闭环变量带误差系统的控制器设计

针对目前大多数系统的研究中,只考虑输出被误差干扰的情况,提出了一种闭环变量带误差系统模型(输入和输出信号均被噪声干扰),并对该系统的控制器进行设计。采用最小方差控制来对控制器进行设计,设计完成最小方差控制器以及自校正最小方差控制器。然后应用最小方差性能指标来评估控制器性能,并通过仿真验证控制器性能。     

Controller performance assessment based on minimum and open-loop output variance

The need to achieve reliable and effective automatic control systems has directed numerous research efforts towards the development of monitoring and assessment methods of the closed-loop performance. A new controller assessment index, called relative variance index (RVI) is introduced in this work, according to which the closed-loop performance is compared with the best theoretical control action (minimum variance control) and no control action. For the estimation of RVI, an identification method is employed to extract the system models and an estimate of the process unit delays. The proposed index, RVI, is in agreement with the classical deterministic closed-loop assessment measures and thus can be utilized for the controller performance evaluation more effectively.     

Theoretical justification of approximate norm minimization method for intelligent digital redesign

This paper investigates the theoretical justification of intelligent digital redesign (IDR) methods based on the approximate discrete-time models. Its main purpose is to design the digital fuzzy controller achieving the minimum norm distance between the trajectories of the approximate closed-loop systems under the analog and the digital controls. It is verified that, if there exist the digital gains such that the approximate norm distance is sufficiently small, the redesigned controller makes the trajectories of the exact closed-loop systems close, or, at least, asymptotically stabilizes the plant.     

H<Subscript>2</Subscript>-optimal tuning algorithms for fixed structure controllers

An H₂-method of optimal tuning is proposed for a fixed order controller. The SISO plant model is considered in state space. The H₂-method of tuning parameter design is based on the minimization of a transient process closeness criterion for appropriate open-loop and closed-loop control systems and their reference models. The controller tuning algorithms use the plant parameter estimations obtained during the plant parameter identification. The analytical expressions are obtained for the square of H₂-norm of a stable dynamic system. The following theorem is proven: the minimum necessary conditions for the functionals of transfer function H₂-norm of open-loop and closed-loop systems are the same as the minimum necessary conditions for the Frobenius norm of the controller parameter tuning polynomial.     

Finite-dimensional control of linear parabolic systems with unbounded output operators

In this paper, we studyH control of linear parabolic systems with unbounded output operators. Our aim is to construct a finite-dimensional stabilizing controller for the linear parabolic system that makes the H norm of the closed-loop transfer function less than a given positive number delta. To this end, we first derive a finite-dimensional reduced-order system for the linear parabolic system. Then, for the reduced-order model, we construct a stabilizing controller that makes theH norm of the closed-loop transfer function less than another positive number. It is proved that the finitedimensional controller together with a residual mode filter plays a role of a finite-dimensional stabilizing controller that makes theH norm of the closed-loop transfer function less than delta for the original linear parabolic system, if the order of residual mode filter is chosen sufficiently large.     

Tuning of Gaussian stochastic control systems

A closed-loop system consisting of a control system and an adaptive controller is called tuning for a specified control objective if the real system and the ideal system defined below achieve the same value for the control objective. The real system is the system consisting of the unknown control system in closed loop with the adaptive controller in which the parameters of the adaptive controller have been determined by identification under feedback or in closed loop. The ideal system is the system consisting of the unknown control system in closed loop with a controller in which the controller has been synthesized with knowledge of the unknown control system and such that the closed-loop system satisfies the control objective. Both the Gaussian stochastic control system with full observations and with partial observations are considered. The approach to the problem is based on stochastic realization theory for Gaussian systems. The control objectives of minimum variance control and pole placement are also given. Necessary conditions for tuning are discussed     

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.     

