未知非线性零和博弈最优跟踪的事件触发控制设计

设计了一种基于事件的迭代自适应评判算法, 用于解决一类非仿射系统的零和博弈最优跟踪控制问题. 通过数值求解方法得到参考轨迹的稳定控制, 进而将未知非线性系统的零和博弈最优跟踪控制问题转化为误差系统的最优调节问题. 为了保证闭环系统在具有良好控制性能的基础上有效地提高资源利用率, 引入一个合适的事件触发条件来获得阶段性更新的跟踪策略对. 然后, 根据设计的触发条件, 采用Lyapunov方法证明误差系统的渐近稳定性. 接着, 通过构建四个神经网络, 来促进所提算法的实现. 为了提高目标轨迹对应稳定控制的精度, 采用模型网络直接逼近未知系统函数而不是误差动态系统. 构建评判网络、执行网络和扰动网络用于近似迭代代价函数和迭代跟踪策略对. 最后, 通过两个仿真实例, 验证该控制方法的可行性和有效性.     

带状态约束的事件触发积分强化学习控制

为克服全状态对称约束以及控制策略频繁更新的局限,同时使得无限时间的代价函数最优,针对一类具有部分动力学未知的仿射非线性连续系统,提出一种带状态约束的事件触发积分强化学习的控制器设计方法。该方法是一种基于数据的在线策略迭代方法。引入系统转换将带有全状态约束的系统转化为不含约束的系统。基于事件触发机制以及积分强化学习算法,通过交替执行系统转换、策略评估、策略改进,最终系统在满足全状态约束的情况下,代价函数以及控制策略将分别收敛于最优值,并能降低控制策略的更新频率。此外,通过构建李亚普诺夫函数对系统以及评论神经网络权重误差的稳定性进行严格的分析。单连杆机械臂的仿真实验也进一步说明算法的可行性。     

双事件触发下非匹配干扰系统积分滑模抗干扰跟踪控制

对于带有不匹配干扰的动态系统,设计具有高资源利用率的抗干扰控制算法具有重要意义.本文在双事件触发机制框架下,研究了非匹配干扰系统的积分滑模抗干扰控制以及动态性能分析问题.首先,基于增广模型构建干扰观测器,实现对未知不匹配干扰的动态估计.为了降低数据冗余并保证传输的同步性,以反馈状态及干扰估计的单触发条件为基础,本文构建了“先到同触发”的双事件触发理论框架.在此框架下,设计积分滑模面和对应的双触发抗干扰控制器,保证被控系统的状态收敛到滑模面.基于Lyapunov稳定性分析方法,计算控制器和观测器增益,保证增广闭环系统具有良好的稳定性和动态跟踪性能.进一步分析了由触发引起的Zeno现象将不会发生.最后,基于典型的A4D模型进行仿真验证,仿真结果表明本文所提的方法具有良好的抗干扰性能.     

基于事件触发和状态翻转的布尔控制网络输出跟踪

利用矩阵半张量积研究事件触发和翻转控制共同作用下布尔控制网络的输出跟踪问题.首先,基于布尔控制网络代数状态空间表示,构造增广系统将输出跟踪问题转化为状态集镇定问题;其次,得到布尔控制网络在两种控制下输出跟踪问题有解的充要条件,并在满足该条件时提出一种基于最小翻转节点集时间最优控制设计方法,进一步给出有限时间内寻找翻转节点集的计算过程;最后,给出一个算例说明结果的可行性.     

具有随机网络攻击的切换信息物理系统的事件触发控制（英文）

本文研究了随机网络攻击下切换信息物理系统的事件触发控制问题.将信息物理系统描述为一种切换线性系统形式.引入事件触发机制来节省系统资源和减轻网络负载,当误差超过给定阈值时传感器中的采样数据才通过通信网络传输到控制器中.考虑在传感器与控制器的通信网络中受到两种不同特征的随机网络攻击.在网络攻击和所设计的事件触发控制器下,建立了切换随机信息物理系统模型.利用模态依赖平均驻留时间方法构建了相应的切换信号.在设计的事件触发控制器和模态依赖平均驻留时间切换信号下实现了系统的均方指数稳定性,并给出了控制器增益.最后,通过实例验证了所得理论结果的有效性.     

