Cloud Control Systems

The concept of cloud control systems is discussed in this paper, which is an extension of networked control systems (NCSs). With the development of internet of things (IOT), the technology of NCSs has played a key role in IOT. At the same time, cloud computing is developed rapidly, which provides a perfect platform for big data processing, controller design and performance assessment. The research on cloud control systems will give new contribution to the control theory and applications in the near future.      

不确定切换系统的可靠控制

研究一类结构参数不确定性的切换系统的可靠控制问题.这种可靠系统可以抵御执行器出现故障给系统带来的影响.利用凸组合技术,设计可靠控制器和相应的切换策略,使得不仅当执行器正常时而且当一些执行器出现故障时仍保证闭环系统是全局渐近稳定的,最后通过仿真算例验证所设计方法的可行性、有效性.     

Neural-Network-Based Control for Discrete-Time Nonlinear Systems with Input Saturation Under Stochastic Communication Protocol

In this paper,an adaptive dynamic programming(ADP)strategy is investigated for discrete-time nonlinear systems with unknown nonlinear dynamics subject to input saturation.To save the communication resources between the controller and the actuators,stochastic communication protocols(SCPs)are adopted to schedule the control signal,and therefore the closed-loop system is essentially a protocol-induced switching system.A neural network(NN)-based identifier with a robust term is exploited for approximating the unknown nonlinear system,and a set of switch-based updating rules with an additional tunable parameter of NN weights are developed with the help of the gradient descent.By virtue of a novel Lyapunov function,a sufficient condition is proposed to achieve the stability of both system identification errors and the update dynamics of NN weights.Then,a value iterative ADP algorithm in an offline way is proposed to solve the optimal control of protocol-induced switching systems with saturation constraints,and the convergence is profoundly discussed in light of mathematical induction.Furthermore,an actor-critic NN scheme is developed to approximate the control law and the proposed performance index function in the framework of ADP,and the stability of the closed-loop system is analyzed in view of the Lyapunov theory.Finally,the numerical simulation results are presented to demonstrate the effectiveness of the proposed control scheme.     

Optimal Fixed-Point Tracking Control for Discrete-Time Nonlinear Systems via ADP

Based on adaptive dynamic programming (ADP), the fixed-point tracking control problem is solved by a value iteration (Ⅵ) algorithm. First, a class of discrete-time (DT) nonlinear system with disturbance is considered. Second, the convergence of a Ⅵ algorithm is given. It is proven that the iterative cost function precisely converges to the optimal value, and the control input and disturbance input also converges to the optimal values. Third, a novel analysis pertaining to the range of the discount factor is presented, where the cost function serves as a Lyapunov function. Finally, neural networks (NNs) are employed to approximate the cost function, the control law, and the disturbance law. Simulation examples are given to illustrate the effective performance of the proposed method.      

Decentralized Event-Triggered Average Consensus for Multi-Agent Systems in CPSs with Communication Constraints

The paper investigates decentralized event-triggered average consensus problem for multi-agent systems in cyberphysical systems (CPSs) with communication constraints. To reduce communication burden and improve the communication efficiency of multi-agent systems in CPSs, event-trigger is distributed at subsystem/agent level. A multi-agent system is then modeled as a reduced dimension hybrid system by taking into account decentralized event-triggered mechanism, communication delays and data dropouts within one framework. Some sufficient conditions for average consensus of each agent and an upper bound of communication delay and maximal allowable number of successive data dropouts (MANSD) are obtained, which can conveniently provide the relationship between the triggering parameters, communication constraints and the system stability. Specially, the quantitative relationship between the triggering parameters, MANSD and the system stability is derived. Finally, simulation results are given to illustrate the effectiveness of the proposed method.      

具有间歇性执行器故障的非线性系统自适应CFB控制

控制系统的执行器经常发生各种未知的间歇性故障. 如何有效地处理这些故障对系统的影响是一个难题. 针对一类不确定严格反馈非线性系统, 提出一种自适应CFB (Command filtered backstepping) 控制方案解决了间歇性执行器故障的补偿问题. 利用神经网络逼近控制器中的未知函数, 并采用投影算子实时在线更新控制器中的估计参数使得参数估计值随着故障次数的累积而不断增加的问题被消除. 提出改进的Lyapunov函数证明了所提出的方案能够保证所有闭环信号的有界性, 同时建立了跟踪误差与Lyapunov函数跳变幅度, 最小故障时间间隔, 设计参数之间的关系. 如果Lyapunov函数的跳变幅度越小以及两个连续故障之间的时间间隔越长, 系统的稳态跟踪指标越好. 通过迭代计算建立了暂态跟踪误差指标的均方根型界. 该界表明了通过选择恰当的设计参数, 可改善系统的暂态指标. 仿真结果表明了所提方案的有效性.     