PID控制器的性能监控与评估

为了解决目前工业系统中普遍使用的PID控制器由于工况变化等原因引起的系统发生时变而导致所在回路PID控制器性能可能下降的问题,本文提出了一种专门针对PID控制器进行性能评估、优化及监控的方法,即:PID循环评估优化算法。该算法利用系统闭环输入输出数据,使用基于MVC(minimum variance control)的PID最小方差准则,来对PID控制器的性能进行评估,并且计算出在最小方差意义下最优PID控制器参数;评估过程结果与现实系统输出方差进行比较,做为PID参数在线优化的判断依据,当现实系统性能低于某一标准的时候对控制器进行优化处理。在整个算法中,通过输入输出数据的处理与判断,利用评估优化后的PID参数对系统进行控制,并再次回到最初的输入输出数据的处理和判断过程,实现在控制过程中的系统性能监控。本文的计算机仿真试验验证了该方法的有效性,由图可看出,系统发生渐变和突变后,当输出方差超过了程序限定的标准时,在1 300秒内系统能自动评估并施行优化而达到稳定。该循环评估优化算法现实了在对系统进行性能评估监控的同时,能按照一定条件作为标准对系统的PID参数进行优化,最终使得系统具有自我监控评估和自我优化的能力...     

基于参数辨识的自适应解耦控制算法的研究

多变量模型的复杂结构、强耦合性、被控对象参数的未知、慢时变等问题要求控制器必须具有良好的自适应性,针对以上问题提出了一种基于改进的广义最小方差闭环自适应解耦控制器实现更好的自适应,其由参数可调的控制器和自适应控制律组成,此控制器通过将闭环系统方程的传递函数矩阵等于期望的对角矩阵来实现解耦,同时改进的辨识算法可进行在线辨识控制器的参数实现同步自适应解耦。通过以CARMA为多变量控制模型,采用该方法进行仿真有效的解决了多变量之间的耦合性。结果表明该方法能够适应相应的变化,跟踪性能较好,且具备良好的解耦能力,进而保证了闭环系统的稳定性,从而验证了此方法能够效提高控制系统的稳定性和鲁棒性。     

基于小波网络的一类非线性系统稳定最小方差控制

研究一类非线性系统的最小方差控制．将非线性系统等价表示为时变线性系统，利用小波网络的非线性逼近特性在线辨识时变系数，利用改进的投影算法在线调整小波网络的权值；在此基础上设计了非线性系统的最小方差控制器，并分析了闭环控制系统的稳定性．仿真结果表明了该算法的有效性．     

SISO线性系统的滑模变结构状态范数控制

在线性系统滑模控制理论的基础上,给出了对于一般SLSO线性系统状态范数控制的形式,提出了状态范数的控制器结构。基于李亚普诺夫稳定性理论证明了状态范数控制系统的闭环稳定性。然后分别针对状态范数控制器与趋近率控制和单位向量控制进行了数值仿真的分析与比较,结果说明状态范数控制综合了趋近率控制和单位向量控制各自的优点,控制信号抖振更小,闭环系统稳态性能有了一定改进。     

Globally convergent multivariable adaptive decoupling controller and its application to a binary distillation column

This paper describes a new multivariable adaptive decoupling controller combining a decoupling compensator with a generalized minimum variance technique. The controller can completely decouple closed-loop systems both dynamically and in the steady state. It can control an unstable and/or non-minimum phase system and it can control the process with an arbitrary time delay structure. The proof of global convergence for the algorithm is also given. Successful experimental results of the adaptive decoupling control for a pilot-scale binary distillation column demonstrate the effectiveness of the proposed adaptive decoupling algorithm.     

An algorithm for minimizing the norm of state feedback controllers in eigenvalue assignment

Minimization of the norm of parametric feedback controllers which assign desired eigenvalues to the closed-loop system is achieved by implementation of vector companion forms described in an earlier paper. The simple algorithm which emerges gives insight into the mechanism by which parametric eigenvalue assignment is affected. A couple of numerical examples are presented to illustrate advantages of this algorithm which easily computes the controller gain matrix with minimum norm.     

