical model is established to characterize the complicated dynamical behaviors of DoS attacks and periodic sampling mechanism. On this basis, a resilient sampled-data control scheme including communication protocols and security controller gains, is proposed to account for the effects of DoS attacks. Meanwhile, a novel index is developed to measure the antiattack ability and security level. By utilizing the information of DoS attacks and resilient sampling intervals, a continuous interval-dependent Lyapunov function is constructed which does not increase at the resilient sampling instants. Then, according to the designed interval-dependent function, several inequalities estimation techniques, convex combination approach, and together with discrete-time Lyapunov theory, two sufficient criteria are derived to guarantee that the closed-loop T--S fuzzy systems are globally asymptotically stable with prespecified security levels subject to DoS attacks. The advantages of designed function are well analyzed in contrast with the previous discontinuous Lyapunov functionals. At last, two simulation examples are given to demonstrate the validity and effectiveness of the obtained results.

Event-Based Distributed Secondary Control for AC Islanded Microgrid With Semi-Markov Switched Topology Under Cyber-Attacks

Abstract: The operation of a microgrid (MG) is easily affected by the environment, which leads to the change of communication topology among distributed generations (DGs). Most of the existing results on secondary control of MG consider that the topology connection is fixed, or just simply switched by arbitrary form. In this article, a more general semi-Markov process is introduced to describe such switching, i.e., the switching of communication topology is regarded as a random change process of system mode under general probability distribution. Then, out of the comprehensive consideration of inevitable network constraints, this article proposes distributed resilient secondary control method for AC islanded MG. In our control strategy, the network security, communication burden, and transmission delay are all considered in the controller design. An event detection mechanism is used as the transmission protocol for information interaction among DGs, reducing the communication burden effectively. And a cyber-attack model is introduced to follow the Bernoulli distribution, and the neighbors' information of each DG may be attacked with possible probability. Based on feedback linearization, the studied secondary control problem is cast into distributed tracking synchronization of first-order multiagent systems. The Lyapunov functional method is used to prove the proposed strategy theoretically, and its effectiveness is verified by a modified IEEE 34 bus test system. The result shows that the proposed method can reduce the communication numbers by over 80% under the premise of realizing frequency restoration and accurate real power sharing of AC islanded MG, and the transmission rate is also reduced by about 40% compared with the existing general method.

Multiple μ‐stability analysis of time‐varying delayed quaternion‐valued neural networks

Abstract: This article addresses the multiple μ$$ \mu $$ ‐stability analysis of n$$ n $$ ‐dimensional quaternion‐valued neural networks (QVNNs) with unbounded time‐varying delays (UTVD) and two general classes of activation functions (AFs). Firstly, the QVNNs are decomposed into four equivalent real‐valued systems, and based on the geometrical configuration of the AFs, the state space ℍn$$ {\mathbb{H}}^n $$ is divided into 34n$$ {3}^{4n} $$ disjoint regions. Considering the properties of AFs, several sufficient conditions are derived to ensure the coexistence of 34n$$ {3}^{4n} $$ equilibria, out of which 24n$$ {2}^{4n} $$ are locally μ$$ \mu $$ ‐stable. Moreover, some sufficient conditions for multiple exponential stability, multiple power stability, and multiple log stability are also derived in this article. Finally, two numerical examples are presented. The first example validates the effectiveness of the proposed theoretical results while the second example illustrates the application of QVNNs on the associative memory, which shows that QVNNs have the ability to reliably retrieve true‐color image patterns.

Event-triggered gain-scheduling dissipative synchronization control for switched neural networks under state-dependent switching

Abstract: The dissipative synchronization problem of delayed Markov jump switched neural networks (MJSNNs) under state-dependent switching by the event-triggered gain-scheduling control scheme is studied in this paper. By the introduction of a Markov jump model, which is used to depict the random variation wherein the connection of MJSNNs, the issues we study can take more generality. Via constructing suitable Lyapunov–Krasovskii functionals (LKFs) and applying some matrix inequality scaling methods, sufficient conditions for dissipative synchronization of delayed MJSNN are established. According to such criteria, the event-triggered gain-scheduling control scheme is adopted to design a controller with less terminal communication costs. Finally, a numerical example is given to demonstrate the effectiveness of the proposed method.

