Event-triggered robust model predictive control of continuous-time nonlinear systems

The event-triggered control is of compelling features in efficiently exploiting system resources, and thus has found many applications in sensor networks, networked control systems, multi-agent systems and so on. In this paper, we study the event-triggered model predictive control (MPC) problem for continuous-time nonlinear systems subject to bounded disturbances. An event-triggered mechanism is first designed by measuring the error between the system state and its optimal prediction; the event-triggered MPC algorithm that is built upon the triggering mechanism and the dual-mode approach is then designed. The rigorous analysis of the feasibility and stability is conducted, and the sufficient conditions for ensuring the feasibility and stability are developed. We show that the feasibility of the event-triggered MPC algorithm can be guaranteed if, the prediction horizon is designed properly and the disturbances are small enough. Furthermore, it is shown that the stability is related to the prediction horizon, the disturbance bound and the triggering level, and that the state trajectory converges to a robust invariant set under the proposed conditions. Finally, a case study is provided to verify the theoretical results.     

An optimal approach to output‐feedback robust model predictive control of LPV systems with disturbances

An observer‐based output feedback predictive control approach is proposed for linear parameter varying systems with norm‐bounded external disturbances. Sufficient and necessary robust positively invariant set conditions of the state estimation error are developed to determine the minimal ellipsoidal robust positively invariant set and observer gain through offline computation. The quadratic upper bound of state estimation error is updated and included in an ‐type cost function of predictive control to optimize transient output feedback control performance. Recursive feasibility of the dynamic convex optimization problem is guaranteed in the proposed predictive control strategy. With the input‐to‐state stable observer, the closed‐loop control system states are steered into a bounded set. Simulation results are given to demonstrate the effectiveness of the proposed control strategy. Copyright © 2016 John Wiley & Sons, Ltd.     

事件触发双模分布式预测控制

本文针对有界扰动作用下的线性离散大系统,提出了事件触发双模分布式预测控制设计方法.利用输入状态稳定性(input-to-state stability,ISS)理论建立了仅与子系统自身信息相关的事件触发条件.只有子系统满足相应的事件触发条件,才进行状态信息的传输和分布式预测控制优化问题的求解,并与邻域子系统交互最优解作用下的关联信息.当子系统进入不变集时,采用状态反馈控制律进行镇定,并与进入不变集的邻域子系统不再交互信息.分析了算法的递推可行性和系统的闭环稳定性,给出了扰动的上界.最后,通过车辆控制系统对算法进行仿真验证,结果表明,本文提出的方法能够有效降低优化问题的求解次数和关联信息的交互次数,节约计算资源和通信资源.     

可变多胞体结构的鲁棒模型预测控制

针对一类具有输入输出约束的线性变参数系统,提出了一种可变多胞体结构的多面体不变集鲁棒模型预测控制算法,算法分为在线和离线两个部分。离线算法构建系统的未知但有界误差描述模型,根据状态的变化得到可变参数的多胞体结构,并转化为状态空间模型的多胞体形式,然后采用线性矩阵不等式的优化方式得到一系列离线嵌套多面体不变集。在线算法根据状态变量在嵌套多面体不变集中的位置,结合可变多胞体结构,通过线性插值的优化方式得到系统的实际控制律。给出算法的详细步骤和系统闭环稳定性证明。仿真结果验证了该算法的有效性,表明该算法使系统的闭环响应更为快速和稳定。     

Constrained receding horizon predictive control for systems with disturbances

A receding horizon predictive control method for systems with input constraints and disturbances is proposed. A polyhedral feasible set of states which is invariant with respect to a given state feedback control law is derived in the presence of bounded disturbances. The proposed predicted control algorithm deploys a strategy in which the current state is steered into the polyhedral invariant feasible set within a finite number N of feasible control moves, despite the presence of disturbances. The future control moves over the horizon N are represented as the sum of the state feedback control and a perturbation; the perturbation term provides the degrees of freedom with which to enlarge the stabilizable set of initial states. The control algorithm is formulated in linear matrix inequalities so that it can be solved using semidefinite programming.     

Robust output feedback model predictive control of constrained linear systems

This paper provides a solution to the problem of robust output feedback model predictive control of constrained, linear, discrete-time systems in the presence of bounded state and output disturbances. The proposed output feedback controller consists of a simple, stable Luenberger state estimator and a recently developed, robustly stabilizing, tube-based, model predictive controller. The state estimation error is bounded by an invariant set. The tube-based controller ensures that all possible realizations of the state trajectory lie in a simple uncertainty tube the ‘center’ of which is the solution of a nominal (disturbance-free) system and the ‘cross-section’ of which is also invariant. Satisfaction of the state and input constraints for the original system is guaranteed by employing tighter constraint sets for the nominal system. The complexity of the resultant controller is similar to that required for nominal model predictive control.     

