一种多变量系统分散优化DMC算法

提出一种多变量系统的分散优化动态矩阵控制（ＤＭＣ）算法,该处冯动 经的特点,将多变量系统ＤＭＣ算法分散为若干单变量系统的ＤＭＣ算法,使多变量ＤＭＣ算法参数设计和算法求解计算人为简化。     

MIMO系统的多模型预测控制

针对非线性多变量系统提出一种多模型预测控制(MMPC)策略.首先给出一种多模型 辨识方法,利用模糊满意聚类算法将复杂非线性系统划分为若干子系统,并获得多个线性模型, 通过模型变换得出全局系统模型,接着对全局MIMO系统设计MMPC,并进行了系统的性能分 析,最后以pH中和过程为例,通过仿真研究验证了辨识和控制算法的有效性.     

多变量动态矩阵控制系统的闭环稳定性

定量分析了无约束多输入多输出（MIMO）动态矩阵控制系统的闭环稳定条件,首先基于脉冲响应模型重新描述了动态矩阵控制（DMC）算法;在此基础上,推导得到了MIMODMC系统的闭环稳定条件,以便于预测控制系统的分析与设计。     

MIMO系统预测控制及其Matlab与VC仿真实现

本文对一个多入多出耦合系统设计了一种基于隐式算法的广义预测控制器(GPC),此隐式算法利用与基于脉冲模型的预测控制(DMC)算法的等价性化简,避免求解Diophantine方程。同时对此多入多出系统设计了DMC控制器,并对二种算法和控制效果进行了比较。本文用Matlab编程实现两种控制算法,用VC++设计界面,在VC中调用M函数的动态链接库DLL实现Matlab和VC的混合编程。对此被控MIMO系统,最终即可通过在VC界面上实现控制方式和控制输出的设定得到控制输入输出的仿真图。此程序可以脱离Matlab环境运行。     

基于预测状态的双速率DMC算法

针对一类快输入慢输出的双速率系统,在状态空间模型下,采用提升技术,将一类单速率的DMC预测控制算法推广到双速率系统中,并比较单速率算法和双速率算法的控制性能,给出双速率算法具有较好的控制性能的原因．仿真研究表明,基于预测状态的双速率预测控制算法,能够没有超调的跟踪设定值,并且具有较快的系统响应。     

先进控制算法的研究

DMC预测控制算法能够较好改善纯滞后复杂对象的控制效果.基于LabVIEW设计实现了DMC、DMC-PID、差分方程MPC等高级控制算法.利用LabVIEW对以上算法进行仿真,并选取一阶纯滞后模型作为控制对象进行控制效果检验.实验结果证明以上控制算法较常规PID有较好的控制效果.     

动态矩阵控制参数的满意优化

动态矩阵控制（DMC）算法是一种基于被控对象的阶跃响应、适用于渐进稳定的线性对象的 预测控制算法,它具有结构清晰、算法简单的特点．但由于其参数仅间接影响控制系统的性 能,与控制的快速性．稳定性、鲁棒性、抗干扰性等没有直接的解析关系可作为设计的定量 依据,给设计者造成很大的困难．本文提出了运用满意解原理,通过仿真进行参数优化的方 法,可为DMC算法的参数设计提供参考．     

Fuzzy systems design: direct and indirect approaches

A systematic classification of the data-driven approaches for design of fuzzy systems is given in the paper. The possible ways to solve this modelling and identification problem are classified on the basis of the optimisation techniques used for this purpose. One algorithm for each of the two basic categories of design methods is presented and its advantages and disadvantages are discussed. Both types of algorithms are self-learning and do not require interaction during the process of fuzzy model design. They perform adaptation of both the fuzzy model structure (rule-base) and the parameters. The indirect approach exploits the dual nature of Takagi-Sugeno (TS) models and is based on recently introduced recursive clustering combined with Kalman filtering-based procedure for recursive estimation of the parameter of the local sub-models. Both algorithms result in finding compact and transparent fuzzy models. The direct approach solves the optimisation problem directly, while the indirect one decomposes the original problem into on-line clustering and recursive estimation problems and finds a sub-optimal solution in real-time. The later one is computationally very efficient and has a range of potential applications in real-time process control, moving images recognition, autonomous systems design etc. It is extended in this paper for the case of multi-input–multi-output (MIMO systems). Both approaches have been tested with real data from an engineering process.     

