广义代数Riccati方程和最优调节器的研究

利用能稳性和精确能观性, 对广义代数Riccati方程和相关的随机最优调节器问题进行了深入的研究. 对广义代数Riccati方程得到了下列结果: 如果随机系统既是能稳定的又是精确能观的, 则广义代数Riccati方程有一个最大解, 同时也是一个反馈镇定解. 在精确能观性的假设下, 广义代数Riccati方程的所有非负定解(如果存在的话)必是正的反馈镇定解. 作为应用, 最优调节器问题, 广义代数Riccati方程的最大解, 反馈镇定解三者之间的关系获得了澄清. 所有这些结果在随机控制和随机稳定性理论中是有     

完全滞后系统的滞后反馈镇定

研究完全滞后系统的滞后状态反馈镇定.通过建立关于时滞系统的新型渐近稳定性定 理.构造适当的Lyapunov泛函、利用矩阵Riccati代数方程,得到了完全滞后系统的滞后反 馈镇定方法.     

时滞不确定组合大系统的鲁棒分散镇定

将Riccati方法扩展到具有状态和输入时滞的不确定组合系统,给出了系统经状态反馈分散二次镇定的一个充分条件并证明该条件的成立等价于一个LMI最优解的存在。利用LMI最优解,给出了分散控制器的设计方案。仿真结果表明了该方法的有效性。     

线性离散系统的鲁棒约束方差控制——含结构参数扰动的情形

研究一粪性能鲁棒控制器的设计问题,即针对含结构参数扰动的线性离散随机系统,设计 反馈控制器,使闭环系统不仅渐近稳定,而且其每个状态分量的稳态方差不大于各自预先给定 的上界.基于一个修正的代数Riccati方程,给出了上述性能鲁棒控制器的存在条件及解析 表达式,并举出相应的数值例子,以说明本文设计方法的简单性与直接性.     

正规和奇异H∞控制器的统一表达式

给出了一种新的H∞控制器设计方法,通过引入设计时可选的非奇异实数阵,取消了控制器设计时对D矩阵的秩限制.适用于正规的H∞控制问题和奇异的H∞控制问题.对状态反馈等四种典型问题和输出反馈控制问题,给出了控制器存在的充分必要条件.控制器通过Riccati方程的解,用参数化方法表示.输出反馈控制器,通过解两个Riccati方程得到.讨论了控制器的相关特性.     

基于微分对策的最优状态观测器和最优状态反馈控制器的设计

研究了线性系统基于二次型指标的最优状态观测器和最优状态反馈控制器的设计问题．将观测状态的状态反馈和状态误差的输出反馈分别作为两个对局方,应用微分对策理论研究了系统的最优控制问题．给出了最优状态观测器和基于状态观测器的最优状态反馈控制的存在性条件．将系统的最优状态观测器和最优控制器的设计问题转化为一对Riccati方程的求解问题．研究表明最优状态观测器在一般情况下不存在．并进一步研究了基于状态观测器的次优控制问题,给出了基于LMI的优化算法．     

不确定性时滞系统H∞鲁棒保性能控制

在Riccati不等式的基础上,给出鲁棒保性能H∞不确定性时滞系统的定义.然后以性能指标上界最小为优化命题设计状态反馈控制器,使得闭环系统不仅对容许的参数不确定性以及时变时滞保持渐近稳定,而且具有给定H∞干扰衰减度以及保性能上界最优.数值实例及仿真结果表明了该方法的有效性和可行性.     

无限时间长时延网络控制系统的随机最优控制

考虑二次性能指标下线性网络控制系统的随机最优控制问题,建立了控制器为事件驱动时长时延线性网络控制系统的数学模型,证明了在无限时间情况下离散随机黎卡提代数方程解的存在性,设计出无限时间情况下线性网络控制系统的随机最优控制器,得到相应的最优性能指标的表达形式,并证明了相应的随机最优控制器可使网络控制系统均方指数稳定.最后以网络控制下的倒立摆为对象进行仿真研究,仿真结果表明该方法的正确性和有效性.     

最优二次型渐近设计法及其应用

本文探讨了用闭环系统预期特征值及特征向量确定加权阵,及依靠最优调节器的渐近特 性确定Riccati代数方程迭代解起始阵的方法.据此提出了线性系统最优二次型渐近设计法, 编写了设计程序,实验结果表明方法是可行的.     

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.     

On the existence of maximal solution for generalized algebraic Riccati equations arising in stochastic control

Existence of maximal solution is proved for a generalized version of the well-known standard algebraic Riccati equations which arise in certain stochastic optimal control problems.     

带有随机跳跃干扰的线性二次随机最优控制问题

给出一类布朗运动和泊松过程混合驱动的正倒向随机微分方程解的存在唯一性结果, 应用这一结果研究带有随机跳跃干扰的线性二次随机最优控制问题,并得到最优控制的显式形 式,可以证明最优控制是唯一的.然后,引入和研究一类推广的黎卡提方程系统,讨论该方程系统 的可解性并由该方程的解得到带有随机跳跃干扰的线性二次随机最优控制问题最优的线性反馈.     

