基于向量Lyapunov函数方法的模糊控制系统稳定性分析和设计

采用模糊动态模型对连续时间非线性系统进行模糊控制,对闭环模糊系统的稳定性进行分析,并给出系统化的控制器设计程序,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,并利用向量Lyapunov函数的概念和方法,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。     

一类模糊系统的分解性质和稳定性分析

探讨一类动态模糊模型的分解性质,通过一个简单的构造性的分解过程,将模糊系统的输入空间划分为一些子输入空间,从而把一般的模糊状态空间系统分解为一些定义在子输入空间上的子模糊系统.这些子模糊系统具有最简单的结构,从而可以简化模糊系统稳定性的分析及设计.进一步基于模糊系统的分解性质,引入分段连续的Lyapunov函数和S-procedure,以线性矩阵不等式的形式给出了模糊系统的稳定性条件的一个新结果.     

Analysis of direct action fuzzy PID controller structures

The majority of the research work on fuzzy PID controllers focuses on the conventional two-input PI or PD type controller proposed by Mamdani (1974). However, fuzzy PID controller design is still a complex task due to the involvement of a large number of parameters in defining the fuzzy rule base. This paper investigates different fuzzy PID controller structures, including the Mamdani-type controller. By expressing the fuzzy rules in different forms, each PLD structure is distinctly identified. For purpose of analysis, a linear-like fuzzy controller is defined. A simple analytical procedure is developed to deduce the closed form solution for a three-input fuzzy inference. This solution is used to identify the fuzzy PID action of each structure type in the dissociated form. The solution for single-input-single-output nonlinear fuzzy inferences illustrates the effect of nonlinearity tuning. The design of a fuzzy PID controller is then treated as a two-level tuning problem. The first level tunes the nonlinear PID gains and the second level tunes the linear gains, including scale factors of fuzzy variables. By assigning a minimum number of rules to each type, the linear and nonlinear gains are deduced and explicitly presented. The tuning characteristics of different fuzzy PID structures are evaluated with respect to their functional behaviors. The rule decoupled and one-input rule structures proposed in this paper provide greater flexibility and better functional properties than the conventional fuzzy PHD structures.     

ARTIFICIAL INTELLIGENCE AND GRAPH THEORY TOOLS FOR DESCRIBING SWITCHED LINEAR CONTROL SYSTEMS

This paper develops a representation of multi-model based controllers using artificial intelligence techniques. These techniques will be graph theory, neural networks, genetic algorithms, and fuzzy logic. Thus, graph theory is used to describe in a formal and concise way the switching mechanism between the various plant parameterizations of the switched system. Moreover, the interpretation of multi-model controllers in an artificial intelligence frame will allow the application of each specific technique to the design of improved multi-model based controllers. The obtained artificial intelligence-based multi-model controllers are compared with classic single model-based ones. It is shown through simulation examples that a transient response improvement can be achieved by using multi-estimation based techniques. Furthermore, a method for synthesizing multi-model-based neural network controllers from already designed single model-based ones is presented, extending the applicability of this kind of technique to a more general type of controller. Also, some applications of genetic algorithms and fuzzy logic to multi-model controller design are proposed. In particular, the mutation operation from genetic algorithms inspires a robustness test, which consists of a random modification of the estimates which is used to select the one leading to the better identification performance towards parameterizing online the adaptive controller. Such a test is useful for plants operating in a noisy environment. The proposed robustness test improves the selection of the plant model used to parameterize the adaptive controller in comparison to classic multi-model schemes where the controller parameterization choice is basically taken based on the identification accuracy of each model. Moreover, the fuzzy logic approach suggests new ideas to the design of multi-estimation structures, which can be applied to a broad variety of adaptive controllers such as robotic manipulator controller design.     

