多变量系统控制器的参数满意优化设计

为了将满意优化拓展到多变量系统中以解决多变量控制系统线性二次型(LQ)控制器设计中加权系数阵确定难的问题,提出多变量系统满意优化设计方法,通过设计满意度函数,构造出多变量系统的满意优化数学模型,并用改进遗传算法实现二级倒立摆系统LQ控制器满意优化设计.仿真结果显示,系统具有更满意的综合性能指标.证实了该方法的有效性和实用性.本文的研究对多变量系统优化设计具有一定的参考价值.     

单通道设计方法在工业锅炉多变量燃烧控制系统中的应用

介绍了采用单通道设计（ＩＣＤ）方法进行了工业锅炉多变量燃烧控制系统的设计,并对控制系统进行了仿真研究。结果表明,采用单通道（ＩＣＤ）方法设计的工业锅炉多变量燃烧控制系统具有良好的控制品质,同时,系统独立是个非线性且易于工程帝现。     

多变量系统近似模型设计方法

本文介绍了多变量系统近似模型设计方法和相应鲁棒性稳定判据.并在 IBM—PC 微机上,用 FORTRAN 语言实现了计算机的辅助设计.用该方法对大型加热炉控制系统进行鲁棒性设计.结果表明,设计的系统具有稳定鲁棒性和良好的性能鲁棒性.     

基于多变量广义预测控制的不稳定系统控制结构选择方法

对于多输入多输出系统, 在控制系统设计时首先要对被控变量和操纵变量进行控制结构选择. Bristol提出的相关增益矩阵(Relative gain array, RGA)法, 以及学者们后来提出的各种改进方法, 都只适用于稳定系统. 本文针对不稳定系统, 基于多变量广义预测控制(Generalized predictive control, GPC)的闭环控制律提出了一种控制结构的变量匹配准则. 通过对预测时域、控制时域等各个参数的优化选择, 使系统闭环稳定; 由闭环控制律得到被控变量期望值与操纵变量的相关性矩阵, 以此得出控制结构的变量配对方案. 通过实例研究表明, 对于开环不稳定系统, 该方法可以得出正确的变量配对结果.     

考虑有限字长影响的离散时间非脆弱H∞滤波

本文研究了线性离散时间系统由有限字长影响的非脆弱滤波问题. 假定所设计的滤波器具有加性增益变量, 此增益变量反映了滤波器执行时的有限字长的影响. 为了解决该非脆弱滤波问题, 提出了一个结构的顶点分离器的概念, 并采用该分离器, 基于线性矩阵不等式给出了非脆弱滤波器设计的充分条件. 设计的结果使得误差系统渐进稳定且具有一定的性能水平. 数值例子验证了方法的有效性.     

多变量控制系统CAD软件包及其应用

以多变量频域法中的逆Ｎｙｑｕｉｓｔ阵列法为基础，开发了设计多变量控制系统的软件包，利用该软件包设计了直流锅炉控制系统。该系统结构简单、工程应用方便、调节品质良好、具有较好的鲁棒性，为多变量控制系统设计提供了一种实用有效的方法。     

基于连续Hopfield网络的多变量时变系统最优控制

针对多变量时变系统提出了一种基于连续Hop fie ld网络的最优控制系统设计方法.该方法不仅从理论上建立了移动时域上的LQ性能指标与连续Hop fie ld网络能量函数间的等价关系,并在此基础上设计出可求解LQ最优控制问题的连续Hop fie ld网络,而且将滚动优化控制策略引入控制系统,形成了包括连续Hop fie ld网络在内的闭环控制结构,实现了多变量时变系统无限域上的动态最优控制.仿真结果验证了该设计方法的有效性.     

控制系统多项式矩阵方法软件包的设计

本文介绍了矩阵形式描述的单变量和多变量控制系统动态补偿器的计算机辅助设计(简称CAD)软件系统。本软件共包括了十种动态补偿器的设计,用户可不必了解其中控制算法的细节及程序结构,便能很快设计出符合要求的控制系统,并可对设计的系统进行仿真运算,给出数值结果和图形显示结果。本软件包括了控制系统多项式矩阵设计理论方面研究的许多新成果,全部程序均用C语言编写。     

一类飞行器的多变量鲁棒控制

对一类飞行器建立了数学模型并进行了分析,使用非最小相位控制系统的智能设计方 法,设计了飞行器的多变量鲁棒控制系统.基于真实模型的控制系统仿真证实了这一设计方 法的有效性.该设计方法对航空航天控制工程具有参考意义.     

基于粒子群优化的非线性系统最小二乘支持向量机预测控制方法

对于非线性系统预测控制问题, 本文提出了一种基于模型学习和粒子群优化(PSO)的单步预测控制算法.该方法使用最小二乘支持向量机(LS-SVM)建立非线性系统模型并预测系统的输出值, 通过输出反馈和偏差校正减少预测误差, 由PSO滚动优化获得非线性系统的控制量. 该方法能在非线性系统数学模型未知的情况下设计出有效的预测控制器. 通过对单变量多变量非线性系统进行仿真, 证明了该预测控制方法是有效的, 且具有良好的自适应能力和鲁棒性.     

