执行器故障不确定非线性系统最优自适应输出跟踪控制

本文针对一类具有执行器故障的多输入多输出（Multi-input multi-output,MIMO）不确定连续仿射非线性系统,提出了一种最优自适应输出跟踪控制方案.设计了保证系统稳定性的不确定项估计神经网络权值调整算法,仅采用评价网络即可同时获得无限时域代价函数和满足哈密顿-雅可比-贝尔曼（Hamilton-Jacobi-Bellman,HJB）方程的最优控制输入.考虑执行器卡死和部分失效故障,设计最优自适应补偿控制律,所设计的控制律可以实现对参考输出的一致最终有界跟踪.飞行器控制仿真和对比验证表明了本文方法的有效性和优越性.     

基于自适应动态规划的非线性鲁棒近似最优跟踪控制

为克服现有近似最优跟踪控制方法只能跟踪连续可微参考输入的局限,本文针对一类具有未知动态的连续时间非线性时不变仿射系统,提出了一种新的基于自适应动态规划的鲁棒近似最优跟踪控制方法.首先采用递归神经网络建立系统模型,然后建立评价神经网络对最优性能指标进行估计,从而得到最优性能指标偏导数的估计值,进而得到近似最优跟踪控制器,最后利用系统输出与参考输入之间的跟踪误差设计鲁棒项对神经网络建模误差进行补偿.分别针对两个非线性系统进行仿真实验,仿真结果表明了所提方法的有效性和优越性.     

间歇过程最优迭代学习控制的发展:从基于模型到数据驱动

本文综述了间歇过程的基于模型的和数据驱动的最优迭代学习控制方法.基于模型的最优迭代学习控制方法需要已知被控对象精确的线性模型,其研究较为成熟和完善,有着系统的设计方法和分析工具.数据驱动的最优迭代学习控制系统设计和分析的关键是非线性重复系统的迭代动态线性化.本文简要综述了基于模型的最优迭代学习控制的研究进展,详细回顾了数据驱动的迭代动态线性化方法,包括其详细的推导过程和突出的特点.回顾和讨论了广义的数据驱动最优迭代学习控制方法,包括完整轨迹跟踪的数据驱动最优迭代学习控制方法,提出和讨论了多中间点跟踪的数据驱动最优点到点迭代学习控制方法,和终端输出跟踪的数据驱动最优终端迭代学习控制方法.进一步,迭代学习控制研究中的关键问题,如随机迭代变化初始条件、迭代变化参考轨迹、输入输出约束、高阶学习控制律、计算复杂性等.本文突出强调了基于模型的和数据驱动的最优迭代学习控制方法各自的特点与区别联系,以方便读者理解.最后,本文提出数据驱动的迭代学习控制方法已成为越来越复杂间歇过程控制发展的未来方向,一些开放的具有挑战性的问题还有待于进一步研究.     

一类离散时间时变系统的自适应辨识与控制

考虑一类离散时间时变系统（ＴＶＳ）的建模、系统参数时变描述及其自适应控制问题,提出一种新的能结合“快”、“慢”时变特性的ＴＶＳ参数时变描述方法,基于零极点配置给出自适应辨识和控制算法及其控制系统的稳定性。仿真结果说明该控制策略是有效的。     

基于迭代神经动态规划的数据驱动非线性近似最优调节

利用数据驱动控制思想,建立一种设计离散时间非线性系统近似最优调节器的迭代神经动态规划方法.提出针对离散时间一般非线性系统的迭代自适应动态规划算法并且证明其收敛性与最优性.通过构建三种神经网络,给出全局二次启发式动态规划技术及其详细的实现过程,其中执行网络是在神经动态规划的框架下进行训练.这种新颖的结构可以近似代价函数及其导函数,同时在不依赖系统动态的情况下自适应地学习近似最优控制律.值得注意的是,这在降低对于控制矩阵或者其神经网络表示的要求方面,明显地改进了迭代自适应动态规划算法的现有结果,能够促进复杂非线性系统基于数据的优化与控制设计的发展.通过两个仿真实验,验证本文提出的数据驱动最优调节方法的有效性.     

离散线性信息融合最优跟踪控制

提出一种有限时间离散线性最优跟踪控制问题的新解法--信息融合估计解法.基于信息融合估计理论,推导出协状态融合滤波方程和控制量融合估计值,由此获得最优融合控制律及二次性能指标最小值.从理论上证明了信息融合估计解法与传统解法的等同性,从信息融合的角度建立了有限时间离散线性最优跟踪控制系统,从而统一了最优控制问题和最优估计问题,电机系统的控制仿真结果验证了该解法的有效性以及与传统解法的等同性.     

基于自适应动态规划的矿渣微粉生产过程跟踪控制

矿渣微粉是一种新型绿色环保型建材,可以大大提高水泥混凝土的力学性能.本文以矿渣微粉生产过程为研究对象,针对该过程难以通过机理建模进行辨识和控制的特点,利用数据驱动的思想,建立矿渣微粉生产过程的递归神经网络模型.在此基础上,利用自适应动态规划,设计具有控制约束的跟踪控制器,并将其应用到矿渣微粉生产过程中.仿真分析表明,建立的数据驱动模型能够有效地辨识矿渣微粉生产过程,同时,本文提出的控制方法能够实现输入受限的微粉比表面积及磨内压差的最优跟踪控制.     

