Polynomial Fuzzy Systems Research Articles

H∞ Control of Polynomial Fuzzy Systems: A Sum of Squares Approach

This paper proposes the control design ofa nonlinear polynomial fuzzy system with H∞ performance objective using a sum of squares (SOS) approach. Fuzzy model and controller are represented by a polynomial fuzzy model and controller. The design condition is obtained by using polynomial Lyapunov functions that not only guarantee stability but also satisfy the H∞ performance objective. The design condition is represented in terms of an SOS that can be numerically solved via the SOSTOOLS. A simulation study is presented to show the effectiveness of the SOS-based H∞ control designfor nonlinear polynomial fuzzy systems.

Discrete polynomial fuzzy systems control

This study presents a sum-of-squares (SOS) approach to stable control and guaranteed cost control of discrete polynomial fuzzy systems. The SOS framework presented in this paper offers a new paradigm over the existing linear matrix inequality (LMI) approaches to discrete Takagi-Sugeno (T-S) fuzzy models. At first, this study, newly introduces a discrete polynomial fuzzy model that is more general and effective than the well-known discrete T-S fuzzy model. With considering the operating domain, stable control design conditions are then derived based on state-dependent Lyapunov functions that contain quadratic Lyapunov functions as a special case. Hence, the design approach discussed in this study could be more general than the LMI design approaches based on quadratic Lyapunov functions. Moreover, this study also discusses a guaranteed cost control design which is carried out by minimising the upper bound of a given performance function. All the design conditions derived in this study can be represented in terms of SOS and are symbolically and numerically solved via the SOSOPT and the SeDuMi, respectively. Finally, the ball-and-beam system is provided as an example to illustrate the utility of the proposed SOS-based design approach.

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.

Polynomial Fuzzy Observer Designs: A Sum-of-Squares Approach

This paper presents a sum-of-squares (SOS) approach to polynomial fuzzy observer designs for three classes of polynomial fuzzy systems. The proposed SOS-based framework provides a number of innovations and improvements over the existing linear matrix inequality (LMI)-based approaches to Takagi-Sugeno (T-S) fuzzy controller and observer designs. First, we briefly summarize previous results with respect to a polynomial fuzzy system that is a more general representation of the well-known T-S fuzzy system. Next, we propose polynomial fuzzy observers to estimate states in three classes of polynomial fuzzy systems and derive SOS conditions to design polynomial fuzzy controllers and observers. A remarkable feature of the SOS design conditions for the first two classes (Classes I and II) is that they realize the so-called separation principle, i.e., the polynomial fuzzy controller and observer for each class can be separately designed without lack of guaranteeing the stability of the overall control system in addition to converging state-estimation error (via the observer) to zero. Although, for the last class (Class III), the separation principle does not hold, we propose an algorithm to design polynomial fuzzy controller and observer satisfying the stability of the overall control system in addition to converging state-estimation error (via the observer) to zero. All the design conditions in the proposed approach can be represented in terms of SOS and are symbolically and numerically solved via the recently developed SOSTOOLS and a semidefinite-program solver, respectively. To illustrate the validity and applicability of the proposed approach, three design examples are provided. The examples demonstrate the advantages of the SOS-based approaches for the existing LMI approaches to T-S fuzzy observer designs.

Sum of Squaresアプローチによる離散多項式ファジィシステムの制御

Sum of Squaresアプローチによる離散多項式ファジィシステムの制御

Improved Polynomial Fuzzy Modeling and Controller with Stability Analysis for Nonlinear Dynamical Systems

This study presents an improved model and controller for nonlinear plants using polynomial fuzzy model‐based (FMB) systems. To minimize mismatch between the polynomial fuzzy model and nonlinear plant, the suitable parameters of membership functions are determined in a systematic way. Defining an appropriate fitness function and utilizing Taylor series expansion, a genetic algorithm (GA) is used to form the shape of membership functions in polynomial forms, which are afterwards used in fuzzy modeling. To validate the model, a controller based on proposed polynomial fuzzy systems is designed and then applied to both original nonlinear plant and fuzzy model for comparison. Additionally, stability analysis for the proposed polynomial FMB control system is investigated employing Lyapunov theory and a sum of squares (SOS) approach. Moreover, the form of the membership functions is considered in stability analysis. The SOS‐based stability conditions are attained using SOSTOOLS. Simulation results are also given to demonstrate the effectiveness of the proposed method.

