Finite-time control of discrete-time semi-Markov jump linear systems: A self-triggered MPC approach

In this paper, a self-triggered model predictive control (MPC) strategy is developed for discrete-time semi-Markov jump linear systems to achieve a desired finite-time performance. To obtain the multi-step predictive states when system mode jumping is subject to the semi-Markov chain, the concept of multi-step semi-Markov kernel is addressed. Meanwhile, a self-triggered scheme is formulated to predict sampling instants automatically and to reduce the computational burden of the on-line solving of MPC. Furthermore, the co-design of the self-triggered scheme and the MPC approach is adjusted to design the control input when keeping the state trajectories within a pre-specified bound over a given time interval. Finally, a numerical example and a population ecological system are introduced to evaluate the effectiveness of the proposed control.     

Robust cooperative output regulation of heterogeneous uncertain linear multi-agent systems by intermittent communication

In this paper, we study the robust cooperative output regulation problem of heterogeneous linear multi-agent systems with system uncertainties and directed communication topology. A robust distributed event-triggered control scheme is proposed based on the internal model principle. To avoid continuous monitoring of measurement errors for the event-triggering condition, a novel self-triggered control scheme is further proposed. Moreover, by introducing a fixed timer in the triggering mechanisms, Zeno behavior can be excluded for each agent. An example is finally provided to demonstrate the effectiveness of the proposed self-triggered control scheme.     

Resilient control co-design for cyber-Physical systems with DoS attacks via a successive convex optimization approach

This paper addresses the issue of resilient control in the presence of denial-of-service (DoS) attacks for a class of cyber-physical systems. The primary objective is to design a static output feedback controller and event-triggered condition simultaneously such that the globally exponential stability of the closed-loop system is ensured. Compared with stepwise techniques, the co-design achieves the trade-off between control performance and communication cost. The control co-design process is formulated as a bilinear matrix inequality (BMI) problem, which involves nonlinear terms. A successive convex optimization approach is proposed to solve the BMI problem. Further, we develop a self-triggered communication scheme to reduce the cost caused by continuous event detection. It is shown that the proposed event/self-triggered strategy is Zeno-free and excludes singular triggering. Finally, a numerical example is presented to demonstrate the validity of the proposed method.     

Frequency synchronization adaptive control for power grids under topological uncertainty

In this paper, an adaptive control strategy is proposed to address the synchronization issue for weakly damped generators under topological uncertainty. A singular perturbation analysis is then adopted for strongly damped generators and a compensation control scheme is subsequently given to maintain synchronization under topological changes. Theoretical proof is laid out for the validity of the proposed control scheme. Besides, a power sharing strategy is supplemented for strongly damped generators based on the designed controller. Finally, simulation studies are carried out to verify the effectiveness of the control strategies. Results show that synchronization can be swiftly restored even when the power grid suffers a fatal topological change. The power sharing property can be achieved under the given restriction with the proposed controller.     

Integral predictor based prescribed performance control for multi-motor driving servo systems

An integral predictor-based dynamic surface control scheme is developed with prescribed performance (IPPDSC) for multi-motor driving servo systems in this paper. By employing a novel finite-time performance function and an improved error transformation, the tracking error is limited within a prescribed zone in any preset time without having the overrun and the singularity problem. Furthermore, integral state predictors are designed to update neural network weights to handle high-frequency oscillations under large adaptive gains. Different from the existing approaches, an integral term of prediction error is introduced to eliminate the steady-state error and avoid chattering. In addition, a synchronization controller based on the mean relative coupling structure is proposed to solve the coupling problem between synchronization and tracking. Finally, simulation and experimental results are presented to demonstrate the effectiveness of the designed approach.     

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

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.     

Synchronization of linear dynamical networks under stochastic impulsive coupling protocols

This paper investigates a stochastic impulsive coupling protocol for synchronization of linear dynamical networks based on discrete-time sampled-data. The convergence of the networks under the proposed protocol is discussed, and some sufficient conditions are showed to guarantee almost sure exponential synchronization. Moreover, this coupling protocol with a pinning control scheme is developed to lead the state of all nodes to almost sure exponentially converge to a virtual synchronization target. It is shown that the almost sure exponential synchronization can be achieved by only interacting based on the stochastic feedback information at discrete-time instants. Some numerical examples are finally provided to present the effectiveness of the proposed stochastic coupling protocols.     