信息物理融合系统网络安全综述

信息物理融合系统（cyber-physical systems， CPS）是集计算、通信和控制于一体的智能系统，实现网络和物理的深度协作和有机融合.目前CPS在关键的基础设施、政府机构等领域发挥着越来越重要的作用.由于物理限制，计算机和网络产生的安全漏洞会导致CPS遭受巨大的破坏，同时还会引起经济损失、社会动乱等连锁反应，所以研究CPS的安全问题对于确保系统安全运行具有重要意义.本文结合国内外的研究现状，概述了CPS安全控制和攻击检测的最新进展.首先本文总结了CPS典型的系统建模以满足对系统性能分析的需要.然后介绍了3种典型的网络攻击，即拒绝服务攻击、重放攻击和欺骗攻击.根据检测方法的类别，对CPS攻击检测的发展进行的概述.此外还讨论了系统的安全控制和状态估计.最后总结和展望了CPS网络安全面临的挑战和未来的研究方向.     

Data-based Fault Tolerant Control for Affine Nonlinear Systems Through Particle Swarm Optimized Neural Networks

In this paper, a data-based fault tolerant control (FTC) scheme is investigated for unknown continuous-time (CT) affine nonlinear systems with actuator faults. First, a neural network (NN) identifier based on particle swarm optimization (PSO) is constructed to model the unknown system dynamics. By utilizing the estimated system states, the particle swarm optimized critic neural network (PSOCNN) is employed to solve the Hamilton-Jacobi-Bellman equation (HJBE) more efficiently. Then, a data-based FTC scheme, which consists of the NN identifier and the fault compensator, is proposed to achieve actuator fault tolerance. The stability of the closed-loop system under actuator faults is guaranteed by the Lyapunov stability theorem. Finally, simulations are provided to demonstrate the effectiveness of the developed method.      

Optimal Control for a Class of Complex Singular System Based on Adaptive Dynamic Programming

This paper presents a new design approach to achieve decentralized optimal control of high-dimension complex singular systems with dynamic uncertainties. Based on robust adaptive dynamic programming (robust ADP) method, controllers for solving the singular systems optimal control problem are designed. The proposed algorithm can work well when the system model is not exactly known but the input and output data can be measured. The policy iteration of each controller only uses their own states and input information for learning, and do not need to know the whole system dynamics. Simulation results on the New England 10-machine 39-bus test system show the effectiveness of the designed controller.      

Deterministic Learning-Based Neural PID Control for Nonlinear Robotic Systems

Traditional proportional-integral-derivative (PID) controllers have achieved widespread success in industrial applications. However, the nonlinearity and uncertainty of practical systems cannot be ignored, even though most of the existing research on PID controllers is focused on linear systems. Therefore, developing a PID controller with learning ability is of great significance for complex nonlinear systems. This article proposes a deterministic learning-based advanced PID controller for robot manipulator systems with uncertainties. The introduction of neural networks (NNs) overcomes the upper limit of the traditional PID feedback mechanism’s capability. The proposed control scheme not only guarantees system stability and tracking error convergence but also provides a simple way to choose the three parameters of PID by setting the proportional coefficients. Under the partial persistent excitation (PE) condition, the closed-loop system unknown dynamics of robot manipulator systems are accurately approximated by NNs. Based on the acquired knowledge from the stable control process, a learning PID controller is developed to further improve overall control performance, while overcoming the problem of repeated online weight updates. Simulation studies and physical experiments demonstrate the validity and practicality of the proposed strategy discussed in this article.

     

基于数据ADP算法的一类带有执行器饱和的未知离散时间系统最优跟踪控制

针对一类带有执行器饱和的未知动态离散时间非线性系统, 提出了一种新的最优跟踪控制方案. 该方案基于迭代自适应动态规划算法, 为了实现最优控制, 首先建立了未知系统动态的数据辨识器. 通过引入M网络, 获得了稳态控制的精确表达式. 为了消除执行器饱和的影响, 提出了一个非二次的性能指标函数. 然后提出了一种迭代自适应动态规划算法获得最优跟踪控制的解, 并给出了收敛性分析. 为了实现最优控制方案, 神经网络被用来构建数据辨识器、计算性能指标函数、近似最优控制策略和求解稳态控制. 仿真结果验证了本文所提出的最优跟踪控制方法的有效性.     

基于自适应动态规划的一类带有时滞的离散时间非线性系统的最优控制策略

针对一类状态和控制变量均带有时滞的非线性系统的带有二次性能指标函数最优控制问题, 本文提出了一种基于新的迭代自适应动态规划算法的最优控制方案. 通过引进时滞矩阵函数, 应用动态规划理论, 本文获得了最优控制的显式表达式, 然后通过自适应评判技术获得最优控制量. 本文给出了收敛性证明以保证性能指标函数收敛到最优. 为了实现所提出的算法, 本文采用神经网络近似性能指标函数、计算最优控制策略、求解时滞矩阵函数、以及给非线性系统建模. 最后本文给出了两个仿真例子说明所提出的最优策略的有效性.     

Reliable State Feedback Control Synthesis For Uncertain Linear Systems

This paper is concerned with the problem of designing reliable state‐ feedback control for a class of uncertain linear systems with norm bounded uncertainty. A procedure for designing reliable state‐feedback control is presented for the case of actuator faults that can be modeled by a scaling factor. In the design, the performance of the normal system (without fault) is optimized, as the considered system operates under the normal condition most of the time. In addition, when actuator faults occur, the closed‐loop system retains robust stability and satisfies a known quadratic performance bound. A numerical example is provided to illustrate the effectiveness of the proposed design method.     