不对称约束多人非零和博弈的自适应评判控制

本文针对连续时间非线性系统的不对称约束多人非零和博弈问题, 建立了一种基于神经网络的自适应评判控制方法. 首先, 本文提出了一种新颖的非二次型函数来处理不对称约束问题, 并且推导出最优控制律和耦合Hamilton-Jacobi方程. 值得注意的是, 当系统状态为零时, 最优控制策略是不为零的, 这与以往不同. 然后, 通过构建单一评判网络来近似每个玩家的最优代价函数, 从而获得相关的近似最优控制策略. 同时, 在评判学习期间发展了一种新的权值更新规则. 此外, 通过利用Lyapunov理论证明了评判网络权值近似误差和闭环系统状态的稳定性. 最后, 仿真结果验证了本文所提方法的有效性     

离散输入受限系统的增益调度事件触发和自触发控制

针对离散输入受限系统,分别设计静态和动态的增益调度事件触发和自触发控制算法.首先设计一种基于离散参量Lyapunov方程的静态增益调度事件触发控制算法,该算法通过事件触发机制更新控制增益,使得在增大闭环系统收敛速率的同时节约通讯资源.为了避免对采样状态和测量误差的连续监测,设计了相应的静态增益调度自触发控制算法;同时,...     

严格反馈系统的事件触发学习控制

本文针对一类严格反馈非线性系统,提出了基于确定学习的事件触发控制方案.首先,在本地控制测试端设计自适应神经网络控制,并在控制过程中实现系统未知动态的知识获取和存储.随后,基于常值权值,设计了新颖的事件触发控制器和事件触发条件.结合李雅普诺夫稳定性分析和非线性脉冲动态系统原理,验证了所提方案能够保证跟踪误差收敛到零的小邻域内以及所有闭环信号是最终一致有界的.此外,本文所提方案采用常值权值代替了估计权值,使得所提方案易于实现,暂态性能好和网络资源占用少.最后,通过对比仿真结果证明了所提方案的有效性.     

事件触发机制下的无人机半全局编队跟踪控制

文章研究了具有输入饱和的无人机系统(UAV)的事件触发半全局编队跟踪问题。首先设计了基于事件触发机制的编队跟踪控制协议,然后利用低增益反馈技术,提出了一种解决事件触发编队跟踪控制的算法。最后,通过仿真验证理论结果的有效性。结果表明:提出的控制策略保证UAV能够完成指定的半全局编队跟踪控制,并排除Zeno行为。     

非线性零和微分对策的事件触发自适应动态规划算法

针对一类非线性零和微分对策问题,本文提出了一种事件触发自适应动态规划(event-triggered adaptive dynamic programming,ET--ADP)算法在线求解其鞍点.首先,提出一个新的自适应事件触发条件.然后,利用一个输入为采样数据的神经网络(评价网络)近似最优值函数,并设计了新型的神经网络权值更新律使得值函数、控制策略及扰动策略仅在事件触发时刻同步更新.进一步地,利用Lyapunov稳定性理论证明了所提出的算法能够在线获得非线性零和微分对策的鞍点且不会引起Zeno行为.所提出的ET--ADP算法仅在事件触发条件满足时才更新值函数、控制策略和扰动策略,因而可有效减少计算量和降低网络负荷.最后,两个仿真例子验证了所提出的ET--ADP算法的有效性.     