Event-triggered adaptive consensus control of nonlinear multi-agent systems with unknown control directions and actuator saturation

This article addresses the event-triggered adaptive consensus control of nonlinear multi-agent systems with unknown control direction and actuator saturation. A new robust adaptive control algorithm based on an event-triggered mechanism is designed. The smooth Lipschitz function approximates the saturated nonlinear function, while the Nussbaum function handles unknown control directions and residual terms. The event-triggered mechanism is designed to determine the time of communication, significantly reducing the communication burden. An additional estimator is utilized to deal with unknown parameters involved in neighbor dynamics and prevent information exchange to consistency errors between connected subsystems. The results show that all the signals of the closed-loop system are uniformly bounded, and the consensus tracking error converges to a bounded set. Meanwhile, Zeno's behavior is eliminated. Simulation results confirm the superiority of the proposed method.     

Observer‐based cooperative distributed fault‐tolerant model predictive control with imperfect network communication and asynchronous measurements

Intermittent actuator and sensor faults tolerant are simultaneously considered in a distributed control system with imperfect communication network. The asynchronous measurements of different output variables in one sampling period are synchronized through a novel two‐stage model‐based projection method. Different from centralized control network, in both layer‐to‐layer and in‐layer communication, the packet delay, loss and disordering are corrected by the predicted data from model predictive control. Moreover, a completely distributed state observer is established for both system states and sensor faults problem with bounded noise uncertainties. For the intermittent actuator faults, actuator plug‐and‐play design methods based on model predictive control has been introduced, making the actuator faults estimation omitted. The distributed stability conditions are derived for the proposed fault‐tolerant controller, and the online feasibility is explained in detail. Numerical simulation is given to verify the design procedure.     

Distributed fuzzy adaptive event-triggered finite-time consensus tracking control for uncertain nonlinear multi-agent systems with asymmetric output constraint

This article investigates the consensus problem for uncertain nonlinear multi-agent systems (MASs) with asymmetric output constraint. Different from BLF-based constraint consensus tracking control, a novel approach based on nonlinear state-dependent function is proposed to solve the asymmetric output constraint, which need not convert output constraint into tracking error bound. First-order sliding mode differentiator is incorporated into each step of backstepping control design to reduce computation burden. Further, in combination of proposed event-triggered mechanism based on time-varying threshold, a distributed fuzzy adaptive event-triggered finite-time consensus method is developed. It can ensure that the consensus tracking error tends to a small neighbor in a finite time and the asymmetric output constraint of each subsystem is not violated. Two simulations are given to demonstrate the effectiveness of control method.     

TCP/AWM网络系统的事件触发预设性能拥塞控制EI北大核心CSCD

研究了具有非响应用户数据报协议流干扰的TCP/AWM网络系统的跟踪控制问题.针对网络拥塞控制系统,给出了一种改进的网络模型.将预设性能控制和事件触发机制相结合,提出了一种新的AWM拥塞控制算法.利用改进的边界性能函数限制TCP/AWM网络系统的队列跟踪误差,提高了闭环系统的暂态和稳态性能,保证了所有闭环信号都是有界的.此外,采用基于一位信号传输的事件触发控制协议,当控制信号更新时,仅通过一个二进制信号(0或1)传输信息.与传统的事件触发控制协议相比,不仅考虑了有限的带宽,而且考虑了网络传输的成本以及安全性问题.最后,通过仿真验证和分析,证明了所提方法的可行性和优越性.     

Resilient distributed model-free adaptive predictive control for multiarea power systems under denial of service attack

This paper proposes a novel load frequency control (LFC) strategy for power systems based on distributed model-free adaptive predictive control. First, a power system dynamic model is established by the input and output signals of the power system. Then, a distributed model-free adaptive predictive control algorithm is established for the power system under denial-of-service attack by the established power system dynamic model, and a predictive compensation algorithm is designed to compensate the impact of DoS attack. Based on the designed distributed model-free adaptive predictive control algorithm, the frequency tracking error of the multiarea power system is bounded. The scheme is independent of the structure of the power system and does not need to measure any state signals of the power system, relying only on the input and output data of the power system. The results of simulations demonstrate the effectiveness and superiority of the design.     

Dynamic Output Feedback Robust MPC With one Free Control Move for LPV Model With Bounded Disturbance

For a linear parameter‐varying (LPV) model which is a convex combination of several linear time invariant sub‐models, this paper considers the case when the combining coefficients are unknown (except being nonnegative and their sum being one). For this model with norm‐bounded unknown disturbance, an output feedback robust model predictive control (MPC) is proposed by parameterizing the infinite horizon control moves and estimated states into one free control move, one free estimated state (i.e., one control move and one estimated state as degrees of freedom for optimization) and a dynamic output feedback law. This is the first endeavour to apply the free control move and free estimated state in the output feedback MPC for this model. The algorithm is shown to be recursively feasible and the system state is guaranteed to converge to the neighborhood of the equilibrium point. A numerical example verifies the effectiveness of the proposed algorithm.     