Multi‐Objective Cost‐Detectability in Unfalsified Adaptive Control

Vector‐valued controller cost functions that are solely data‐dependent and reflect multiple objectives of a control system are examined within the framework of unfalsified adaptive control. The notion of Pareto optimality of vector‐valued cost functions and the conditions under which they are cost‐detectable are discussed. A sampled data/discrete‐time Level‐Set controller switching algorithm is investigated which allows for the relaxation of the assumption that the controller cost function be monotonically nondecreasing in time. This opens up the possibility of the use of fading memory cost functions which are nonmonotone. When an active controller is falsified at the current threshold cost level, the Level‐Set switching algorithm replaces it by an effectively unique solution of the weighted Tchebycheff method, thus ensuring the selection of an unfalsified Pareto optimal controller. Theoretical results for convergence and stability of the adaptive system are given. Simulation results validate the use of cost‐detectable multi‐objective cost functions. An example of a cost‐detectable cost function which uses fading memory norm of the fictitious tracking error as a performance measure is shown. This allows for computation of performance of nonactive controllers with respect to a reference model.     

随机系统的多模型直接自适应解耦控制器

针对多变量离散时间随机系统, 提出了一种采用广义最小方差性能指标的多模型直接自适应解耦控制器. 该多模型控制器由多个固定控制器和两个自适应控制器构成. 固定控制器用以覆盖系统参数的可能变化范围, 自适应控制器用以保证系统的稳定性和提高暂态性能. 该多模型控制器利用矩阵的伪交换性和拟Diophantine方程性质, 基于广义最小方差性能指标, 将随机系统辨识算法和最优控制器设计相结合, 直接辨识出控制器的参数, 通过广义最小方差性能指标中加权多项式的选取,不但实现了多变量系统的动态解耦控制, 而且消除了稳态误差、配置了闭环极点. 文末给出了全局收敛性分析. 仿真结果表明该方法明显优于常规自适应控制器.     

Multi-objective voltage and frequency regulation in autonomous microgrids using Pareto-based Big Bang-Big Crunch algorithm

Voltage and frequency regulation is one of the most vital issues in autonomous microgrids to ensure an acceptable electric power quality supply to customers. In this paper, a real-time control structure including power, voltage, and current control loops is proposed for microgrid inverters to restore voltage and frequency of the system after the initiation and load changes. The Proportional-Integral (PI) gains of the voltage controller are optimized in a real-time basis after a perturbation in the microgrid to have a fast and smooth response and a more stable system. The current controller produces Space Vector Pulse Width Modulation command signals to be fed into the three-leg inverter. The multi-objective optimization problem has objective functions of voltage overshoot/undershoot, rise time, settling time, and Integral Time Absolute Error (ITAE). The modified Multi-Objective Hybrid Big Bang-Bing Crunch (MOHBB-BC) algorithm is employed as one of efficient evolutionary algorithms in order to solve the optimization problem. The MOHBB-BC method obtains a set of Pareto optimal solutions; a fuzzy decision maker is used to pick up the most preferred Pareto solution as the final solution of the problem. Results from testing the control strategy on a case study are discussed and compared with previous works; according to them, the proposed method is able to obtain dynamic PI regulator gains to have a more appropriate response.     

Identification and Control of Multivariable Stochastic Feedback Systems

Abstract

This paper considers the problem of simultaneous identification and control of stochastic processes characterized by linear dynamic models with unknown systems parameter coefficients. Stochastic approximation is used to derive consistent identification algorithms for the case in which arbitrary feedback controls are present. These identification methods can also be used for determining the order of the system, if the latter is unknown, as well as the exact canonical structure for the multivariable case.

An approximation to the optimal control solution is obtained by explicitly separating the functions of identification and control, and asymptotic convergence to a stochastic optimal controller is attained without on-line structural modification.     

Robust jump linear quadratic control: A mode stabilizing solution

This correspondence is concerned with optimal feedback control of a class of linear stochastic systems. For a quadratic performance index the solution to this problem is known but the resulting optimal regulator does not ensure stability in every mode of operation. To improve robustness, a new mode stabilizing solution is defined. An example illustrates the advantages of the proposed controller.     

Discrete-time optimal control for stochastic nonlinear polynomial systems

This paper presents a solution to the discrete-time optimal control problem for stochastic nonlinear polynomial systems over linear observations and a quadratic criterion. The solution is obtained in two steps: the optimal control algorithm is developed for nonlinear polynomial systems by considering complete information when generating a control law. Then, the state estimate equations for discrete-time stochastic nonlinear polynomial system over linear observations are employed. The closed-form solution is finally obtained substituting the state estimates into the obtained control law. The designed optimal control algorithm can be applied to both distributed and lumped systems. To show effectiveness of the proposed controller, an illustrative example is presented for a second degree polynomial system. The obtained results are compared to the optimal control for the linearized system.     

H Optimal control for linear stochastic systems

The aim of the present paper is to provide an optimal solution to the H² state-feedback and output-feedback control problems for stochastic linear systems subjected both to Markov jumps and to multiplicative white noise. It is proved that in the state-feedback case the optimal solution is a static gain which is also optimal in the class of all higher-order controllers. In the output-feedback case the optimal H² controller has the same order as the given stochastic system. The realization of the optimal controllers depend on the stabilizing solutions of some appropriate systems of Riccati-type coupled equations. An effective iterative convergent algorithm to compute these stabilizing solutions is also presented. The paper gives some illustrative numerical example allowing to compare the results obtained by the proposed design approach with the ones presented in the recent control literature.