离散时滞系统最优跟踪控制及应用

本文研究线性时滞离散时间系统的最优跟踪控制问题,首先针对具有控制时滞,且输入输出之间具有前向直接通道的系统讨论,进而考虑一般多重时滞系统.本文利用线性二次型加积分(LQI)的最优状态反馈控制理论实现负荷变化时的最优跟踪控制.文中研究了闭环系统的稳定性及输出完全跟踪,并针对某针厂一无纺针热处理淬火炉,进行了温度跟踪控制的仿真研究.仿真结果表明,本文提出的控制方案在温度给定值变化条件下能达到快速、小偏差的跟踪效果.     

一类双线性系统的最优跟踪控制问题

针对一类具有二次型性能指标的双线性系统的最优跟踪控制问题,提出了一种通过逐次逼近法设计最优控制律的近似方法。首先将状态向量含有时滞的双线性系统的最优跟踪问题转化为最优调节问题;然后利用逐次逼近算法,将既含有时滞项又含有超前项的两点边值问题转化为不含时滞项和超前项的线性两点边值问题族,得到调节系统的最优控制律,并可以通过截取最优控制序列的有限项得到调节系统的前馈-反馈次优控制律。最后,将最优控制问题转化为最优跟踪问题。仿真结果表明,此方法达到了较好的跟踪效果。     

非线性非仿射离散时间系统的两阶段最优迭代学习控制

针对非仿射非线性离散时间系统, 基于一种新的沿迭代轴的动态线性化技术, 提出了双层最优迭代学习控制算法. 双层意味着分别设计了两个最优学习层, 迭代的改进控制输入序列和学习增益. 其主要特点是控制器的设计和收敛性分析只依赖于动态系统的 I/O 数据. 换句话说, 不需要知道系统的任何其他信息就可以很容易的选取控制器参数. 仿真研究表明了提出的算法沿迭代轴具有几何收敛性, 这一特点在快速路交通迭代学习控制中具有重要的工程意义.     

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.      

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.     

Parallel Control for Optimal Tracking via Adaptive Dynamic Programming

This paper studies the problem of optimal parallel tracking control for continuous-time general nonlinear systems. Unlike existing optimal state feedback control, the control input of the optimal parallel control is introduced into the feedback system. However, due to the introduction of control input into the feedback system, the optimal state feedback control methods can not be applied directly. To address this problem, an augmented system and an augmented performance index function are proposed firstly. Thus, the general nonlinear system is transformed into an affine nonlinear system. The difference between the optimal parallel control and the optimal state feedback control is analyzed theoretically. It is proven that the optimal parallel control with the augmented performance index function can be seen as the suboptimal state feedback control with the traditional performance index function. Moreover, an adaptive dynamic programming (ADP) technique is utilized to implement the optimal parallel tracking control using a critic neural network (NN) to approximate the value function online. The stability analysis of the closed-loop system is performed using the Lyapunov theory, and the tracking error and NN weights errors are uniformly ultimately bounded (UUB). Also, the optimal parallel controller guarantees the continuity of the control input under the circumstance that there are finite jump discontinuities in the reference signals. Finally, the effectiveness of the developed optimal parallel control method is verified in two cases.      

Online Adaptive Approximate Optimal Tracking Control with Simplified Dual Approximation Structure for Continuous-time Unknown Nonlinear Systems

This paper proposes an online adaptive approximate solution for the infinite-horizon optimal tracking control problem of continuous-time nonlinear systems with unknown dynamics. The requirement of the complete knowledge of system dynamics is avoided by employing an adaptive identifier in conjunction with a novel adaptive law, such that the estimated identifier weights converge to a small neighborhood of their ideal values. An adaptive steady-state controller is developed to maintain the desired tracking performance at the steady-state, and an adaptive optimal controller is designed to stabilize the tracking error dynamics in an optimal manner. For this purpose, a critic neural network (NN) is utilized to approximate the optimal value function of the Hamilton-Jacobi-Bellman (HJB) equation, which is used in the construction of the optimal controller. The learning of two NNs, i.e., the identifier NN and the critic NN, is continuous and simultaneous by means of a novel adaptive law design methodology based on the parameter estimation error. Stability of the whole system consisting of the identifier NN, the critic NN and the optimal tracking control is guaranteed using Lyapunov theory; convergence to a near-optimal control law is proved. Simulation results exemplify the effectiveness of the proposed method.      

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.      

基于数据自适应评判的离散2-D系统零和博弈最优控制

提出了基于一种迭代自适应评判设计(ACD)算法解决一类离散时间Roesser型2-D系统的二人零和对策问题. 文章主要思想是采用自适应评判技术迭代的获得最优控制对使得性能指标函数达到零和对策的鞍点. 所提出的ACD可以通过输入输出数据进行实现而不需要系统的模型. 为了实现迭代ACD算法, 神经网络分别用来近似性能指标函数和计算最优控制率. 最后最优控制策略将应用到空气干燥过程控制中以证明其有效性.     

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 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.      

Data-Based Optimal Tracking of Autonomous Nonlinear Switching Systems

In this paper,a data-based scheme is proposed to solve the optimal tracking problem of autonomous nonlinear switching systems.The system state is forced to track the reference signal by minimizing the performance function.First,the problem is transformed to solve the corresponding Bellman optimality equation in terms of the Q-function(also named as action value function).Then,an iterative algorithm based on adaptive dynamic programming(ADP)is developed to find the optimal solution which is totally based on sampled data.The linear-in-parameter(LIP)neural network is taken as the value function approximator.Considering the presence of approximation error at each iteration step,the generated approximated value function sequence is proved to be boundedness around the exact optimal solution under some verifiable assumptions.Moreover,the effect that the learning process will be terminated after a finite number of iterations is investigated in this paper.A sufficient condition for asymptotically stability of the tracking error is derived.Finally,the effectiveness of the algorithm is demonstrated with three simulation examples.