Interval type-2 fuzzy inverse controller design in nonlinear IMC structure

In the recent years it has been demonstrated that type-2 fuzzy logic systems are more effective in modeling and control of complex nonlinear systems compared to type-1 fuzzy logic systems. An inverse controller based on type-2 fuzzy model can be proposed since inverse model controllers provide an efficient way to control nonlinear processes. Even though various fuzzy inversion methods have been devised for type-1 fuzzy logic systems up to now, there does not exist any method for type-2 fuzzy logic systems. In this study, a systematic method has been proposed to form the inverse of the interval type-2 Takagi-Sugeno fuzzy model based on a pure analytical method. The calculation of inverse model is done based on simple manipulations of the antecedent and consequence parts of the fuzzy model. Moreover, the type-2 fuzzy model and its inverse as the primary controller are embedded into a nonlinear internal model control structure to provide an effective and robust control performance. Finally, the proposed control scheme has been implemented on an experimental pH neutralization process where the beneficial sides are shown clearly.     

A Survey on Analysis and Design of Model-Based Fuzzy Control Systems

Fuzzy logic control was originally introduced and developed as a model free control design approach. However, it unfortunately suffers from criticism of lacking of systematic stability analysis and controller design though it has a great success in industry applications. In the past ten years or so, prevailing research efforts on fuzzy logic control have been devoted to model-based fuzzy control systems that guarantee not only stability but also performance of closed-loop fuzzy control systems. This paper presents a survey on recent developments (or state of the art) of analysis and design of model based fuzzy control systems. Attention will be focused on stability analysis and controller design based on the so-called Takagi–Sugeno fuzzy models or fuzzy dynamic models. Perspectives of model based fuzzy control in future are also discussed.     

基于RBF-ARX模型的预测控制在液位系统中的应用

针对单容液位系统紊流时的非线性特征,研究了基于RBF-ARX模型预测控制策略控制单容液位系统;讨论RBF-ARX模型结构的选取,模型参数辨识,RBF参数优化,基于RBF-ARX模型的预测控制策略等问题;模型的仿真结果,证实了RBF-ARX模型在非线性系统建模和辨识中的有效性;同基于全局线性ARX模型的预测控制器和PID控制器相比较,基于此模型的预测控制取得了优异的控制效果。     

Analysis of stability and robust stability of polynomial fuzzy model-based control systems using a sum-of-squares approach

The well known Takagi–Sugeno (T–S) fuzzy model can be extended in different ways including the polynomial fuzzy model, whose consequent parts are polynomial sub-systems. Compared with the traditional T–S fuzzy model, the polynomial fuzzy model can represent a nonlinear system more accurately with a smaller number of fuzzy logic rules. It is worth emphasizing that the stability analysis and controller design of polynomial fuzzy model-based (PFMB) control systems are not based on the linear matrix inequalities but the recently developed sum-of-squares decompositions. In this paper, based on an existing result for traditional fuzzy control systems, we propose a new stability condition for the stability analysis of PFMB control systems. Furthermore, the stability of PFMB control systems with parameter uncertainties is investigated. The popular inverted pendulum and an unstable nonlinear system are employed to demonstrate the quality of the proposed stability conditions.     

非线性动态系统神经模糊建模与内模/PID双重控制系统设计

非线性动态系统的内模控制要求建立精确的对象正模型和逆模型,这对于大多数实际对象是难以做到.提出了基于一类神经模糊模型的非线性动态系统建模方法,并在此基础上研究了基于神经模糊模型的非线性系统的内模控制设计.基于输入输出数据辨识的对象正模型和逆模型存在着模型失配问题,导致神经模糊内模控制范围变窄和控制鲁棒性降低,为了改善系统的性能,提出了神经模糊内模控制与PID控制结合的双重控制策略.对CSTR的反应物浓度控制研究表明,双重控制策略能有效地拓宽系统可控范围,改善系统性能.仿真结果证明该控制策略简单而有效.     

