MIMO线性离散系统的最优鲁棒跟踪控制

将MIMO线性离散定常系统在指定信号输入下获取最优时域指标的最优跟踪控制器设计问题归纳为求解一个有限维线性规划问题, 并证明这种最优跟踪控制器也是鲁棒稳定控制器.     

输出概率密度函数鲁棒弹性最优跟踪控制

研究了一类随机动态系统的鲁棒弹性最优跟踪控制问题。在采用B样条神经网络模型逼近随机动态系统的输出概率密度函数(PDF)的基础上,同时考虑系统模型和控制器增益不确定性,结合Lyapunov稳定性理论和线性矩阵不等式(LMI)技术,引入增广控制作用,设计基于广义状态反馈的鲁棒弹性最优跟踪控制器,目的是使系统的输出PDF跟踪给定PDF。通过求解LMI,所得控制器不仅能实现跟踪目的,而且能确保该随机动态系统全局稳定并满足一定的线性二次型性能指标上界。仿真结果表明该方法简单易行,且无需任何设计参数调整。     

不确定广义模糊系统的鲁棒模糊H∞控制器设计

研究了不确定广义模糊系统鲁棒H∞状态反馈控制器和动态输出反馈控制器设计问题。在E确定其它系数矩阵均存在不确定性情况下，给出鲁棒模糊H∞状态反馈控制器和动态输出反馈控制器存在的充分条件。鲁棒H∞状态反馈控制律的设计可能通过求解线性矩阵不等式得到，而动态输出反馈鲁棒H∞控制器可通过定义新变量得到，所求控制器使闭环系统对所有的不确定性稳定且满足H∞性能指标γ。     

不确定性系统的自适应鲁棒跟踪控制

针对存在未知干扰和未建模动态等不确定性的系统的自适应鲁棒跟踪控制问题进行了 探讨.首选将l1优化控制器的有限拍设计方法结合给出了最优鲁棒稳态跟踪控制器的设计方法． 然后利用集员辨识的思想,将名义模型的参数和未建模动态及干扰的大小作为未知参数,提出了 一种递推参数估计方法.最后将上述研究结果结合起来提出了一种自适应鲁棒跟踪控制策略,证 明了自适应算法的全局收敛性并给出了鲁棒跟踪性能指标的一下较紧的上界.与现有的结果相 比,本文提出的自适应控制具有非保守的鲁棒稳定性,具有渐近最优的鲁棒跟踪性能.     

离散不确定系统的鲁棒预测控制

对预测控制中模型不确定性的处理一直是预测控制算法亟待解决的问题.考虑一类包含模型不确定性的控制对象模型,提出一种极大极小预测控制器设计方法.在滚动优化的每一步,考虑了状态变量不完全可测的情况,引入动态输出反馈的思想得到一个最坏条件下的性能指标的最优解,以最坏条件下的性能指标为求解优化问题的上界,通过线性矩阵不等式的方法求解凸优化问题.并从理论上证明了所设计的鲁棒预测控制器对不确定模型是稳定的.通过仿真算例的分析,证明了极大极小鲁棒预测控制器设计的有效性.     

一种鲁棒故障检测与反馈控制的最优集成设计方法

研究线性不确定系统的反馈控制器与鲁棒故障检测滤波器集成设计问题.基于新提出 的性能指标函数,将鲁棒故障检测滤波器设计问题归结为最优化问题,通过求解Riccati方程可 得到鲁棒故障检测滤波器设计问题的最优解.在共用同一状态观测器的情况下,将反馈控制器 和鲁棒故障检测滤波器的集成设计问题归结为两目标优化问题,解决了同时满足闭环控制系统 设计要求和故障诊断系统鲁棒性能的最优集成设计问题.简例验证了提出算法的有效性.     

基于H∞方法的不确定系统迭代学习控制设计

针对不确定线性离散系统,研究了开闭环型的鲁棒迭代学习控制器。给出了控制器收敛的充分条件,根据此条件,将迭代学习控制的设计问题转化为H∞设计问题,提出了一种兼具反馈闭环控制与前馈学习控制的鲁棒迭代学习控制律,并采用H∞性能指标对系统进行优化,使系统的收敛率基于H∞最优,然后使用线性不等式（LMI）方法求解迭代学习控制器的参数。仿真实例表明了该设计方法的有效性。     

考虑执行器故障的鲁棒H∞可靠控制

本文采用比离散模型更一般的连续增益故障模型，提出了线性不确定系统考虑执行器故障鲁棒H∞可靠控制问题，利用LMI分别给出了多故障鲁棒H∞可靠控制器和单故障鲁棒H∞可靠控制器存在的充分条件。在故障情况下，保证闭环系统渐近稳定和可接受的性能指标基础上，优化了无故障正常情况下的指标。在仿真实例中，不仅验证所提出方法的可行性，而且比较了鲁棒H∞正常控制器和鲁棒H∞可靠控制器的控制效果，进一步看出对系统进行最优鲁棒H∞靠控制器设计的必要性。     

考虑执行器故障的鲁棒H可靠控制

本文采用比离散模型更一般的连续增益故障模型，提出了线性不确定系统考虑执行器故障鲁棒H∞可靠控制问题，利用 LMI分别给出了多故障鲁棒 H∞可靠控制器和单故障鲁棒 H∞可靠控制器存在的充分条件。在故障情况下，保证闭环系统渐近稳定和可接受的性能指标基础上，优化了无故障正常情况下的指标。在仿真实例中，不仅验证所提出方法的可行性，而且比较了鲁棒 H∞正常控制器和鲁棒 H∞可靠控制器的控制效果，进一步看出对系统进行最优鲁棒 H∞可靠控制器设计的必要性。     

基于T-S模型的非线性系统非脆弱极小极大控制

研究一类非线性离散系统的鲁棒非脆弱极小极大控制问题.针对含有不确定项的非线性离散系统,构造T-S模型,引入参数不确定项,使得模糊模型能够更精确逼近原系统.考虑系统和控制器同时含有不确定性,利用线性矩阵不等式(LMI)处理方法和Lyapunov稳定性理论,设计鲁棒且非脆弱的控制器.考虑不确定性使得性能指标最大的情形,得到极小极大鲁棒非脆弱控制器存在的充分条件.引入凸优化算法,求解使闭环系统渐近稳定且性能指标上界最小的最优极小极大鲁棒控制器的参数.最后以著名的truck-trailer模型为例的仿真结果表明所设计的控制器具有良好的鲁棒性和非脆弱性.     