A PSO-BASED MULTIVARIABLE FUZZY DECISION-MAKING PREDICTIVE CONTROLLER FOR A ONCE-THROUGH 300-MW POWER PLANT

Model predictive control is an available method for controlling large-lag process in power plants, but conventional constrained predictive control cannot deal with the widely existent uncertainties and nonlinearities in power plants. With the help of the fuzzy set theory, this article proposes a new constrained predictive control algorithm based on Fuzzy Decision-Making Method (FDMPC). Compared with the other traditional constrained predictive control, this new algorithm replaces the conventional objective function with the appropriate fuzzy index function. As a result, it is easy to integrate the constraints into the fuzzy index function, which can greatly reduce the complexity of the optimization. Then a new evolutionary computation method named particle swarm optimization is firstly applied into the design of a model predictive controller. Moreover, this article also demonstrates that the conventional predictive control is actually a particular case of the proposed algorithm even though in the MIMO case, so this new algorithm is an extension of the traditional constrained predictive control strategy. At last, the proposed FDMPC has been applied into a real once-through power unit model, and the simulation results have validated the good control performance of the proposed FDMPC.     

Predictive control based on local linear fuzzy models

This paper deals with predictive control based on fuzzy models. A novel algorithm (LOLIMOT) is proposed for the construction of Takagi-Sugeno fuzzy models. The rule consequents are optimized by a local orthogonal least-squares method that selects the significant regressors. The rule premises are optimized by a tree construction algorithm which partitions the input space in hyper-rectangles. A generalized predictive controller (GPC) and a dynamic matrix controller (DMC) are designed. Both controllers require the extraction of a linear model from the Takagi-Sugeno fuzzy model. For the GPC a new technique called local dynamic linearization is proposed that exploits the special structure of the local linear models. The DMC is based on the evaluation of a step response. The effectiveness of both the identification algorithm and the predictive controllers is shown by application to temperature control of an industrial-scale cross-flow heat exchanger.     

基于分散模糊推理的多输入多输出系统预测控制

对于动态过程具有明显迟延和惯性的MIMO系统,常规模糊控制难以建立模糊规则,控制效果不理想.针对MIMO控制对象,提出一种基于分散模糊推理的预测控制(predictive control based on decentralized fuzzy inference, DFIPC)方法.构造一组与被控输出相对应的分散模糊推理模块,每个推理模块利用一组分散的模糊推理单元,分别根据各个输出的期望值与预测值之间的偏差进行分散推理.在时间层面,根据动态响应程度对推理结果进行加权综合,获得等效控制输入;进一步,通过对等效控制输入加权综合产生系统实际控制输入增量,从而有效克服模糊推理系统处理多维输入信息时模糊规则难以建立的困难.最后,通过实验验证所提出控制方法对于迟延和惯性明显的MIMO控制对象的有效性和适应性.     

应用于PLC的一种新的模糊控制算法

针对一般的模糊控制算法所存在的问题,提出了一种新的模糊算法。它既能自适应参数的变化,又能克服输出模糊量变化时控制量的跳变,同时其算法简便,易于在PLC等一般的工业控制计算机上加以实现。通过应用实践证明,该算法具有响应快、超调小,稳态精度高,自适应能力强等优点。     

一种基于T-S模型的模糊预测控制

提出了一种基于T-S模型的模糊预测控制策略。T-S模糊模型用来描述对象的非线性动态特性,通过当前的工况参数实时在线的修正每一时刻的阶跃响应模型参数,将模糊模型作为常规线性预测控制DMC方法的预测模型,从而把T-S模型对复杂的非线性系统的良好描述特性和预测控制的滚动优化算法相结合,来实现利用常规线性预测控制策略对非线性系统的有效控制,有效地解决了复杂工业过程的强非线性问题。pH中和过程的仿真结果表明其性能明显优于传统的PID控制器。     

Predictive Functional Control Based on Fuzzy Model: Design and Stability Study

In the paper the design methodology and stability analysis of parallel distributed fuzzy model based predictive control is presented. The idea is to design a control law for each rule of the fuzzy model and blend them together. The proposed control algorithm is developed in state space domain and is given in analytical form. The analytical form brings advantages in comparison with optimization based control schemes especially in the sence of realization in real-time. The stability analysis and design problems can be viewed as a linear matrix inequalities problem. This problem is solved by convex programming involving LMIs. In the paper a sufficient stability condition for parallel distributed fuzzy model-based predictive control is given. The problem is illustrated by an example on magnetic suspension system.     