Stable and robust fuzzy control for uncertain nonlinear systems

Presents the stability and robustness analysis for multivariable fuzzy control systems subject to parameter uncertainties based on a single-grid-point (SGP) approach. To perform the analysis, we represent a multivariable nonlinear system using a TS-fuzzy plant model. Three design approaches of fuzzy controllers are introduced to close the feedback loop. By estimating the matrix measures of the system parameters and parameter uncertainties, stability and robustness conditions for different cases are derived. Application examples are given to show the design procedures and the merits of the proposed fuzzy controller     

Robust stability analysis and design method for the fuzzy feedbacklinearization regulator

A robust stability analysis and design method for a fuzzy feedback linearization regulator is presented. The well-known Takagi-Sugeno fuzzy model is used as the nonlinear plant model. Uncertainties and disturbance are assumed to be included in the model structure with known bounds. For these structured uncertainties, stability robustness of the closed system is analyzed in both input-output sense and Lyapunov sense. The robust stability conditions are proposed using multivariable circle criterion and the relationship between input-output stability and Lyapunov stability. Also, based on the stability analysis, a systematic design procedure for the fuzzy feedback linearization regulator is provided. The effectiveness of the proposed analysis and design method is illustrated by a simple example     

基于增益协调方法的非线性控制器设计

为了寻求结构简单、易于进行稳定性分析的非线性控制器的设计方法，提出一种基于增益协调的非线性控制器设计方法理论。该控制器可以逼近很多复杂的非线性控制器，并具有相同的控制效果。该方法与模糊控制器的控制效果进行比较，说明了基于增益协调的非线性控制器简单、易于实现，同时更具便于调节的优点。同时基于李亚普诺夫稳定性理论，对例题中设计出的增益协调控制器的稳定性给予证明。结合桌飞控系统舵机控制实例，说明了该方法的实用性。     

On Maximum Stability Margin Design of Nonlinear Uncertain Systems: Fuzzy Control Approach

This paper studies the maximum stability margin design for nonlinear uncertain systems using fuzzy control. First, the Takagi and Sugeno fuzzy model is employed to approximate a nonlinear uncertain system. Next, based on the fuzzy model, the maximum stability margin for a nonlinear uncertain system is studied to achieve as much tolerance of plant uncertainties as possible using a fuzzy control method. In the proposed fuzzy control method, the maximum stability margin design problem is parameterized in terms of a corresponding generalized eigenvalue problem (GEVP). For the case where state variables are unavailable, a fuzzy observer‐based control scheme is also proposed to deal with the maximum stability margin for nonlinear uncertain systems. Using a suboptimal approach, we characterize the maximum stability margin via fuzzy observer‐based control in terms of a linear matrix inequality problem (LMIP). The GEVP and LMIP can be solved very efficiently via convex optimization techniques. Simulation examples are given to illustrate the design procedure of the proposed method.     

Reliable LQ fuzzy control for continuous-time nonlinear systems with actuator faults

This paper deals with the reliable linear quadratic (LQ) fuzzy control problem for continuous-time nonlinear systems with actuator faults. The Takagi-Sugeno (T-S) fuzzy model is employed to represent a nonlinear system. By using multiple Lyapunov functions, an improved linear matrix inequality (LMI) method for the design of reliable LQ fuzzy controllers is investigated, which reduces the conservatism of using a single Lyapunov function. The different upper bounds on the LQ performance cost function for the normal and different actuator fault cases are provided. A suboptimal reliable LQ fuzzy controller is given by means of an LMI optimization procedure, which can not only guarantee the stability of the closed-loop overall fuzzy system for all cases, but also provide an optimized upper bound on a weighted average LQ performance cost function. Finally, numerical simulations on the chaotic Lorenz system are given to illustrate the application of the proposed design method.     

Quadratic-Stability Analysis of Fuzzy-Model-Based Control Systems Using Staircase Membership Functions

This paper presents the stability analysis of fuzzy-model-based (FMB) control systems. Staircase membership functions are introduced to facilitate the stability analysis. Through the staircase membership functions approximating those of the fuzzy model and fuzzy controller, the information of the membership functions can be brought into the stability analysis. Based on the Lyapunov-stability theory, stability conditions in terms of linear-matrix inequalities (LMIs) are derived in a simple and easy-to-understand manner to guarantee the system stability. The proposed stability-analysis approach offers a nice property that includes the membership functions of both fuzzy model and fuzzy controller in the LMI-based stability conditions for a dedicated FMB control system. Furthermore, the proposed stability-analysis approach can be applied to the FMB control systems of which the membership functions of both fuzzy model and fuzzy controller are not necessarily the same. Greater design flexibility is allowed to choose the membership functions during the design of fuzzy controllers. By employing membership functions with simple structure, it is possible to lower the structural complexity and the implementation cost. Simulation examples are given to illustrate the merits of the proposed approach.      