Identification for control: Optimal input intended to identify a minimum variance controller

It is well known that the quality of the parameters identified during an identification experiment depends on the applied excitation signal. Prediction error identification using full order parametric models delivers an ellipsoidal region in which the true parameters lie with some prescribed probability level. This ellipsoidal region is determined by the covariance matrix of the parameters. Input design strategies aim at the minimization of some measure of this covariance matrix. We show that it is possible to optimize the input in an identification experiment with respect to a performance cost function of a closed-loop system involving explicitly the dependence of the designed controller on the identified model. In the present contribution we focus on finding the optimal input for the estimation of the parameters of a minimum variance controller, without the intermediate step of first minimizing some measure of the model parameter accuracy. We do this in conjunction with using covariance formulas which are not asymptotic in the model order, which is rather new in the domain of optimal input design. The identification procedure is performed in closed-loop. Besides optimizing the input power spectrum for the identification experiment, we also address the question of optimality of the controller. It is a wide belief that the minimum variance controller should be the optimal choice, since we perform an experiment for designing a minimum variance controller. However, we show that this may not always be the case, but rather depends on the model structure.     

Synthesis of anisotropic suboptimal control for linear time-varying systems on finite time horizon

This paper presents the statement and solution to the problem of synthesis of the anisotropic control that guarantees some prescribed level of attenuation of the uncertain stochastic disturbances affecting the linear discrete time-varying system on finite time horizon. The anisotropy of a random vector is considered as a measure of the statistical uncertainty of the disturbance. The closed-loop system capabilities to attenuate the external disturbances are characterized by its anisotropic norm. The synthesis problem solution is formulated in form of sufficient conditions of existence of a controller that guarantees the anisotropic norm of the closed-loop system to be bounded by some given threshold level. The controller synthesis algorithm is based on solving the system of matrix inequalities recursively.     

Minimum variance performance map for constrained model predictive control

A minimum variance performance map is introduced for constrained linear model predictive control (MPC). The minimum variance performance map provides a demonstration of the effect of constraints in an MPC on the best achievable controller performance. The constrained minimum variance controller is formulated for the MPC system to be monitored. Using multi-parametric quadratic programming (mp-QP), the linear, piecewise control law is obtained for the constrained minimum variance controller. The linear, piecewise control law is used with a Kalman filter to obtain the minimum output variance in each region of the state space partition. The minimum variance performance map is demonstrated on a second order process with a constraint on the input amplitude.     

基于LMI不确定离散随机系统输出反馈保性能控制

针对一类范数有界不确定离散随机系统,研究了具有输出反馈控制器的保性能控制,由此得到闭环系统的二次型性能指标上界的一般形式;然后应用LMI凸优化技术,推导出使闭环系统渐近稳定的最优保性能控制器的设计方法;最后通过仿真算例验证了所提出算法的可行性和有效性.     

基于Riccati方程和LMI算法的H∞控制器的设计与实现

H∞控制是一种重要的鲁棒控制方法,它以H∞范数作为控制性能指标,是一种最优控制方法,目的是求出系统内部稳定的控制器,使闭环传递函数的无穷范数极小,达到控制的目的。以固高公司的直线一级倒立摆为控制对象,实现基于Riccati方程和LMI算法的H∞控制器设计,采用M文件及simulink实现系统建模、控制器的设计,完成系统算法的验证,实验表明,控制器的输出、倒立摆系统的状态变量变化平稳,系统具有较强的鲁棒性,系统表现出良好的动态品质,验证了H∞控制器的有效性。     

On the relationship between controllability indexes, Eigenvector assignment, and deadbeat control

The subspaces, to which closed-loop generalized eigenvectors are restricted, are described in terms of the controllability indexes of the pair (A, B) and the polynomials of minimal degree that span ker[A- lambda I, B]. This characterization of the eigenspaces is then used to calculate the deadbeat controller of minimum Frobenius norm.     

基于数据驱动的控制器综合性能优化

为了提高过程回路的控制性能,提出一种基于数据驱动的控制器综合性能优化方法.首先,通过分析虚拟参考反馈校正方法与内模控制器设计过程的联系,确定数据驱动控制器校正所需参考模型的结构;然后,定义一种结合绝对误差积分和最小方差性能基准的综合性能指标,再基于该指标确定参考模型的未决参数;最后,利用控制系统闭环数据实现对控制器参数...