PID-DMC算法及其在液位控制系统中的应用

提出了一种新的带PID校正环节的DMC算法，论述了其原理并将其应用于实际液位控制系统，实现了系统在模型失配明显的情况下对二阶液位对象的有效控制。由于控制阀的非线性特性使得本实验中的液位对象具有时滞弱非线性特性，常规DMC算法控制存在明显的模型失配问题并影响了系统的控制性能。为此在常规DMC算法中引入PID环节，从而得到了一种新的PID—DMC算法。实验结果表明该算法能有效改善系统在模型失配情况下的控制性能，且算法简单易行，在线计算量小。     

MIMO格密码的设计与实现

对大规模MIMO物理层安全通信进行了研究,提出了一种基于OAEP+算法的MIMO格密码实现方案。在适当的限制条件下,可将窃听者对大规模MIMO的解码复杂性问题归约为解标准格问题。由于在方案设计中利用格密码和OAEP+密码的特性,使得所提方案具有抗量子计算攻击和选择性密文攻击的能力,并且合法通信双方不需要预共享密钥,有效地简化密钥管理。在Matlab中对所提方案进行了仿真实现,仿真结果证明了该方案的可行性。另外,仿真计算结果显示在格基归约算法下MIMO信道可计算保密容量跟发射天线数目之间呈较强的线性关系。     

T-S模糊广义系统的逼近性

本文研究T-S模糊广义系统的逼近性,给出了T-S模糊广义系统的逼近性定理.证明其可以以任意的精度逼近一类广泛存在的非线性广义系统.还将MISO(多输入单输出)情况推广到MIMO(多输入多输出)的情况.在逼近性定理的基础上,利用神经网络的方法对非线性广义系统建模,给出了神经网络的结构及学习算法.本文共提出了两种神经网路的训练策略,对各自的优点与不足给出了分析,最后用数值例子验证了算法的有效性.     

The isolation layered optimization algorithm of MIMO polygonal fuzzy neural network

The single-input single-output or multi-input single-output polygonal fuzzy neural network can accomplish some information disposing based on a finite number of points of polygonal fuzzy number. Although it does not depend on a precise mathematical model, it involves logical reasoning, numerical calculation and nonlinear functional approximation. The multi-input multi-output (MIMO) polygonal fuzzy neural network model is proposed for the first time in this article. The two different algorithms are designed in the input layer and hidden layer, respectively, and some parameters of the connection weights in the isolation layered manner can be optimized. Particularly, the neurons in the hidden layer are optimized one by one. Results showed that the isolation layered optimization algorithm of MIMO polygonal fuzzy neural network could improve the computational efficiency and convergent rate.

     

多变量模糊控制的现状与发展（Ⅰ）：关于分层,自学习,自适应等问题

回顾了多变量模糊控制近年来的发展过程，着重介绍近年来出现的一些新研究方向，并对当前该研究领域的热点课题：模糊模型的辨识、多变量模糊自校正控制、模糊解耦、模糊分层控制、模糊神经网络的研究现状以及它们的发展趋势作了进一步讨论。     

Adaptive sliding mode control for MIMO nonlinear systems based on fuzzy logic scheme

In this study an indirect adaptive sliding mode control (SMC) based on a fuzzy logic scheme is proposed to strengthen the tracking control performance of a general class of multi-input multi-output (MIMO) nonlinear uncertain systems. Combining reaching law approach and fuzzy universal approximation theorem, the proposed design procedure combines the advantages of fuzzy logic control, adaptive control and sliding mode control. The stability of the control systems is proved in the sense of the Lyapunov second stability theorem. Two simulation studies are presented to demonstrate the effectiveness of our new hybrid control algorithm.