LQ最优控制系统中加权阵的确定

本文研究了LQ最优调节器的逆问题.在控制变量加权矩阵R给定的条件下,通过引入 一组自由变量,给出了满足闭环系统特征值要求的状态加权矩阵Q的一种参数化表示结果.基 于这种结果,研究了LQ逆问题的矩阵变换解法和一类系统的LQ逆问题的解法.此外,文中 还给出了不求解代数矩阵Riccati方程确定系统的最优状态反馈系数矩阵K的方法.     

LQ逆问题解的一种有效算法

本文研究了LQ最优控制逆问题解的参数化表示结果和基于这一参数化表示结果的矩阵变换解法。研究的对象是线性时不变离散时间系统。此外,文中还给出了不求解代数矩阵Riccati方程确定系统的最优状态反馈系数矩阵K的方法。     

一种新的最优控制系统设计方法

本文采用LQ逆问题方法得到了一种新的最优控制系统设计方法，推导了线性二次型性能指标中的加权矩功Q与开环特征多项式，最优闭环特征多项式之间的关系。并研究LQ逆问题解的存在性和唯一性问题。只要给定期望的闭环极点，即可确定与之对应的加权矩阵Q，从而获得一个具有指定闭环极点的最优控制系统。     

一种新的离散系统最优闭环极点配置方法

分析了LQ逆问题解的存在条件,以便合理选择期望的闭环极点,使之成为一组最优极点．提 出了一种以离散系统LQ逆问题分析为基础的新的最优控制系统设计方法,得到了开环、闭环 特征多项式系数与加权矩阵之间的解析关系,只要给定一组期望闭环极点,即可确定与 之对应的加权矩阵Q和R,从而得到一个具有指定极点的最优控制系统．     

LQ最优控制之逆问题的研究

本文通过适当地选取LQ性能指标函数中的加权矩阵R,给出了该二次型性能指标函数中的另一个加权矩阵Q与系统的开环特征多项式、闭环特征多项式的系数以及系数的系数矩阵A、B之间的对应关系。如果给定一个系统以及该系统的一组最优闭环极点,就可以求得矩阵Q。同时,用本文的研究结果,还可以直接确定系统的最优状态反馈系数矩阵。     

基于MATLAB的LQR控制器设计方法研究

采用LQ校正、参考输入及状态观测器的设计方法来设计最优二次控制器,选取加权矩阵Q和R使控制器的性能达到最优.从介绍代数Riccali方程求解着手,利用MATLAB的强大计算功能及仿真能力,不断的调整参数得到设计结果并画出系统的输出响应曲线.很多文献介绍了基于输出反馈的PID控制系统,但其控制效果不理想,主要原因是系统的高阶次和多变量.本文采用基于状态空间设计法的LQR最优调节器,较好地兼顾了系统的鲁棒稳定性和快速性,倒立摆的实例说明了该方法的有效性.     

一种新的最优极点配置方法

本文从LQ逆问题着眼提出了一种新的最优极点配置方法,推导了加权矩阵Q和R与开环特征多项式、最优闭环特征多项式之间的关系。只要给定一组期望的闭环极点,即可确定与之对应的加权矩阵Q和R,从而得到一个具有指定极点的最优控制系统。     

离散系统LQ逆问题研究

本文研究了离散系统LQ逆问题解的存在性问题，给出了逆问题解的参考化公式，了加权矩阵与开环，最优闭环特征多项式系数之间的关系，只要给定一组期望的闭环极点，即可确定与之对应的加权矩阵，并且不必求解复杂的Riccati方程也可直接得到满足极点配置的要求的记反馈增益矩阵。     

具有相对稳定性的状态反馈控制算法

本文提出了计算状态反馈矩阵的新方法。它不需要了解原开环系统的固有特性,只要任意指定两个矩阵,就能够给出满足相对稳定性要求的状态反馈矩阵。从而,我们较好地解决了线性系统理论中的这一问题。此外,文中还讨论了线性二次型(LQ)控制的逆问题,给出了求解相应的加权矩阵的公式。     

线性二次型指标下的最优输出反馈问题

本文研究了线性二次型指标下的最优输出反馈问题。根据系统参数,在二次型指标中适当选择状态加权矩阵Q可以将LQ问题的状态反馈解表成输出反馈的形式。文中给出了这种输出反馈解存在的充分性条件。     

Robust exponential stability conditions for retarded systems with Lipschitz nonlinear stochastic perturbations

This paper investigates robust mean‐square exponential stability of a class of uncertain stochastic state‐delayed systems with Lipschitz nonlinear stochastic perturbation. Based on Lyapunov–Krasovskii functional (LKF) method and free‐weighting matrix technique, some new delay‐dependent stability conditions are established in terms of linear matrix inequalities (LMIs). In order to reduce the conservatism, (1) the delay is divided into several segments, i.e. the delay decomposition method is applied; (2) cross terms estimation is avoided; (3) some information of the cross terms relationships which has not been involved in Reference (IET Control Theory Appl. 2008; 2(11):966–973) is considered. Moreover, from the mathematical point of view, the results obtained by free‐weighting matrix technique can be equivalently re‐formulated by simpler ones without involving any additional free matrix variables. The effectiveness of the method is demonstrated by numerical examples. Copyright © 2010 John Wiley & Sons, Ltd.     