Vectorization and multitasking of dynamic programming in control: Experiments on a CRAY-2

Parallelism in dynamic programming is considered within the specificity of optimal control. We present the program PDVP developed for solving a general deterministic discrete-time optimization problem by means of a parallel dynamic programming algorithm on the state variables. Multitasking and vectorization are considered from the viewpoint to implement PDVP on a CRAY-2. The performances are analysed through a significant application to the optimization of satellite trajectories. Promising results are obtained.     

离散时间最优控制—–评论动态规划

阐述离散时间最优控制的特点.对比3种求解离散时间最优控制的解法,即:1)用非线性规划求解离散时间最优控制;2)用无约束优化求解离散时间最优控制;3)动态规划及其数值解.1)和2)都适用于多维静态优化,计算效率较高,是高级方法.在名义上,3)为动态优化.实际上,3)为一维分段无约束静态优化,计算效率较低,是初级方法.本文并用数字实例进一步阐明动态规划及其数值解在求解方面较差,故动态规划及其数值解已失去实用价值.在求解离散时间最优控制问题方面,无法与非线性规划求解相匹敌.     

Robust H 2-control for discrete-time Markovian jump linear systems

This paper deals with the robust H₂-control of discrete-time Markovian jump linear systems. It is assumed that both the state and jump variables are available to the controller. Uncertainties satisfying some norm bounded conditions are considered on the parameters of the system. An upper bound for the H₂-control problem is derived in terms of a linear matrix inequality (LMI) optimization problem. For the case in which there are no uncertainties, we show that the convex formulation is equivalent to the existence of the mean square stabilizing solution for the set of coupled algebraic Riccati equations arising on the quadratic optimal control problem of discrete-time Markovian jump linear systems. Therefore, for the case with no uncertainties, the convex formulation considered in this paper imposes no extra conditions than those in the usual dynamic programming approach. Finally some numerical examples are presented to illustrate the technique.     

Dynamic programming for constrained optimal control of discrete-time linear hybrid systems

In this paper we study the solution to optimal control problems for constrained discrete-time linear hybrid systems based on quadratic or linear performance criteria. The aim of the paper is twofold. First, we give basic theoretical results on the structure of the optimal state-feedback solution and of the value function. Second, we describe how the state-feedback optimal control law can be constructed by combining multiparametric programming and dynamic programming.     

Viability decision of linear discrete-time stochastic systems with probability criterion

In this paper, the optimal viability decision problem of linear discrete-time stochastic systems with probability criterion is investigated. Under the condition of sequence-reachable discrete-time dynamic systems, the existence theorem of optimal viability strategy is given and the solving procedure of the optimal strategy is provided based on dynamic programming. A numerical example shows the effectiveness of the proposed methods.     

An algorithm for a solution of a stochastic adaptive linear quadratic optimal control problem

A variational approach is taken to derive optimality conditions for a discrete-time linear quadratic adaptive stochastic optimal control problem. These conditions lead to an algorithm for computing optimal control laws which differs from the dynamic programming algorithm.     

Constrained Optimal Control of Hybrid Systems With a Linear Performance Index

We consider the constrained finite and infinite time optimal control problem for the class of discrete-time linear hybrid systems. When a linear performance index is used the finite and infinite time optimal solution is a piecewise affine state feedback control law. In this paper, we present algorithms that compute the optimal solution to both problems in a computationally efficient manner and with guaranteed convergence and error bounds. Both algorithms combine a dynamic programming exploration strategy with multiparametric linear programming and basic polyhedral manipulation     

Risk-sensitive control and dynamic games for partially observeddiscrete-time nonlinear systems

Solves a finite-horizon partially observed risk-sensitive stochastic optimal control problem for discrete-time nonlinear systems and obtains small noise and small risk limits. The small noise limit is interpreted as a deterministic partially observed dynamic game, and new insights into the optimal solution of such game problems are obtained. Both the risk-sensitive stochastic control problem and the deterministic dynamic game problem are solved using information states, dynamic programming, and associated separated policies. A certainty equivalence principle is also discussed. The authors' results have implications for the nonlinear robust stabilization problem. The small risk limit is a standard partially observed risk-neutral stochastic optimal control problem     

Dissipative control of linear discrete-time systems with dissipative uncertainty

This paper focuses on the dissipative control of uncertain linear discrete-time systems. The uncertainty under consideration is characterized by a dissipative system, which contains commonly used uncertainty structures, such as normbounded and positive real uncertainties, as special cases. We consider the design of a feedback controller which can achieve asymptotic stability and strict quadratic dissipativeness for all admissible uncertainties. Both the linear static state feedback and the dynamic output feedback controllers are considered. It is shown that the robust dissipative control problem can be solved in terms of a scaled quadratic dissipative control problem without uncertainty. Linear matrix inequality (LMI) based methods for designing robust controllers are derived. The result of this paper unifies existing results on discrete-time H and positive real control and it provides a more flexible and less conservative control design as it al ows for a bet er trade-off between phase and gain performances.