一类非齐次时变线性模型及自适应预测控制策略

通过分析系统闭环预测存在的缺陷，为了改善系统的预测性能、便于系统辨识和实现自适应控制，提出了一类适合于一般非线性系统的非齐次时变线性模型，给出并证明了其存在性定理。而后给出了这类非齐次时变线性模型的渐消记忆递推最小二乘参数估计，推出了基于该类模型的自适应预测控制算法。仿真结果表明基于该类模型的自适应预测控制策略具有优良的控制品质。     

未知相对度非线性系统的一阶D型迭代学习控制

The classical D-type iterative learning control law depends crucially on the relative degree of the controlled system, high order differential iterative learning law must be taken for systems with high order relative degree. It is very difficult to ascertain the relative degree of the controlled system for uncertain nonlinear systems. A first-order D-type iterative learning control design method is presented for a class of nonlinear systems with unknown relative degree based on dummy model in this paper. A dummy model with relative degree 1 is constructed for a class of nonlinear systems with unknown relative degree. A first-order D-type iterative learning control law is designed based on the dummy model, so that the dummy model can track the desired trajectory perfectly, and the controlled system can track the desired trajectory within a certain error. The simulation example demonstrates the feasibility and effectiveness of the presented method.     

基于行为法多智能体系统构形控制研究

为实现多智能体系统的构形控制,针对二阶多智能体系统,采用了一种基于智能体行为的控制算法,这种控制算法考虑到智能体的驶向目标行为和构形维持行为,可以有效实现智能体相对于期望目标的构形控制,由于采用该算法使得多智能体系统中有明确的队形反馈,因而有利于分布式控制和实时控制。智能体的动力学模型采用多智能体问题研究广泛使用的独轮车模型,通过反馈线性化方法,将这种非线性模型转化成了实用的双积分系统模型。通过Matlab仿真验证了算法的有效性,结果表明控制器参数整定简单,具有很好的稳定性和鲁棒性。     

网络控制系统的增广状态极点配置设计法

参数时变系统可以通过增广状态法转换成一种标准形式．本文应用极点配置设计法，设计了基于网络控制系统标准形式模型的控制规律和观测器，并证明了分离定理仍然成立．仿真研究表明该设计方法应用到网络控制系统的有效性．     

Design of multivariable adaptive model following control systems

The results for the problems of “perfect model following” and “hyperstability of model reference adaptive systems” are integrated in order to develop a general design technique for multivariable adaptive model following control systems. Two types of adaptation are involved: adaptation of the parameters of the control loop and signal synthesis adaptation for model following control systems with a fixed structure. The design and the implementation of the adaptation mechanism based on the use of a hyperstable adaptation algorithm are discussed. The feasibility and the advantages of the procedure are illustrated by applying it to a non trivial aircraft control problem.     

基于ANN逆模型补偿的复合控制系统仿真

为对复杂非线性系统进行辨识建模和实施有效控制，分析了基于神经网络的非线性系统逆模型的辨识和控制原理，研究了基于神经网络的非线性系统逆模型补偿的复合控制方法。基于复合控制思想，时常规PID控制器＋前馈神经网络逆模型补偿的复合控制结构方案进行了仿真。仿真结果表明，基于神经网络的非线性系统逆模型补偿的复合控制结构方案是有效的、相对简单的网络结构，可提高逆模型的泛化能力和非线性系统的控制精度。     

An imaginary robot model and double-PD control for redundant robotic systems

The theory and applications of an imaginary robot model with a double-PD control law for redundant robotic systems are presented. The imaginary robot model is based on a special Riemannian metric decomposition for general nonlinear dynamic systems. This model offers an effective way for reducing nonlinear feedback formulation while preserving the linearized system equation. The developed procedure is also applicable to redundant robots. A three-dimensional redundant robot main-frame having three revolute joints plus a prismatic joint is used in the paper to illustrate the design procedure based on the imaginary robot model with the double-PD control scheme. The entire dynamic control algorithm is also verified by a simulation study on the four-joint three-dimensional robot arm.     

多采样率数字控制系统的函数空间模型

采用函数空间的方法 ,利用提升 (lifting)技术 ,构造出多采样率数字控制系统的函数空间模型 .该线性时不变离散模型同时描述了被控对象在采样点上和采样点之间的动态行为 .根据这种模型所设计的多采样率数字控制系统 ,有望克服目前多采样率数字控制系统所存在的采样点之间存在有纹波或振荡等弊病 ,为多采样率数字控制系统的分析与综合提供了一个新的方法 .     