다항식 퍼지 대규모 시스템의 출력 궤환 제어기 설계 : 제곱합 접근 방법

본 논문에서는 출력 궤환 제어기를 이용해 다항식 퍼지 대규모 시스템을 안정화하는 방법을 제안한다. 이를 위해, 먼저, 대규모 시스템의 부분 시스템에 대해 퍼지 모델링을 한 후, 각각에 대해 출력 궤환 제어기를 설계하여 대규모 시스템을 안정화하는 방법을 제안한다. 이때, 다항식 Lyapunov 함수를 적용하면 다항식 퍼지 대규모 시스템의 안정화 조건은 제곱합 조건으로 주어지고, 이것은 MATLAB의 툴박스인 SOSTOOLS로 해석 가능하다. 이러한 과정을 거친 후에 얻어진 해에서 시스템을 안정화시키는 출력 궤환 제어기의 이득을 구할 수 있다. 마지막으로, 제안된 기법의 적합성을 검증하기 위해 시뮬레이션 예제가 주어진다. This paper presents the stabilization method for polynomial fuzzy large-scale system by using output feedback controller. Each sub system of the large-scale system is transformed into polynomial fuzzy model, and then output feedback controller is designed to stabilize the large-scale system. Stabilization condition is derived as sum-of-square (SOS) condition by applying the polynomial Lyapunov function. This condition can be easily solved by SOSTOOLS which is the third party of the MATLAB. From these solutions, output feedback controller gain can be obtained by SOS condition. Finally, a simulation example is presented to illustrate the effectiveness and the suitability of the proposed method.

多項式ファジィシステムに対するSOS-Based Observerの設計

多項式ファジィシステムに対するSOS-Based Observerの設計

A Sum-of-Squares Approach to Modeling and Control of Nonlinear Dynamical Systems With Polynomial Fuzzy Systems

This paper presents a sum of squares (SOS) approach for modeling and control of nonlinear dynamical systems using polynomial fuzzy systems. The proposed SOS-based framework provides a number of innovations and improvements over the existing linear matrix inequality (LMI)-based approaches to Takagi-Sugeno (T-S) fuzzy modeling and control. First, we propose a polynomial fuzzy modeling and control framework that is more general and effective than the well-known T--S fuzzy modeling and control. Secondly, we obtain stability and stabilizability conditions of the polynomial fuzzy systems based on polynomial Lyapunov functions that contain quadratic Lyapunov functions as a special case. Hence, the stability and stabilizability conditions presented in this paper are more general and relaxed than those of the existing LMI-based approaches to T-S fuzzy modeling and control. Moreover, the derived stability and stabilizability conditions are represented in terms of SOS and can be numerically (partially symbolically) solved via the recently developed SOSTOOLS. To illustrate the validity and applicability of the proposed approach, a number of analysis and design examples are provided. The first example shows that the SOS approach renders more relaxed stability results than those of both the LMI-based approaches and a polynomial system approach. The second example presents an extensive application of the SOS approach in comparison with a piecewise Lyapunov function approach. The last example is a design exercise that demonstrates the viability of the SOS-based approach to synthesizing a stabilizing controller.

Sum of Squaresを用いた多項式ファジィシステムの最適制御

Sum of Squaresを用いた多項式ファジィシステムの最適制御

Guaranteed Cost Control of Polynomial Fuzzy Systems via a Sum of Squares Approach

This paper presents the guaranteed cost control of polynomial fuzzy systems via a sum of squares (SOS) approach. First, we present a polynomial fuzzy model and controller that are more general representations of the well-known Takagi-Sugeno (T-S) fuzzy model and controller, respectively. Second, we derive a guaranteed cost control design condition based on polynomial Lyapunov functions. Hence, the design approach discussed in this paper is more general than the existing LMI approaches (to T-S fuzzy control system designs) based on quadratic Lyapunov functions. The design condition realizes a guaranteed cost control by minimizing the upper bound of a given performance function. In addition, the design condition in the proposed approach can be represented in terms of SOS and is numerically (partially symbolically) solved via the recent developed SOSTOOLS. To illustrate the validity of the design approach, two design examples are provided. The first example deals with a complicated nonlinear system. The second example presents micro helicopter control. Both the examples show that our approach provides more extensive design results for the existing LMI approach.

Compartmental Analysis in Biology and Medicine.John A. Jacquez

Previous articleNext article No AccessNew Biological BooksCompartmental Analysis in Biology and Medicine. John A. Jacquez M. E. MagarM. E. Magar Search for more articles by this author PDFPDF PLUS Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinkedInRedditEmail SectionsMoreDetailsFiguresReferencesCited by The Quarterly Review of Biology Volume 48, Number 4Dec., 1973 Published in association with Stony Brook University Article DOIhttps://doi.org/10.1086/407898 Views: 2Total views on this site Citations: 2Citations are reported from Crossref PDF download Crossref reports the following articles citing this article:Meng Han, H. K. Lam, Fucai Liu, Yinggan Tang, Hongying Zhou Estimation of Domain of Attraction for Discrete-Time Positive Interval Type-2 Polynomial Fuzzy Systems With Input Saturation, IEEE Transactions on Fuzzy Systems 30, no.22 (Feb 2022): 397–411.https://doi.org/10.1109/TFUZZ.2020.3039676Meng Han, H.K. Lam, Fucai Liu, Yinggan Tang More relaxed stability analysis and positivity analysis for positive polynomial fuzzy systems via membership functions dependent method, Fuzzy Sets and Systems (Dec 2020).https://doi.org/10.1016/j.fss.2020.12.015