Self-triggered MPC for trajectory tracking of unicycle-type robots with external disturbance

In this paper, a self-triggered model predictive controller (MPC) strategy for nonholonomic vehicle with coupled input constraint and bounded disturbances is presented. First, a self-triggered mechanism is designed to reduce the computation load of MPC based on a Lyapunov function. Second, by designing a robust terminal region and proper parameters, recursive feasibility of the optimization problem is guaranteed and stability of the the closed-loop system is ensured. Simulation results show the effectiveness of proposed algorithm.     

Proportional-integral control for synchronization of complex dynamical networks under dynamic event-triggered mechanism

In this paper, the exponential synchronization problem is investigated for a class of continuous-time complex dynamical networks (CDNs) with proportional-integral control strategy and dynamic event-triggered mechanism (DETM). To reduce communication overhead, a novel DETM is proposed to decide whether a certain control signal generated by proportional-integral controller should be transmitted or not. The dynamics of each network node is analyzed in conjunction with the proposed proportional-integral strategy under the DETM, and then a sufficient condition for achieving exponential synchronization of CDNs is provided. The validity of the DETM is further verified by the exclusion of the Zeno behavior. The gain matrices of the controller and the parameters of the DETM are jointly designed. The effectiveness of the proportional-integral control strategy under the DETM is demonstrated by a numerical example.     

Event-based discrete-time multi-agent consensus over signed digraphs with communication delays

The ability to ensure the desired performance of the cooperative-antagonistic multi-agent networks (MANs) in the presence of communication constraints is an important task in many applications of real systems. In this paper, under the proposed event-triggered condition (ETC), different types of consensus are obtained under different network topology. We concentrates on the event-based bipartite consensus. It is shown that under the proposed ETC (i) the addressed cooperative-antagonistic network with arbitrary communication delays reaches bipartite consensus provided that the network is balanced; (ii) the network model reaches zero if the network is unbalanced. Further, to avoid the continuously verifying the triggering condition, a self-triggered algorithm is proposed for realizing the bipartite consensus of the network model. A numerical example is given to illustrate the effectiveness of the theoretical results.     

Finite-time synchronization of delayed complex dynamic networks via aperiodically intermittent control

In this paper, the finite-time synchronization problem of complex dynamic networks with time delay is studied via aperiodically intermittent control. By compared with the existed results concerning aperiodically intermittent control, some new results are obtained to guarantee the synchronization of networks in a finite time. Especially, a new lemma is proposed to reduce the convergence time. In addition, based on aperiodically intermittent control scheme, the essential condition ensuring finite-time synchronization of dynamic networks is also obtained, and the convergence time is closely related to the topological structure of networks and the maximum ratio of the rest width to the aperiodic time span. Finally, a numerical example is provided to verify the validness of the proposed theoretical results.     

Scaled consensus of second-order multi-agent systems based on edge-event driven control

In this paper, the scaled consensus of resource-limited multi-agent systems with second-order integrator dynamics and undirected topologies is investigated. In order to reduce bandwidth and computation requirements, a scaled consensus protocol based on periodic edge-event driven control is proposed. It is proven that all the agents could converge to a scaled consensus state while the interaction topology is connected. Moreover, a self-triggered scheme is proposed so as to further reduce communication times between agents. Notably, the event-detecting period is introduced so that Zeno behavior could be excluded in our model. Finally, simulations are given to demonstrate the effectiveness of our theoretical results.     

Event-triggered synchronization of delayed neural networks with actuator saturation using quantized measurements

This paper studies event-triggered synchronization control problem for delayed neural networks with quantization and actuator saturation. Firstly, in order to reduce the load of network meanwhile retain required performance of system, the event-triggered scheme is adopted to determine if the sampled signal will be transmitted to the quantizer. Secondly, a synchronization error model is constructed to describe the master-slave synchronization system with event-triggered scheme, quantization and input saturation in a unified framework. Thirdly, on the basis of Lyapunov–Krasovskii functional, sufficient conditions for stabilization are derived which can ensure synchronization of the master system and slave system; particularly, a co-designed parameters of controller and the corresponding event-triggered parameters are obtained under the above stability condition. Lastly, two numerical examples are employed to illustrate the effectiveness of the proposed approach.     

Design of a model-free adaptive sliding mode control to synchronize chaotic fractional-order systems with input saturation: An application in secure communications

In this work, a model-free adaptive sliding mode control (ASMC) methodology is proposed for synchronization of chaotic fractional-order systems (FOSs) with input saturation. Based on the frequency distributed model and the non-integer version of the Lyapunov stability theorem, a model-free ASMC method is designed to overcome the chaotic behavior of the FOSs. The control inputs are free from the nonlinear-linear dynamical terms of the system because of utilizing the boundedness feature of the states of chaotic FOSs. Moreover, a new medical image encryption scheme is tentatively proposed according to our synchronization method, and its effectiveness is verified by numerical simulations. Furthermore, the performance and security analyses are given to confirm the superiority of the proposed encryption scheme, including statistical analysis, key space analysis, differential attack analysis, and time performance analysis.     