一种非线性和时滞系统自适应控制方法

提出一种新的基于高斯基函数(GPFN-G aussian potential function networks)网络的内模控制算法,并对高斯基函数网络内部模 型和内模控制器的建立进行了深入分析．仿真及实时控制结果表明该算法是有效的,具有很 好的自适应性和鲁棒性,可以应用于具有时滞和非线性时变系统中．     

Modeling privacy control in context-aware systems

Significant complexity issues challenge designers of context-aware systems with privacy control. Information spaces provide a way to organize information, resources, and services around important privacy-relevant contextual factors. In this article, we describe a theoretical model for privacy control in context-aware systems based on a core abstraction of information spaces. We have previously focused on deriving socially based privacy objectives in pervasive computing environments. Building on Ravi Sandhu's four-layer OM-AM (objectives, models, architectures, and mechanisms) idea, we aim to use information spaces to construct a model for privacy control that supports our socially based privacy objectives. We also discuss how we can introduce decentralization, a desirable property for many pervasive computing systems, into our information space model, using unified privacy tagging.     

Adaptive-Predictive Control of a Class of SISO Nonlinear Systems

In this paper, an adaptive-predictive control algorithm is developed for a class of SISO nonlinear discrete-time systems based on a generalized predictive control (GPC) approach. The design is model-free, based directly on pseudo-partial-derivatives derived on-line from the input and output information of the system using a recursive least squares type of identification algorithm. The proposed control is especially useful for nonlinear systems with vaguely known dynamics. Robust stability of the closed-loop system is analyzed and proven in the paper. Simulation and real-time application examples are provided for real nonlinear systems which are known to be difficult to model and control.     

基于模糊动态模型的多变量系统模糊控制

采用模糊动态模型对多变量复杂非线性系统进行模糊控制.首先针对局部线性动态模 型设计状态反馈控制器,然后利用模糊推理确定整个系统的控制;在一系列局部模型通过模糊 隶属函数连接得到的连续的全局模型中,全面考虑其它关联子系统对标称线性系统的摄动,并 利用大系统分散控制关联稳定性的概念和方法,得到了闭环模糊系统稳定的充分条件.仿真例 子验证了该设计方法的正确性.     

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.     

复杂高阶对象的预测PID控制

针对复杂高阶对象提出了一种基于数值最优模型降阶方法,并基于这种降阶模型设计了预测PID控制器,将此控制器应用于原始模型能够得到很好地控制效果。数值最优模型降阶算法使高阶对象能近似为一阶加时滞对象或二阶加时滞对象,通过模型阶跃响应和Bode图对比,降阶模型曲线很好地逼近原始模型曲线。预测PID对大时滞对象有着很好地控制效果,模型降阶使得预测PID很好地控制复杂高阶对象,且其结构简单,可调参数少的特点。     

An integrated state space partition and optimal control method of multi-model for nonlinear systems based on hybrid systems

Multilinear model approach turns out to be an ideal candidate for dealing with nonlinear systems control problem. However, how to identify the optimal active state subspace of each linear subsystem is an open problem due to that the closed-loop performance of nonlinear systems interacts with these subspaces ranges. In this paper, a new systematic method of integrated state space partition and optimal control of multi-model for nonlinear systems based on hybrid systems is initially proposed, which can deal with the state space partition and associated optimal control simultaneously and guarantee an overall performance of nonlinear systems consequently. The proposed method is based on the framework of hybrid systems which synthesizes the multilinear model, produced by nonlinear systems, in a unified criterion and poses a two-level structure. At the upper level, the active state subspace of each linear subsystem is determined under the optimal control index of a hybrid system over infinite horizon, which is executed off-line. At the low level, the optimal control is implemented online via solving the optimal control of hybrid system over finite horizon. The finite horizon optimal control problem is numerically computed by simultaneous method for speeding up computation. Meanwhile, the model mismatch produced by simultaneous method is avoided by using the strategy of receding-horizon. Simulations on CSTR (Continuous Stirred Tank Reactor) confirm that a superior performance can be obtained by using the presented method.