Stabilization of nonlinear systems using sampled-data output-feedback fuzzy controller based on polynomial-fuzzy-model-based control approach

This paper investigates the stability of sampled-data output-feedback (SDOF) polynomial-fuzzy-model-based control systems. Representing the nonlinear plant using a polynomial fuzzy model, an SDOF fuzzy controller is proposed to perform the control process using the system output information. As only the system output is available for feedback compensation, it is more challenging for the controller design and system analysis compared to the full-state-feedback case. Furthermore, because of the sampling activity, the control signal is kept constant by the zero-order hold during the sampling period, which complicates the system dynamics and makes the stability analysis more difficult. In this paper, two cases of SDOF fuzzy controllers, which either share the same number of fuzzy rules or not, are considered. The system stability is investigated based on the Lyapunov stability theory using the sum-of-squares (SOS) approach. SOS-based stability conditions are obtained to guarantee the system stability and synthesize the SDOF fuzzy controller. Simulation examples are given to demonstrate the merits of the proposed SDOF fuzzy control approach.     

一个新混沌系统的自适应模糊同步

本文基于Takagi-Sugeno(T-S)模糊模型,研究了混沌系统的自适应同步。基于T-S模糊模型重构了混沌系统,推导了在衰减率α下,自适应同步全局渐近稳定的充分条件;同时,在驱动系统参数未知的情况下,使用自适应参数调节律,得到响应系统参数的估计值。设计的模糊控制器均由线性函数构成,结构简单,规则少,有利于实际应用中构造控制器。数值仿真结果验证了方法的有效性。     

A multirule-base controller using the robust property of a fuzzycontroller and its design method

This paper suggests a new fuzzy adaptive controller, which is able to solve the problems of classical adaptive controllers and conventional fuzzy adaptive controllers. It explains the architecture of a fuzzy adaptive controller using the robust property of a fuzzy controller. The basic idea of new adaptive control scheme is that an adaptive controller can be constructed with parallel combination of robust controllers. This new adaptive controller uses a multirule-base architecture which has several independent fuzzy controllers in parallel, each with different robust stability area. Out of several independent fuzzy controllers, the most suited one is selected by a system identifier which observes variations in the controlled system parameter. Here, we propose a design procedure which can be carried out mathematically and systematically from the model of a controlled system; related mathematical theorems and their proofs are also given. The performance of the proposed adaptive control algorithm is analyzed through a design example and a DC motor control simulation     

基于H∞环路成形和自适应神经模糊推理系统的模糊控制器设计

H_∞环路成形方法设计的控制器阶次较高,不便于工程实现和参数调整;用传统方法确定模糊控制器隶属度函数的参数和模糊规则比较费时且难以保证鲁棒性能和时频域性能指标.针对上述情况,提出了一种综合运用H_∞环路成形和自适应神经模糊推理系统来设计模糊控制器的方法.首先采用H_∞环路成形设计方法,得到鲁棒裕量、动态和稳态性能都符合要求的控制器,然后用自适应神经模糊推理系统来逼近此控制器,最后根据自适应神经模糊推理系统参数确定相应的模糊控制器规则和参数.该方法确定模糊控制器隶属度函数的参数精确而省时,且能保证控制器具有较强的鲁棒性和良好的控制效果.通过对小车倒立摆系统进行的仿真,验证了该控制器设计方法的有效性.     