A comparative study of recent relay autotuning methods for multivariable systems

A typical procedure for designing multivariable controllers is the following: build a model for the multivariable process, choose the control structure, calculate the control parameters, test the controller (possibly with simulation) and then retune controller parameters as necessary. This procedure is complex and time consuming even for scalar control loops. For multivariable controllers, the procedure is even more daunting. Automation of the design method is and has been a concern of many researchers. There has been a large number of papers on relay autotuning of control systems. The choice of relay feedback to solve the design problem is justified by the possible integration of system identification and control into the same design strategy, giving birth to relay autotuning. In this paper, nine different relay autotuning methods for multivariable systems are compared. Most of these methods have common basics but they may differ in the tuning procedure, convergence, identification method, control structure and performance achievement. The paper summarizes these methods and investigates the advantages and drawback of each algorithm.     

An index of applicability for the decomposition method ofmultivariable fuzzy systems

Research on the application of fuzzy set theory to the design of control systems has led to interest in decomposition of multivariable fuzzy systems. Decomposition of multivariable control rules is preferable since it alleviates the complexity of the problem, but the inference error is inevitable because of its approximate nature. In this paper we show that a large inference error is generated when the Gupta's decomposition method (1986) is applied to Exclusive-nor (ENOR) gate model which is used as a counter example. We define an index of applicability which can classify whether the decomposition method can be applied to a multivariable fuzzy system or not     

A DESIGN OF MULTILOOP PID CONTROLLERS WITH A NEURAL‐NET BASED DECOUPLER

In process industries, PID control schemes have been widely used due to their simple structures and easiness of comprehending the physical meanings of control parameters. However, the good control performance cannot be obtained by simply using PID controlschemes, since most processes are considered as nonlinear multivariable systems with mutual interactions. In this paper, a design method of multiloop PID controllers neural‐net based decoupler is proposed for nonlinear multivariable systems with mutual interactions. The proposed method consists of a decoupler given by the sum of a static decoupler and a neural‐net based decoupler, and multi‐loop PID controllers. Finally, the effectiveness of the proposed control scheme is evaluated on the simulation examples.     

A fuzzy basis function vector-based multivariable adaptivecontroller for nonlinear systems

In this paper, a new fuzzy basis function vector (FBFV) approach for the adaptive control of multivariable nonlinear systems is presented. With this method, the nonlinear plant is first linearized. The linearized bias and uncertainties as well as disturbances are assumed to be included in the model structure and their upper bound will be adaptively learned by the FBFV method. The output of the FBFV is used as the parameters of the robust controller in the sense that both the robustness and the asymptotic error convergence can be obtained for the multivariable nonlinear system. The effectiveness of the proposed analysis and design method is illustrated with a simulated example.     

不确定多变量系统的高阶滑模控制

针对一类系统矩阵和输入矩阵存在不确定性的多变量系统,结合微分估计器技术和极点配置技术,提出一种二阶非奇异终端滑模分解控制方法.所提方法适用于维数较高系统,可简化控制器设计,消除控制信号的高频抖振,实现系统的鲁棒递阶控制.仿真结果验证了所提方法的有效性.     

QFT prefilter design for multivariable systems using interval constraint satisfaction technique

In this paper, a computationally efficient method is proposed for automated design of the prefilters for multivariable systems. In quantitative feedback theory （QFT） method, proposed by Horowitz, the prefilter is designed to achieve the desired tracking specifications. In the proposed approach, we pose the prefilter design problem as an interval constraint satisfaction problem and solve it using the well-established interval constraint satisfaction techniques. The proposed method finds optimal values of the parameters of fixed structure prefilter within the initial search domain. An approach based on prefilter synthesis for single-input single-output is already developed. The purpose of this paper is to extend this approach to QFT prefilter design for general multivariable systems. To validate the above design approach, we applied the method to a laboratory setup of magnetic levitation system.     

Robust control of multivariable critical systems

Based on the method of inequalities and H _∞-optimization method, this paper develops an approach to robust control design of multivariable critical systems with external and internal uncertainties. In this approach the formulation of the robust control design of these systems is expressed by a set of inequalities which includes output performance criteria in the time domain and a robust performance criterion in the frequency domain of the system. Some relationships between an input space, a modelling error space, a controller, output performance and robust performance are established by inequalities for SISO and MIMO critical systems so that the robust control design problem of these systems is largely simplified.     

具有完整性的最优控制系统设计*

本文利用Lyapunov方程和Riccati方程,提出一种多变量控制系统设计法。所设计的系统具有完整性、最优性和良好的动特性,并对系统参数变化具有鲁棒性。     

Non-diagonal H∞ weighting function design: Exploiting spatio-temporal deformations in precision motion control

Model-based control design requires a careful specification of performance and robustness requirements. In typical norm-based control designs, performance and robustness requirements are specified in a scalar optimization criterion, even for complex multivariable systems. This paper aims to develop a novel approach for the formulation of this optimization criterion for multivariable motion systems that exhibit spatio-temporal deformations. To achieve this, characteristics of the underlying system are exploited to design multivariable weighting functions. In contrast to pre-existing approaches, which typically lead to diagonal weighting functions, the proposed approach enables the design of non-diagonal weighting functions. Extensive experimental results confirm that the proposed procedure can significantly improve the performance of an industrial motion system compared to earlier approaches.