Event-triggered and quantized self-triggered control for multi-agent systems based on relative state measurements

This paper studies the consensus problem of multi-agent systems by event- and self-triggered control. An event-triggered algorithm with periodic event detection and relative state measurements is designed to determine all event times. Furthermore, a self-triggered control algorithm with periodic event detection and quantization is proposed to further reduce resource consumption. In the proposed control strategies, only relative state information, or called edge information, is utilized by controllers, and all edge state information incident to each agent is processed together. The results are illustrated by two numerical experiments.     

Adaptive single input sliding mode control for hybrid-synchronization of uncertain hyperchaotic Lu systems

This paper addresses the problem of hybrid synchronization for hyperchaotic Lu systems without and with uncertain parameters via a single input sliding mode controller (SMC). Based on the SMC approach, the proposed controller not only minimizes the influence of uncertainty but also enhances the robustness of the system. The uncertain parameters are estimated by using new adaptation laws which ensure the uncertain parameters convergence to their original value. A hybrid synchronization scheme is useful to maintain the vastly secured and secrecy in the area of secure communication by using the control theory approach. The proposed hybrid synchronization results are providing a superiority of forming a chaotic secure communication scheme. Finally, a numerical example is provided to demonstrate the validity of the theoretical analysis.     

Design of adaptive sliding mode tracking controllers for chaotic synchronization and application to secure communications

Synchronization of two identical chaotic systems with matched and mismatched perturbations by utilizing adaptive sliding mode control (ASMC) technique is presented in this paper. The sliding surface function is specially designed based on the Lyapunov stability theorem and linear matrix inequality (LMI) optimization technique. The designed tracking controller can not only suppress the mismatched perturbations when the controlled dynamics (master–slave) are in the sliding mode, but also drive the trajectories of synchronization errors into a small bounded region whose size can be adjusted through the designed parameters. Adaptive mechanisms are employed in the proposed control scheme for adapting the unknown upper bounds of the perturbations, and the stability of overall controlled synchronization systems is guaranteed. The comparison of the proposed chaotic synchronization technique with an existing generalized chaotic synchronization (GCS) method as well as application of the proposed control method to secure communications is also demonstrated in this paper.     

Synchronization for chaotic systems via mixed-objective dynamic output feedback robust model predictive control

This paper focuses on mixed-objective dynamic output feedback robust model predictive control (OFRMPC) for the synchronization of two identical discrete-time chaotic systems with polytopic uncertainties, energy bounded disturbances, and input constraint. Using active control strategy, the chaos synchronization is transformed into standard dynamic OFRMPC scenarios tractable through receding horizon min–max optimization. Utilizing the notion of quadratic boundedness, the augmented closed-loop stability is further characterized. Then, the concepts of mixed performance criteria are firstly incorporated into the dynamic OFRMPC scheme to guarantee both the robust stability and the disturbance attenuation ability while preserving better dynamical behaviors. Necessary and/or sufficient conditions for desired mixed-objective dynamic OFRMPC are formulated involving linear matrix inequalities (LMIs). Finally, two numerical examples are given to demonstrate the theoretical results.     

科技意识的文化特征和功能

科技意识乃是社会群体或个体对科学技术的思想认识和觉悟程度。这是社会思想意识体系的重要组成部分,广义文化体系的核心因素之一。科学意识就是科技存在的客观反映,——客观性;科技意识更是科技发展的能动反映,——能动性。马克思主义经典作家关于科学技术的精辟论述,这是当代先进科技意识的指导思想和理论基础。科技意识是解放科技生产力的文化保障。它作为科技活动参与者的主导素质而发挥重要的社会功能。当代中国的社会主义现代化建设,正在全面刷新和迅速强化整个社会的科技意识。     

Synchronization of chaotic systems with unknown parameters using adaptive passivity-based control

This paper investigates the problem of complete synchronization of chaotic systems with unknown parameters. An adaptive control scheme based on a feedback passivity approach is proposed. The convergence of the synchronization error is guaranteed. The unified chaotic and hyperchaotic Lü systems are taken as illustrative examples. The feasibility and effectiveness of the proposed scheme are demonstrated through numerical simulations.