Observer‐Based Reliable Control for Discrete‐Time Switched Linear Systems with Faulty Actuators

This paper deals with the issue of reliable control for discrete‐time switched linear systems with faulty actuators by utilizing a multiple Lyapunov functions method and estimate state‐dependent switching technique. A solvability condition for the reliable control problem is given in terms of matrix inequality with an extra matrix variable. This condition allows the reliable control problem for each individual subsystem to be unsolvable. For each subsystem of such a switched system, we design an observer and an observer‐based controller. A switching rule depending on the observer state is designed which, together with the controllers, can guarantee the stability of the closed‐loop switched system for all admissible actuator failures. The observers, controllers, and switching law are explicitly computed by solving linear matrix inequalities (LMIs). The proposed design method is illustrated by two numerical examples.     

Adaptive Fault-Delay Accommodation for a Class of State-Delay Systems With Actuator Faults

Fault and delay accommodating simultaneously for a class of linear systems subject to state delays, actuator faults and disturbances is investigated in this work. A matrix norm minimization technique is applied to minimize the norms of coefficient matrix on time delay terms of the system in consideration. Compared with the matrix inequality scaling technique, the minimization technique can relax substantially the obtained stability conditions for state delay systems, especially, when the coefficient matrices of time delay terms have a large order of magnitudes.An output feedback adaptive fault-delay tolerant controller(AFDTC) is designed subsequently to stabilize the plant with state delays and actuator faults. Compared with the conventional fault tolerant controller(FTC), the designed output feedback AFDTC can be updated on-line to compensate the effect of both faults and delays on systems. Simulation results under two numerical examples exhibit the effectiveness and merits of the proposed method.     

Discounted Iterative Adaptive Critic Designs With Novel Stability Analysis for Tracking Control

The core task of tracking control is to make the controlled plant track a desired trajectory. The traditional performance index used in previous studies cannot eliminate completely the tracking error as the number of time steps increases. In this paper, a new cost function is introduced to develop the value-iteration-based adaptive critic framework to solve the tracking control problem. Unlike the regulator problem,the iterative value function of tracking control problem cannot be regarded as a ...     

信息-物理融合系统若干关键问题综述

信息-物理融合系统(Cyber-Physical System,CPS)集成了计算系统与物理系统,并通过嵌入式计算机与网络实现了两者之间的协作和融合,将对人们的生产和生活方式产生重要影响.CPS是一个全新的研究领域,利用现有基础理论和技术设计CPS时面临着众多问题.介绍了CPS的概念、特点和体系结构,分析了与嵌入式系统、网络的关联,从计算系统、网络系统和控制系统3个方面概括了CPS设计面临的主要挑战,并着重探讨了当前一些可用于CPS设计的理论和技术以及CPS研究的最新进展,指出CPS当前的发展应以解决系统抽象层次设计、系统建模、体系结构设计、数据传输和管理、子系统集成方面的问题作为其下一步发展主要的研究方向,并提出了一些可行的解决办法,可为相关研究提供参考.     

具有混合故障模型的时滞系统的可靠控制

针对线性定常时滞系统,基于Lyapunov稳定性理论,讨论考虑执行器具有混合故障的系统存在可靠控制器的充分条件,并结合时滞系统相关处理方法和线性矩阵不等式完成状态反馈可靠控制器的设计.数值仿真验证了本文控制器设计方法的可行性.     

Secure Synchronization Control for a Class of Cyber-Physical Systems With Unknown Dynamics

This paper investigates the secure synchronization control problem for a class of cyber-physical systems (CPSs) with unknown system matrices and intermittent denial-of-service (DoS) attacks. For the attack free case, an optimal control law consisting of a feedback control and a compensated feedforward control is proposed to achieve the synchronization, and the feedback control gain matrix is learned by iteratively solving an algebraic Riccati equation (ARE). For considering the attack cases, it is difficult to perform the stability analysis of the synchronization errors by using the existing Lyapunov function method due to the presence of unknown system matrices. In order to overcome this difficulty, a matrix polynomial replacement method is given and it is shown that, the proposed optimal control law can still guarantee the asymptotical convergence of synchronization errors if two inequality conditions related with the DoS attacks hold. Finally, two examples are given to illustrate the effectiveness of the proposed approaches.      

一类带有时延的非线性网络控制系统可靠模糊控制

研究了带有状态时延及执行器故障的非线性网络控制系统的可靠模糊控制问题. 利用输入时延方法, 将带有网络诱导时延和数据包丢失的非线性网络控制系统等价的转化为具有时变时延的Takagi-Sugeno(T-S)模糊系统. 时延对象的状态信息, 采用时滞分解方法, 得以充分的考虑. 并利用锥补线性化迭代算法, 将非凸的稳定性条件转化成可行的线性矩阵不等式(LMI)的形式. 文中将更紧的界处理方法(相互凸组合技术)与不相关增广矩阵项引入到Lyapunov函数的处理当中, 获得保守性更小的稳定性条件. 数值算例验证了该方法的有效性.