Stability analysis and development of a class of fuzzy control systems

Precup, Radu‐Emil; Doboli, Simona; Preitl, Ștefan

doi:10.1016/s0952-1976(00)00002-6

Cited by 65 publications

(23 citation statements)

References 7 publications

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“…, are the weighting parameters, with the subscript indicating the process parameters indicated in Equation (5), * ρ is the optimal parameter vector, i.e., the optimal value of These two increments are obtained by discretizing the continuous-time PI controller by Tustin's method, which leads to the recurrent equation of the incremental discrete-time PI controller and its parameters K P and µ [27][28][29] …”

Section: Problem Settingmentioning

confidence: 99%

“…The stability of the fuzzy control is recommended to be taken into consideration in order to set the domain D ρ , and useful approaches are reported in [23,[27][28][29][31][32][33][34][35][36]. The objective function J α j (ρ) is referred to as the weighted sum of the absolute value of the control error and of the squared output sensitivity function, but the state sensitivity functions can be included as well.…”

Section: Problem Settingmentioning

confidence: 99%

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An Easily Understandable Grey Wolf Optimizer and Its Application to Fuzzy Controller Tuning

et al. 2017

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This paper proposes an easily understandable Grey Wolf Optimizer (GWO) applied to the optimal tuning of the parameters of Takagi-Sugeno proportional-integral fuzzy controllers (T-S PI-FCs). GWO is employed for solving optimization problems focused on the minimization of discrete-time objective functions defined as the weighted sum of the absolute value of the control error and of the squared output sensitivity function, and the vector variable consists of the tuning parameters of the T-S PI-FCs. Since the sensitivity functions are introduced with respect to the parametric variations of the process, solving these optimization problems is important as it leads to fuzzy control systems with a reduced process parametric sensitivity obtained by a GWO-based fuzzy controller tuning approach. GWO algorithms applied with this regard are formulated in easily understandable terms for both vector and scalar operations, and discussions on stability, convergence, and parameter settings are offered. The controlled processes referred to in the course of this paper belong to a family of nonlinear servo systems, which are modeled by second order dynamics plus a saturation and dead zone static nonlinearity. Experimental results concerning the angular position control of a laboratory servo system are included for validating the proposed method.

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Section: Problem Settingmentioning

confidence: 99%

Section: Problem Settingmentioning

confidence: 99%

An Easily Understandable Grey Wolf Optimizer and Its Application to Fuzzy Controller Tuning

et al. 2017

Self Cite

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“…Relevant process and control applications are presented in [35][36][37][38][39][40][41][42][43], with both crisp and fuzzy models. However, the online identification algorithms must be adapted accordingly in order to cope with the specific nonlinear elements and operating conditions of these processes [44][45][46][47][48][49][50].…”

Section: Introductionmentioning

confidence: 99%

Automotive Applications of Evolving Takagi-Sugeno-Kang Fuzzy Models

Precup¹,

Preitl²,

Bojan-Dragoş³

et al. 2017

FU Mech Eng

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Abstract. This paper presents theoretical and application results concerning the development of evolving Takagi-Sugeno-Kang fuzzy models for two dynamic systems, which will be viewed as controlled processes, in the field of automotive applications. The two dynamic systems models are nonlinear dynamics of the longitudinal slip in the Antilock Braking Systems (ABS) and the vehicle speed in vehicles with the Continuously

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“…Fuzzy-Rule-Based Systems (FRBSs) have been applied in applications such as control engineering, expert systems, pattern recognition, operation research, and decision support systems [1][2][3][4][5][6][7]. FRBS output is generated by an inference mechanism based on a knowledge base of IF-THEN rules.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Rule Interpolation and Extrapolation Techniques: Criteria and Evaluation Guidelines

Tikk

Johanyák

Kovács

et al. 2011

J. Adv. Comput. Intell. Intell. Inform.

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This paper comprehensively analyzes Fuzzy Rule Interpolation and extrapolation Techniques (FRITs). Because extrapolation techniques are usually extensions of fuzzy rule interpolation, we treat them both as approximation techniques designed to be applied where sparse or incomplete fuzzy rule bases are used, i.e., when classical inference fails. FRITs have been investigated in the literature from aspects such as applicability to control problems, usefulness regarding complexity reduction and logic. Our objectives are to create an overall FRIT standard with a general set of criteria and to set a framework for guiding their classification and comparison. This paper is our initial investigation of FRITs. We plan to analyze details in later papers on how individual techniques satisfy the groups of criteria we propose. For analysis, MATLAB FRI Toolbox provides an easy-to-use testbed, as shown in experiments.

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Stability analysis and development of a class of fuzzy control systems

Cited by 65 publications

References 7 publications

An Easily Understandable Grey Wolf Optimizer and Its Application to Fuzzy Controller Tuning

An Easily Understandable Grey Wolf Optimizer and Its Application to Fuzzy Controller Tuning

Automotive Applications of Evolving Takagi-Sugeno-Kang Fuzzy Models

Fuzzy Rule Interpolation and Extrapolation Techniques: Criteria and Evaluation Guidelines

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