Reinforcement Learning-Based Near Optimization for Continuous-Time Markov Jump Singularly Perturbed Systems

Abstract: The design of a suboptimal controller for continuous-time Markov jump singularly perturbed systems with partially unknown dynamics is studied in this brief. With fast and slow decomposition technique, the original Markov jump singularly perturbed systems are decomposed into fast and slow subsystems as a new attempt. On this basis, an offline parallel Kleinman algorithm and an online parallel integral reinforcement learning algorithm are presented to cope with the different subsystems, respectively. Meanwhile, the controllers obtained by the above two algorithms are used to design the suboptimal controllers for original systems. Furthermore, the suboptimality of the proposed controllers is also discussed. Finally, an example of the electric circuit model is shown to illustrate the applicability of the proposed method.

Leader-following consensus of semi-Markov jump nonlinear multi-agent systems under hybrid cyber-attacks

Abstract: In this paper, the leader-following consensus issue is investigated for a class of nonlinear multi-agent systems with semi-Markov parameters subject to hybrid cyber-attacks. A semi-Markov chain is adopted to describe the variation of switching topologies caused by the complexity of the environment and makes the studied problem more general. Hybrid cyber-attacks consisting of denial-of-service attacks and deception attacks are described with the help of two groups of Bernoulli sequences which are assumed to be independent of each other. On this basis, a sufficient condition for the stability of the consensus error system is established by using linear matrix inequality techniques. Finally, the validity of the results obtained is verified by two numerical examples.

A Decentralized Learning Control Scheme for Constrained Nonlinear Interconnected Systems Based on Dynamic Event-Triggered Mechanism

Abstract: This article presents a decentralized learning control method for a class of partially unknown nonlinear systems with asymmetric control input constraints and mismatched interconnections via a novel dynamic event-triggering condition. By employing an integral reinforcement learning strategy, the system drift dynamics can be avoided in the learning process. Meanwhile, a critic neural network is designed to obtain the approximated value function and tuned by using the gradient descent approach. Furthermore, a novel dynamic event-triggering condition is designed to determine the occurrence of an event by introducing a dynamic variable. By using the Lyapunov theory, all signals in the closed-loop system are proved to be uniformly ultimately bounded. Finally, we present a nonlinear interconnected system and an interconnected power system to verify the effectiveness of the proposed method.

ℋ∞ filtering for discrete‐time hidden singular Markov jump systems subject to partially known probability information under DoS attacks

Abstract: This paper concentrates on the problem of asynchronous ℋ∞$$ {\mathscr{H}}_{\infty } $$ filtering for singular Markov jump systems under DoS attacks, in which the unknown probability information cases are considered. To better describe cyber security problem, DoS attacks are considered as encountered randomly and modeled by a set of stochastic variables obeying Bernoulli distribution. Besides, hidden Markov model with partially known probability information is proposed to handle the cases that the Markov state information of the system is restrictedly accessed, and the transition probability information and observation probability information can not be directly obtained. A set of criteria are derived to analyze the regularity, causality, and stochastic stability for the filtering error system with an ℋ∞$$ {\mathscr{H}}_{\infty } $$ performance index. Furthermore, an asynchronous filter design method is proposed to against DoS attacks. Finally, a numerical example and a practical example modeled by a DC motor are given to demonstrate the effectiveness of the proposed method.

Fuzzy-model-based ℋ∞ filtering for discrete-time singular Markov jump nonlinear systems against hybrid attacks

Abstract: This paper investigates the H∞ filtering problem for a class of discrete-time singular Markov jump nonlinear systems against hybrid attacks via a fuzzy-model-based method, in which the hybrid attacks contain deception attacks and denial of service attacks. Two random variables subject to the Bernoulli distribution are used to describe whether the hybrid attacks are encountered during the measurement output of the original system being transmitted to the filter. By using linear matrix inequality technology and Lyapunov stability theory, some sufficient conditions are given to guarantee the considered systems are stochastically admissible and meet H∞ performance index γ. The design approach of the secure filter and a specific form to acquire the expected secure filter gains are obtained, respectively. Finally, to demonstrate the effectiveness of the proposed approach, both a numerical example and a practical example are provided.