Butterworth最优控制的逆问题

为了认识Butterworth最优控制的本质,揭开Butterworth最优传递函数与加权矩阵Q,R的相互关系,本文研究Butterworth最优控制的逆问题.首先用Butterworth最优控制确定状态反馈增益阵K,然后给出计算加权矩阵Q的参数化公式,最后用一个例子说明这种确定加权矩阵Q,R的方法的有效性和简便性.     

Design of the multi‐objective constrained nonlinear robust excitation controller with extended Kalman filter estimates of all state variables

A new optimal back‐stepping robust adaptive control method for the military moving power station (MMPS) excitation system is proposed in this paper. Through the extended Kalman filter estimates of the state variables, the tracking of the operating point, and the back‐stepping technique, the proposed controller has been shown to improve system robustness to disturbances and dynamic uncertainties and minimise the effect of disturbances by solving the linear matrix inequality to obtain the optimal control law on all operating points. The simulation and experimental results show that the proposed control strategy can enhance the transient stability of the MMPS excitation system more effectively than other methods and can optimise the convergence rates of the state variables by modifying the values of the weighting matrices. Moreover, the terminal voltage of the MMPS can be sampled quickly by alternating current (AC) tracking comparison. Copyright © 2013 John Wiley & Sons, Ltd.     

Receding Horizon Controls for Input-Delayed Systems

This paper presents a receding horizon control (RHC) for an unconstrained input-delayed system. To begin with, we derive a finite horizon optimal control for a quadratic cost function including two final weighting terms. The RHC is easily obtained by changing the initial and final times of the finite horizon optimal control. A linear matrix inequality (LMI) condition on two final weighting matrices is proposed to meet the cost monotonicity, under which the optimal cost on the horizon is monotonically nonincreasing with time and hence the asymptotical stability is guaranteed only if an observability condition is met. It is shown through simulation that the proposed RHC stabilizes the input-delayed system effectively and its performance can be tuned by adjusting weighting matrices with respect to the state and the input.      

LMI-based H ∞-optimal control with transients

In this article, the worst-case norm of the regulated output over all exogenous signals and initial states as a performance measure of the system is characterised in terms of linear matrix inequalities (LMIs). Optimal time-invariant state- and output-feedback controllers are synthesised as minimising this performance measure. The essential role in this synthesis plays a weighting matrix reflecting the relative importance of the uncertainty in the initial state contrary to the uncertainty in the exogenous signal. H _∞-optimal control with transients is shown to be actually a trade-off between H _∞-control, being optimal under unknown exogenous disturbances and zero initial state, and γ-control, being optimal under zero exogenous signal and unknown initial conditions, if and only if the weighting matrix satisfies a fundamental inequality. If this inequality is met, the performance measure is achieved and the explicit formulae for the worst-case disturbance and initial state are provided. If this inequality fails, the performance measure coincides with the H _∞-norm and the trade-off gets broken.     

Sensitivity design of optimal linear systems†

A measure of sensitivity is introduced into the performance index of the optimal linear system. The sensitivity vector is adjoined to the system state vector and the overall system is optimized. A procedure for selecting the weighting matrix elements is given. Finally a design procedure for obtaining approximately optimal feedback controllers with bounds on sensitivity is given. The methods are illustrated with a first-order example.     

Linear matrix inequalities, Riccati equations, and indefinitestochastic linear quadratic controls

This paper deals with an optimal stochastic linear-quadratic (LQ) control problem in infinite time horizon, where the diffusion term in dynamics depends on both the state and the control variables. In contrast to the deterministic case, we allow the control and state weighting matrices in the cost functional to be indefinite. This leads to an indefinite LQ problem, which may still be well posed due to the deep nature of uncertainty involved. The problem gives rise to a stochastic algebraic Riccati equation (SARE), which is, however, fundamentally different from the classical algebraic Riccati equation as a result of the indefinite nature of the LQ problem. To analyze the SARE, we introduce linear matrix inequalities (LMIs) whose feasibility is shown to be equivalent to the solvability of the SARE. Moreover, we develop a computational approach to the SARE via a semi-definite programming associated with the LMIs. Finally, numerical experiments are reported to illustrate the proposed approach     

Some facts about the choice of the weighting matrices in Larimore type of subspace algorithms

In this paper the effect of some weighting matrices on the asymptotic variance of the estimates of linear discrete time state space systems estimated using subspace methods is investigated. The analysis deals with systems with white or without observed inputs and refers to the Larimore type of subspace procedures. The main result expresses the asymptotic variance of the system matrix estimates in canonical form as a function of some of the user choices, clarifying the question on how to choose them optimally. It is shown, that the CCA weighting scheme leads to optimal accuracy. The expressions for the asymptotic variance can be implemented more efficiently as compared to the ones previously published.