Stabilization for T-S model based uncertain stochastic systems

This paper considers the stabilization problem of a class of uncertain Itô stochastic fuzzy systems driven by a multidimensional Wiener process. The uncertainty modeled in the systems is of the linear fractional type which includes the norm-bounded uncertainty as a special case. The objective is to design a state-feedback fuzzy controller such that the closed-loop system is robustly asymptotically stable under a stochastic setting. By using a stochastic Lyapunov approach, sufficiency conditions for the stability and stabilization of this class of systems are established based on a novel matrix decomposition technique. The derived stability conditions are then employed to design controllers which stabilize the uncertain Itô stochastic fuzzy systems. Two simulation examples are given to illustrate the effectiveness of the approaches proposed.     

Finite-dimensional constrained fuzzy control for a class of nonlinear distributed process systems.

This correspondence studies the problem of finite-dimensional constrained fuzzy control for a class of systems described by nonlinear parabolic partial differential equations (PDEs). Initially, Galerkin's method is applied to the PDE system to derive a nonlinear ordinary differential equation (ODE) system that accurately describes the dynamics of the dominant (slow) modes of the PDE system. Subsequently, a systematic modeling procedure is given to construct exactly a Takagi-Sugeno (T-S) fuzzy model for the finite-dimensional ODE system under state constraints. Then, based on the T-S fuzzy model, a sufficient condition for the existence of a stabilizing fuzzy controller is derived, which guarantees that the state constraints are satisfied and provides an upper bound on the quadratic performance function for the finite-dimensional slow system. The resulting fuzzy controllers can also guarantee the exponential stability of the closed-loop PDE system. Moreover, a local optimization algorithm based on the linear matrix inequalities is proposed to compute the feedback gain matrices of a suboptimal fuzzy controller in the sense of minimizing the quadratic performance bound. Finally, the proposed design method is applied to the control of the temperature profile of a catalytic rod.     

Fuzzy system identification via probabilistic sets

The paper deals with the applications of probabilistic sets in system theory, especially in system identification and the design of fuzzy logic controllers. Fuzzy systems described by means of fuzzy relational equations and Λ-fuzzy systems are discussed. The identification procedure is based on some ideas of clustering techniques and probabilistic sets. Numerical examples using fuzzy and nonfuzzy data are used to illustrate the theory.     

Iterative performance improvement of fuzzy control systems for three tank systems

This paper suggests the performance improvement of fuzzy control systems (FCSs) for three tank systems using iterative feedback tuning (IFT). The stable design of Takagi–Sugeno–Kang fuzzy controllers is guaranteed by means of a stability theorem based on LaSalle’s global invariant set theorem formulated for a class of multi input-multi output (MIMO) nonlinear processes. An IFT algorithm characterized by setting the step size to guarantee the FCS stability is proposed. The theoretical approaches are applied in a case study that deals with the IFT-based stable design of fuzzy controllers dedicated to the level control of a cylindrical three tank system as a representative MIMO system. A set of experimental results for a laboratory setup illustrates the performance improvement.     

Four wheel steering control by fuzzy approach

This study introduces a fuzzy four-wheel steering control design method for automotive vehicles. After the analysis of some stability aspects of the vehicle lateral motion, including front steering angle variations, the representation of vehicle nonlinear model by Takagi-Sugeno (T-S) fuzzy model is presented. Next, based on the fuzzy model, a fuzzy controller is developed to improve the stability of the vehicle. Sufficient conditions for stability and stabilization of the T-S fuzzy model using fuzzy feedback controllers is given. To demonstrate the effectiveness of the proposed fuzzy controller, simulation results are given showing the performance improvements of the vehicle in terms of the stability and the maneuverability in critical situations.     

Design of Mamdani fuzzy logic controllers with rule base minimisation using genetic algorithm

This paper presents a design procedure for Mamdani fuzzy logic controller including rule base minimisation. The rules are modelled with binary weights on which constraints are imposed in order to ensure consistency. A genetic algorithm is used for finding stabilising controllers that minimise the number of rules. The cost function includes a stability/performance coefficient which insures that stable, performance satisfying controllers are given the highest possible fitness. The number of fuzzy sets for the input and the control variables are set by the user and the design procedure is concerned only with the rule base and the distribution of the fuzzy sets in the universes of discourses. Two examples were studied: the control of the pole and cart system and the control of the concentration in CSTR. In both cases, the fuzzy sets were isosceles triangles evenly distributed, in the universe of discourses.