Fuzzy $\mathcal{H}_\infty$ Control of Semi-Markov Jump Singularly Perturbed Nonlinear Systems with Partial Information and Actuator Saturation

Abstract: This paper focuses on the fuzzy control problem for a class of semi-Markov jump singularly perturbed nonlinear systems with actuator saturation. Nonlinearities in the underlying systems are tackled with the Takagi-Sugeno fuzzy model. As distinct from the previous achievements, the case that the semi- Markov kernel with partially known information is taken into account such that the underlying systems can be extended to a wider scope. Given that actuator saturation is ubiquitous in engineering systems, the convex hull method is embraced to address this circumstance. To reduce the conservativeness of the obtained results, on the basis of Lyapunov stability theory and LaSalle's invariance principle, some criteria related to the sojourn time are presented. Next, the mean-square exponential stability of the underlying system is obtained based on the derived results. Finally, further experiment to verify the feasibility of the proposed methodology is given by a tunnel diode circuit model.

$\mathcal {H}_{\infty }$ State Estimation for PDT-Switched Coupled Neural Networks Under Round-Robin Protocol: A Cooperation-Competition-Based Mechanism

Abstract: This paper is concentrated on the $\mathcal {H_{\infty }}$ state estimation problem for switched coupled neural networks based on a Takagi-Sugeno fuzzy model. Notably, the time-variant network topology with alternate fast switchings and slow ones is described suitably by a persistent dwell-time rule, and the interactive dynamics with both cooperative properties and antagonistic ones among nodes are featured comprehensively by the switching signed graph. In view of the communication pressures brought by network-induced problems and the requirements in digital control, the round-robin protocol and logarithmic quantization are flexibly integrated for more transmission efficiency and fewer data collisions. Thereafter, by utilizing a relaxed multiple Lyapunov function method and some novel matrix process techniques, sufficient criteria guaranteeing the exponential stability in a globally uniform sense with a prescribed $\mathcal {H_{\infty }}$ performance level of the estimation error system are established. Finally, the synthesized analysis of the proposed method is presented with an illustrative example.

Reinforcement-Learning-Based Composite Optimal Control for Looper Hydraulic Servo Systems in Hot Strip Rolling

Abstract: The control scheme of the looper angle plays an essential role in hot rolling, which is directly related to the tension maintenance of the strip. The conventional scheme uses a proportion–integration–differentiation (PID) controller to control the servo valve to drive the hydraulic actuator. For different steel types and production temperature changes, the PID control mainly relies on empirical parameter adjustment, which may bring inaccuracy or inefficiency. The objective of this article is to propose a reinforcement-learning-based looper hydraulic servo optimization control scheme to automatically tune the control gain to optimum. First, the modeling error caused by variable parameters and the influence of external disturbance are considered, and the corresponding control model of the looper system is given. Subsequently, a feedforward controller with a radial basis neural-network-based disturbance observer is used to deal with modeling errors and disturbances. A feedback controller with off-policy reinforcement learning is applied in the meantime. The proposed control scheme can realize uniformly ultimately bounded of the system with Lyapunov theory. Simulation results verify the effectiveness of the proposed method.

Mixed H∞ and passive consensus of Markov jump multi-agent systems under DoS attacks with general transition probabilities

Abstract: This paper investigates the leader-following consensus problem of Markov jump multi-agent systems affected by random denial-of-service attacks. A Markov jump process is used to describe the switching topologies caused by attacks, and the general transition probability matrix is employed which is considered as not completely available and contains some errors. Besides, with the help of the Lyapunov stability theory and linear matrix inequality technology, some sufficient conditions are established to make sure that the mixed H∞ and passive consensus can be achieved. Finally, a numerical example is given to illustrate the correctness and effectiveness of the proposed method.

H∞$$ {H}_{\infty } $$ synchronization of Markov jump neural networks with MPDT switching transition probabilities under DoS attacks

Abstract: In this paper, the H ∞ $$ {H}_{\infty } $$ synchronization issue of discrete-time Markov jump neural networks under DoS attacks is studied, in which the transition probability of the Markov chain is considered as time-varying governed by a mode-dependent persistent dwell-time switching rule. The purpose of this paper is to design a suitable controller such that mean-square exponentially stable of the synchronization error system can be accomplished, and an H ∞ $$ {H}_{\infty } $$ performance is satisfied under denial of service (DoS) attacks. In order to reduce the conservatism of obtained results, some synchronization analysis criteria are obtained by adopting an activation function division method. Based on these criteria, an effective security synchronization controller design method is presented. Conclusively, an example is given to verify the validity of the results.

