Maximum likelihood-based adaptive differential evolution identification algorithm for multivariable systems in the state-space form

Parameter estimation plays an important role in the field of system control. This article is concerned with the parameter estimation methods for multivariable systems in the state-space form. For the sake of solving the identification complexity caused by a large number of parameters in multivariable systems, we decompose the original multivariable system into some subsystems containing fewer parameters and study identification algorithms to estimate the parameters of each subsystem. By taking the maximum likelihood criterion function as the fitness function of the differential evolution algorithm, we present a maximum likelihood-based differential evolution (ML-DE) algorithm for parameter estimation. To improve the parameter estimation accuracy, we introduce the adaptive mutation factor and the adaptive crossover factor into the ML-DE algorithm and propose a maximum likelihood-based adaptive differential evolution algorithm. The simulation study indicates the efficiency of the proposed algorithms.     

Data structure and algorithms for fast automatic differentiation

In this paper we discuss the data structure and algorithms for the direct application of generalized Leibnitz rules to the numerical computation of partial derivatives in forward mode. The proposed data structure provides constant time access to the partial derivatives, which accelerates the automatic differentiation computations. The interaction among elements of the data structure is explained by several numerical examples. The paper contains analysis of the developed data structure and algorithms. Copyright © 2003 John Wiley & Sons, Ltd.     

线性二次型最优控制闭环系统极点的必要条件

本文推导了线性二次型（LQ）最优控制闭环系统极点应满足的必要条件，以及与加权阵Q和R的等式关系     

具有减少辨识参数的随机多变量极点配置自适应控制器

本文提出了一种新的限制输出个数减少随机多变量自适应控制中辨识参数的方法，并给出了减少辨识参数的极点配置自适应算法。虽然采用ｎ个输入１个输出的减少辨识参数的模型来设计控制器，但所提出的控制器能够保证被控系统的几个输出跟踪参考输入信号，仿真结果表明，所提出的方法是成功的。     

Issues in nonlinear stochastic grey box identification

Grey box identification refers to the practice of identifying dynamical systems in model structures exploiting partial prior information. This contribution reviews a method for stochastic grey box identification and surveys experiences and lessons of applying it to a number of industrial processes. Issues to be addressed include advantages and costs of introducing stochastics into the model, the question of what contribution must be expected from the model designer as opposed to what can be formalized in computer algorithms, and an outlook on future plans to resolve present shortcomings.     

多变量系统故障诊断和容错控制新方法及其在精馏过程中的应用

本文提出一种多变量系统的故障诊断和容错控制新方法，整个系统由四个神经元组成。将本文方法用于二元精馏塔顶和塔底组分的容错控制，仿真结果证实了本文方法的有效性。该方法可望在难以建模的复杂化工过程中得到应用。     

Experimental evaluation of a multivariable adaptive nonlinear predictive controller

A new multivariable adaptive nonlinear predictive controller is designed using a general nonlinear input-output model and variable transformations. The controller is similar in form to typical linear predictive controllers can be tuned analogously or by specifying a single parameters for each controlled variable. In addition, the design procedure is computationally efficient. The new controller is compared to a multi-loop proportional-integral (PI) controller with one-way static decoupling and to an adaptive linear predictive controller through tests on a simulated nonlinear distillation column. The new controller performed well in an experimental application to a multicomponent distillation column.     

Convolution profiles for right inversion of multivariable non-minimum phase discrete-time systems

The problem of the non-causal inversion of linear multivariable discrete-time systems is analyzed in the geometric approach framework and is solved through the computation of convolution profiles which guarantee perfect tracking under the assumption of infinite-length preaction and postaction time intervals. It is shown how the shape of the convolution profiles is related to both the relative degree and the invariant zeros of the plant. A computational setting for the convolution profiles is derived by means of the standard geometric approach tools. Feasibility constraints are also taken into account. A possible implementation scheme, based on a finite impulse response system acting on a stabilized control loop, is provided.     

多变量加权多步预报控制*

对于线性多变量系统,本文给出了一种完全不同于以往的全状态反馈或观测器-控制器型的算法——多变量加权多步预报控制(MWLPC)算法。这种算法除引进了预测控制中的多步输出预报、滚动优化等机制外,最重要的是在二次型性能指标中引入了可调的多项式或有理分式矩阵权因子;适当选取这些权因子,便可按设计要求、仅用系统的输出信息反馈便能任意配置闭环系统的特征矩阵,从而保证闭环稳定性和其他优良性质。此外,该算法不改变原系统的零点,因而适用于非最小相位系统。     

预测控制算法的统一格式及其在电加热炉的应用

本文根据常用的两类预测控制算法推出一种新型预测控制算法。并将其应用于一双输入/双输出的力学持久机电加热炉的温度控制系统上,获得了满意的结果:控温精度为±1.5℃/300℃,且适用于非最小相位系统。