Dynamic Output Feedback Controller Design for Uncertain Takagi–Sugeno Fuzzy Systems: A Premise Variable Selection Approach

Tognetti, Eduardo S.; Linhares, Tássio Melo

doi:10.1109/tfuzz.2020.2981931

Cited by 12 publications

(5 citation statements)

References 37 publications

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“…Proof: See Appendix B. Remark 5: It is easy to see that equality (12) includes the function in (13). The main purpose of introducing equality (12) in our article is to deduce another presentation of grades of membership of the closed-loop system to make the following process easy.…”

Section: Resultsmentioning

confidence: 99%

Quantized Interval Type-2 Fuzzy Control for Persistent Dwell-Time Switched Nonlinear Systems With Singular Perturbations

Wang

Liu

Xia

et al. 2022

IEEE Trans. Cybern.

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This article investigates the problem of quantized fuzzy control for discrete-time switched nonlinear singularly perturbed systems, where the singularly perturbed parameter (SPP) is employed to represent the degree of separation between the fast and slow states. Taking a full account of features in such switched nonlinear systems, the persistent dwell-time switching rule, the technique of singular perturbation and the interval type-2 Takagi-Sugeno fuzzy model are introduced. Then, by means of constructing SPP-dependent multiple Lyapunov-like functions, some sufficient conditions with the ability to ensure the stability and an expected H ∞ performance of the closed-loop system are deduced. Afterward, through solving a convex optimization problem, the gains of the controller are obtained. Finally, the correctness of the proposed method and the effectiveness of the designed controller are demonstrated by an explained example. Index Terms-Interval type-2 Takagi-Sugeno (IT2 T-S) fuzzy model, persistent dwell-time (PDT) switching rule, quantized control, singularly perturbed nonlinear systems. I. INTRODUCTION S OME parasitic parameters, such as small time constants, capacitances, and inductances, may increase the order of dynamic equations and lead to the numerical ill-conditioning problem. In order to overcome these obstacles, the singular perturbation technique is usually employed. Examples of singularly perturbed systems (SPSs) are power systems, airplanes, and so on [1]-[3]. When discretizing continuous-time SPSs, the sampling period influences the discrete-time model, which

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Section: Resultsmentioning

confidence: 99%

Quantized Interval Type-2 Fuzzy Control for Persistent Dwell-Time Switched Nonlinear Systems With Singular Perturbations

Wang

Liu

Xia

et al. 2022

IEEE Trans. Cybern.

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show abstract

“…Due to the non-negative value for the function, x min must be forced to 0. The membership functions can be defined as follows: (30) where F 1 z 1 and F 2 z 1 consequents are x max and x min = 0 respectively.…”

Section: Type Iii: Z 1 (T) = X(t)mentioning

confidence: 99%

“…(1) (first-order model) of non-linear models. The premise variables are known functions which may depend on the state variables and/or time [30].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Modeling Framework Using Sector Non-Linearity Techniques for Fixed-Wing Aircrafts

Brusola,

Garcia-Nieto,

Salcedo

et al. 2024

Aerospace

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This paper presents a mathematical modeling approach utilizing a fuzzy modeling framework for fixed-wing aircraft systems with the goal of creating a highly desirable mathematical representation for model-based control design applications. The starting point is a mathematical model comprising fifteen non-linear ordinary differential equations representing the dynamic and kinematic behavior applicable to a wide range of fixed-wing aircraft systems. Here, the proposed mathematical modeling framework is applied to the AIRBUS A310 model developed by ONERA. The proposed fuzzy modeling framework takes advantage of sector non-linearity red techniques to recast all the non-linear terms from the original model to a set of combined fuzzy rules. The result of this fuzzification is a more suitable mathematical description from the control system design point of view. Therefore, the combination of this fuzzy model and the wide range of control techniques available in the literature for such kind of models, like parallel and non-parallel distributed compensation control laws using linear matrix inequality optimization, enables the development of control algorithms that guarantee stability conditions for a wide range of operations points, avoiding the classical gain scheduling schemes, where the stability issues can be extremely challenging.

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“…The conceptualization of the fuzzy inference system (FIS) was marked by the introduction of two distinguished methods, namely the Mamdani and the Takagi-Sugeno (T-S) methods [36][37][38][39]. The former relies on fuzzy outputs that require a subsequent defuzzification, while the latter employs fired multilinear equations followed by a weighted averaging technique.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, unlike prior works in refs. [25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42] which completely rely on designers' expert knowledge to relate the fuzzy inputs and outputs, this study avoids complete dependency on expert knowledge by utilizing the gathered knowledge of an RL agent after extensive training in a simulated environment.…”

Section: Introductionmentioning

confidence: 99%

An improved fuzzy inference strategy using reinforcement learning for trajectory-tracking of a mobile robot under a varying slip ratio

Zaman,

2024

Robotica

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In this study, a fuzzy reinforcement learning control (FRLC) is proposed to achieve trajectory tracking of a differential drive mobile robot (DDMR). The proposed FRLC approach designs fuzzy membership functions to fuzzify the relative position and heading between the current position and a prescribed trajectory. Instead of fuzzy inference rules, the relationship between the fuzzy inputs and actuator voltage outputs is built using a reinforcement learning (RL) agent. Herein, the deep deterministic policy gradient (DDPG) methodology consisted of actor and critic neural networks is employed in the RL agent. Simulations are conducted with considering varying slip ratio disturbances, different initial positions, and two different trajectories in the testing environment. In the meantime, a comparison with the classical DDPG model is presented. The results show that the proposed FRLC is capable of successfully tracking different trajectories under varying slip ratio disturbances as well as having performance superiority to the classical DDPG model. Moreover, experimental results validate that the proposed FRLC is also applicable to real mobile robots.

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Dynamic Output Feedback Controller Design for Uncertain Takagi–Sugeno Fuzzy Systems: A Premise Variable Selection Approach

Cited by 12 publications

References 37 publications

Quantized Interval Type-2 Fuzzy Control for Persistent Dwell-Time Switched Nonlinear Systems With Singular Perturbations

Quantized Interval Type-2 Fuzzy Control for Persistent Dwell-Time Switched Nonlinear Systems With Singular Perturbations

Fuzzy Modeling Framework Using Sector Non-Linearity Techniques for Fixed-Wing Aircrafts

An improved fuzzy inference strategy using reinforcement learning for trajectory-tracking of a mobile robot under a varying slip ratio

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