Mode-dependent Scalable Control for Large-Scale Networked Systems

Abstract: A novel mode-dependent scalable control strategy is proposed for large-scale networked systems, which are composed of several dynamically interconnected subsystems. First, to remove the restrictions on the coupling structure of these subsystems, an arbitrary topology is carefully constructed. Then, some sufficient conditions under the subsystem level are derived, which uses each subsystem’s knowledge of its own dynamics and a limited amount of information about the coupled subsystems. And, a set of flexible controller gains are solved, guaranteeing all closed-loop signals to be globally exponentially stable. Finally, simulation results on a coupled microgrid validate the effectiveness of the proposed approach.

Dynamic event-triggered consensus for discrete-time hidden Markov multi-agent systems with partially unknown probabilities

Abstract: This paper addresses the consensus problem of Markov jump multi-agent systems under dynamic event-triggered communication. In order to utilize the limited network resources reasonably and improve the efficiency of data transmission, a dynamic event-triggered method is adopted. Considering the challenge of obtaining system mode information, a hidden Markov model is introduced. On this basis, because of the limitation of mode information acquisition, the case with partially unknown probabilities both exist in the transition probability matrix and the observation probability matrix is discussed, which makes the conclusion realistic. Moreover, a sampled-data consensus protocol is proposed, and based on the Lyapunov stability theory, several sufficient conditions are derived to ensure the consensus of the system under specified [Formula: see text] performance. Finally, a numerical example is given to demonstrate the effectiveness of the proposed protocol.

Sliding-Mode Control for IT2 Fuzzy Nonlinear Singularly Perturbed Systems and Its Application to Electric Circuits: A Dynamic Event-Triggered Mechanism

Abstract: This work is devoted to an $H_{\infty }$ sliding-mode control for nonlinear singularly perturbed system under a network control framework. Aiming at describing the nonlinearities with parameter uncertainties in the studied system, the idea of interval type-2 (IT2) fuzzy method is employed in the system modeling. A disturbance observer is developed to acquire the unknown disturbance, and the estimated value is implemented into the controller for neutralizing the influence of the disturbance. To further reduce the occupation of network resources, an event-triggered communication protocol with a dynamic threshold parameter is adopted, where the triggered condition can be adjusted adaptively as the evolution of the system states. Then, an appropriate fuzzy integral sliding motion is constructed based on the constructed system model and disturbance estimation. It is worth noting that the constructed fuzzy controller follows the idea of nonparallel distribution compensation, which improves the designed flexibility. In terms of fuzzy processing, by introducing the membership functions dependent method into the stability analysis, a series of relaxed criteria are established to ensure the global asymptotic stability for the closed-loop systems with $H_{\infty }$ performance level. Finally, two classical examples of circuit model and inverted pendulum model are extended to IT2 fuzzy idea, showing the rationality of the proposed approach.

A Switching Memory-Based Event-Trigger Scheme for Synchronization of Lur'e Systems With Actuator Saturation: A Hybrid Lyapunov Method.

Abstract: This article is concerned with the event-triggered synchronization of Lur'e systems subject to actuator saturation. Aiming at reducing control costs, a switching-memory-based event-trigger (SMBET) scheme, which allows a switching between the sleeping interval and the memory-based event-trigger (MBET) interval, is first presented. In consideration of the characteristics of SMBET, a piecewise-defined but continuous looped-functional is newly constructed, under which the requirement of positive definiteness and symmetry on some Lyapunov matrices is dropped within the sleeping interval. Then, a hybrid Lyapunov method (HLM), which bridges the gap between the continuous-time Lyapunov theory (CTLT) and the discrete-time Lyapunov theory (DTLT), is used to make the local stability analysis of the closed-loop system. Meanwhile, using a combination of inequality estimation techniques and the generalized sector condition, two sufficient local synchronization criteria and a codesign algorithm for the controller gain and triggering matrix are developed. Furthermore, two optimization strategies are, respectively, put forward to enlarge the estimated domain of attraction (DoA) and the allowable upper bound of sleeping intervals on the premise of ensuring local synchronization. Finally, a three-neuron neural network and the classical Chua's circuit are used to carry out some comparison analyses and to display the advantages of the designed SMBET strategy and the constructed HLM, respectively. Also, an application to image encryption is provided to substantiate the feasibility of the obtained local synchronization results.