基于最小衰减率多面体不变集的鲁棒模型预测控制

针对一类具有输入输出约束的多胞体结构线性变参数系统,提出了一种基于最小衰减率多面体不变集的鲁棒模型预测控制算法,算法分为在线和离线两个部分.为增强系统控制效果,提高系统响应速度,离线算法首先采用寻求状态变量的最小衰减率的方法优化出一系列状态变量及相应的状态反馈控制律,然后构建出相应的多面体不变集序列;在线算法根据当前实测状态变量,在多面体不变集序列内确定状态变量所处的最小多面体不变集,通过在线优化得出系统的控制输入.给出了鲁棒模型预测控制算法的详细步骤和系统的闭环稳定性证明.仿真结果验证了本算法的有效性,表明本算法使系统的闭环响应更为快速和稳定.     

切换拓扑下多自主体系统的事件触发一致性控制

本文针对一阶非线性多自主体系统,考察了切换拓扑下的事件触发一致性控制问题.当切换拓扑子图的并图包含有向生成树时,基于一阶保持器提出了一种分布式事件触发一致性算法,用以降低网络的通信负载.运用迭代法和不等式法,得到了多自主体系统达到有界一致性的充分条件.此外,证明了所提事件触发机制不存在Zeno现象,并得到了触发间隔的正下界.最后,给出仿真实例,验证了所提事件触发一致性算法和理论分析结果的有效性.     

On the computation of invariant sets for constrained nonlinear systems: An interval arithmetic approach

This paper deals with the computation of control invariant sets for constrained nonlinear systems. The proposed approach is based on the computation of an inner approximation of the one step set, that is, the set of states that can be steered to a given target set by an admissible control action. Based on this procedure, control invariant sets can be computed by recursion.We present a method for the computation of the one-step set using interval arithmetic. The proposed specialized branch and bound algorithm provides an inner approximation with a given bound of the error; this makes it possible to achieve a trade off between accuracy of the computed set and computational burden. Furthermore an algorithm to approximate the one step set by an inner bounded polyhedron is also presented; this allows us to relax the complexity of the obtained set, and to make easier the recursion and storage of the sets.     

Robust output feedback model predictive control of constrained linear systems: Time varying case

The problem of output feedback model predictive control of discrete time systems in the presence of additive but bounded state and output disturbances is considered. The overall controller consists of two components, a stable state estimator and a tube based, robustly stabilizing model predictive controller. Earlier results are extended by allowing the estimator to be time varying. The proposed robust output feedback controller requires the online solution of a standard quadratic program. The closed loop system renders a specified invariant set robustly exponentially stable.     

Synthesis of Mixed Objective Output Feedback Robust Model Predictive Control

Aiming at the constrained polytopic uncertain system with energy‐bounded disturbance and unmeasurable states, a novel synthesis scheme to design the output feedback robust model predictive control(MPC)is put forward by using mixed H₂/H_∞ design approach. The proposed scheme involves an offline design of a robust state observer using linear matrix inequalities(LMIs)and an online output feedback robust MPC algorithm using the estimated states in which the desired mixed objective robust output feedback controllers are cast into efficiently tractable LMI‐based convex optimization problems. In addition, the closed‐loop stability and the recursive feasibility of the proposed robust MPC are guaranteed through an appropriate reformulation of the estimation error bound (EEB). A numerical example subject to input constraints illustrates the effectiveness of the proposed controller.     

事件触发下多智能体系统一致性的干扰主动控制

本文针对带有外部干扰影响的多智能体系统,研究了基于事件触发机制下的多智能体系统Leader-Following一致性的控制问题.采用干扰观测器来估计系统中存在的干扰,并设计了基于事件触发机制的干扰主动控制方案.运用现代控制理论和矩阵论等工具分析了多智能体协同运动算法得到了多智能体系统在分布式事件触发机制下的一致性收敛条件,并且分析了本文设计的分布式事件触发机制的时间间隔存在正的下界.最后通过计算机仿真,验证了本文所提控制算法的有效性.     

Event-triggered finite-time dynamic output feedback control for switched affine systems with asynchronous switching

This paper investigates the event-triggered finite-time dynamic output feedback control for switched affine systems (SASs) with asynchronous switching. A key point of the study is to set up a dynamic output feedback switched affine controller together with an event-triggered mechanism (ETM). By using an average dwell time (ADT) approach and resorting to a controller-mode-dependent Lyapunov functional, the finite-time stability (FTS) performance is achieve for the closed-loop SASs. The system dynamics under the proposed ETM are fully discussed during the synchronous and asynchronous switching intervals. The existence of the lower bound on interevent intervals is discussed emphatically, which excludes the occurrence of Zeno behavior. Finally, an application example of the DC-DC converter is given to show effectiveness of the acquired control algorithm.