Finite-time optimal tracking control for a class of nonlinear systems with prescribed performance

In this paper, a finite-time optimal tracking control scheme based on integral reinforcement learning is developed for partially unknown nonlinear systems. In order to realize the prescribed performance, the original system is transformed into an equivalent unconstrained system so as to a composite system is constructed. Subsequently, a modified nonlinear quadratic performance function containing the auxiliary tracking error is designed. Furthermore, the technique of experience replay is used to update the critic neural network, which eliminates the persistent of excitation condition in traditional optimal methods. By combining the prescribed performance control with the finite-time optimization control technique, the tracking error is driven to a desired performance in finite time. Consequently, it has been shown that all signals in the partially unknown nonlinear system are semiglobally practical finite-time stable by stability analysis. Finally, the provided comparative simulation results verify the effectiveness of the developed control scheme.     

Event‐triggered optimal tracking control of nonlinear systems

We propose a novel event‐triggered optimal tracking control algorithm for nonlinear systems with an infinite horizon discounted cost. The problem is formulated by appropriately augmenting the system and the reference dynamics and then using ideas from reinforcement learning to provide a solution. Namely, a critic network is used to estimate the optimal cost while an actor network is used to approximate the optimal event‐triggered controller. Because the actor network updates only when an event occurs, we shall use a zero‐order hold along with appropriate tuning laws to encounter for this behavior. Because we have dynamics that evolve in continuous and discrete time, we write the closed‐loop system as an impulsive model and prove asymptotic stability of the equilibrium point and Zeno behavior exclusion. Simulation results of a helicopter, a one‐link rigid robot under gravitation field, and a controlled Van‐der‐Pol oscillator are presented to show the efficacy of the proposed approach. Copyright © 2016 John Wiley & Sons, Ltd.     

Event-triggered optimal tracking control of multiplayer unknown nonlinear systems via adaptive critic designs

In this article, the event-triggered optimal tracking control problem for multiplayer unknown nonlinear systems is investigated by using adaptive critic designs. By constructing a neural network (NN)-based observer with input–output data, the system dynamics of multiplayer unknown nonlinear systems is obtained. Subsequently, the optimal tracking control problem is converted to an optimal regulation problem by establishing a tracking error system. Then, the optimal tracking control policy for each player is derived by solving coupled event-triggered Hamilton-Jacobi (HJ) equation via a critic NN. Meanwhile, a novel weight updating rule is designed by adopting concurrent learning method to relax the persistence of excitation (PE) condition. Moreover, an event-triggering condition is designed by using Lyapunov's direct method to guarantee the uniform ultimate boundedness (UUB) of the closed-loop multiplayer systems. Finally, the effectiveness of the developed method is verified by two different multiplayer nonlinear systems.     

Robust optimal control of the multi-input systems with unknown disturbance based on adaptive integral reinforcement learning Q-function

Considering overshoot and chatter caused by the unknown interference, this article studies the adaptive robust optimal controls of continuous-time (CT) multi-input systems with an approximate dynamic programming (ADP) based Q-function scheme. An adaptive integral reinforcement learning (IRL) scheme is proposed to study the optimal solutions of Q-functions. First, multi-input value functions are presented, and Nash equilibrium is analyzed. A complex Hamilton–Jacobi–Issacs (HJI) equation is constructed with the multi-input system and the zero-sum-game-based value function. It is a challenging task to solve the HJI equation for nonlinear system. Thus, A transformation of the HJI equation is constructed as a Q-function. The neural network (NN) is applied to learn the solution of the transformed Q-functions based on the adaptive IRL scheme. Moreover, an error information is added to the Q-function for the issue of insufficient initial incentives to relax the persistent excitation (PE) condition. Simultaneously, an IRL signal of the critic networks is introduced to study the saddle-point intractable solution, such that the system drift and NN derivatives in the HJI equation are relaxed. The convergence of weight parameters is proved, and the closed-loop stability of the multi-system with the proposed IRL Q-function scheme is analyzed. Finally, a two-engine driven F-16 aircraft plant and a nonlinear system are presented to verify the effectiveness of the proposed adaptive IRL Q-function scheme.     

非线性离散系统的近似最优跟踪控制

研究非线性离散系统的最优跟踪控制问题. 通过在由最优控制问题所导致的非线性两点边值问题中引入灵敏度参数, 并对它进行Maclaurin级数展开, 将原最优跟踪控制问题转化为一族非齐次线性两点边值问题. 得到的最优跟踪控制由解析的前馈反馈项和级数形式的补偿项组成. 解析的前馈反馈项可以由求解一个Riccati差分方程和一个矩阵差分方程得到. 级数补偿项可以由一个求解伴随向量的迭代算法近似求得. 以连续槽式反应器为例进行仿真验证了该方法的有效性．     

Distributed tracking control of uncertain mechanical systems with velocity constraints

This paper considers the leader‐following control problem of multiple mechanical systems with uncertainty and velocity constraints. So as to deal with the velocity constraints, a reduction procedure is applied to transform the model of each system to a cascaded system. With the aid of the cascade structure of each system and the properties of linear time‐varying systems, distributed robust feedback controllers are proposed such that the state of each follower system asymptotically converges to the state of a leader system with the aid of neighbors' information. So as to reduce the cost of the communication between systems, an event‐triggered leader‐following control problem is also considered, and event‐triggered distributed controllers are proposed. As an application of the proposed results, formation control of wheeled mobile robots is considered, and distributed controllers are obtained with the aid of the results in Theorems 1 and 2. Simulation results show the effectiveness of the proposed results. Copyright © 2017 John Wiley & Sons, Ltd.     

Observer-based approximate optimal tracking control for time-delay systems with external disturbances

This paper proposes a successive approximation design approach of observer-based optimal tracking controllers for time-delay systems with external disturbances. To solve a two-point boundary value problem with time-delay and time-advance terms and obtain the optimal tracking control law, two sequences of vector differential equations are constructed first. Second, the convergence of the sequences of the vector differential equations is proved to guarantee the existence and uniqueness of the control law. Third, a design algorithm of the optimal tracking control law is presented and the physically realisable problem is addressed by designing a disturbance state observer and a reference input state observer. An example of an industrial electric heater is given to demonstrate the efficiency of the proposed approach.     

An optimal control approach to robust tracking of linear systems

In our early work, we show that one way to solve a robust control problem of an uncertain system is to translate the robust control problem into an optimal control problem. If the system is linear, then the optimal control problem becomes a linear quadratic regulator (LQR) problem, which can be solved by solving an algebraic Riccati equation. In this article, we extend the optimal control approach to robust tracking of linear systems. We assume that the control objective is not simply to drive the state to zero but rather to track a non-zero reference signal. We assume that the reference signal to be tracked is a polynomial function of time. We first investigated the tracking problem under the conditions that all state variables are available for feedback and show that the robust tracking problem can be solved by solving an algebraic Riccati equation. Because the state feedback is not always available in practice, we also investigated the output feedback. We show that if we place the poles of the observer sufficiently left of the imaginary axis, the robust tracking problem can be solved. As in the case of the state feedback, the observer and feedback can be obtained by solving two algebraic Riccati equations.     

基于观测器的受扰非线性系统近似最优跟踪控制

研究一类受扰非线性系统的最优输出跟踪控制问题.给出了有限时域最优输出跟踪控制律的近似设计算法.首先将求解受扰非线性系统最优跟踪控制问题转换为求解状态向量与伴随向量耦合的非线性两点边值问题,然后利用逐次逼近方法构造序列将其转化为求解两个解耦的线性微分方程序列问题.通过迭代求解伴随向量的序列,可得到由解析的线性前馈-反馈控制部分和伴随向量的极限形式的非线性补偿部分组成的最优输出跟踪控制律.利用参考输入降维观测器和扰动降维观测器,解决了前馈控制的物理可实现问题.最后仿真结果表明了该方法的有效性.