Direct adaptive fuzzy control with a self-structuring algorithm

Phan, Phi Anh; Gale, Timothy J.

doi:10.1016/j.fss.2007.09.012

Cited by 76 publications

(38 citation statements)

References 26 publications

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“…In this note, we use a PDC controller and we based it on the relationship (10). The displacement of the α(t) midpoint to both left or right is directed by the position of the premise variable Z(t), i.e., if the premise variable is set to the left of the midpoint, then α(t) must approach to the minimum α by the function defined in relationship (24). If the premise variable is set to the right of the midpoint, then α(t) must approach to the maximum α by the function defined in relationship (24).…”

Section: Direction Criterion Displacement Of the α(T) Midpointmentioning

confidence: 99%

New time-varying fuzzy sets based on a PSO midpoint of the universe of discourse

Ziani

2016

Int. J. Autom. Comput.

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The paper presents a robust parallel distributed compensation (PDC) fuzzy controller for a nonlinear and certain system in continuous time described by the Takagi-Sugeno (T-S) fuzzy model. This controller is based on a new type of time-varying fuzzy sets (TVFS). These fuzzy sets are characterized by displacement of the kernels to the right or left of the universe of discourse, and they are directed by a well-defined criterion. In this work, we only focused on the movement of midpoint of the universe. The movements of this midpoint are optimized by particle swarm optimization (PSO) approach.

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Section: Direction Criterion Displacement Of the α(T) Midpointmentioning

confidence: 99%

New time-varying fuzzy sets based on a PSO midpoint of the universe of discourse

Ziani

2016

Int. J. Autom. Comput.

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“…Therefore, in order to start the control of the plant using very limited information, we need to use advanced adaptive skills that exceed the aforementioned adaptive process, i.e., more internal parameters of the used controller need to be adapted and optimized. Nevertheless, important number of works that appear in the literature present adaptive controllers with prefixed structure [20,21,22], hence, there are some risks that the plant may not be adequately represented [19]. The methodology suggested in this work is based on the use of large amount of data provided from the real behavior of the control process; the Input/output data extracted in real time can reflect important information about the real inverse function of the controlled plant.…”

Section: ͵ǥ͵ǥʹǥ ǧǣmentioning

confidence: 99%

A New Adaptive and Self Organizing Fuzzy Policy to Enhance the Real Time Control Performance

Lasri

Rojas

Pomares

et al. 2014

IJCIS

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In this paper, a temperature control in real time control process was presented using several control algorithms. A quantitative comparison based on the real power consumption and (the precision and the robustness) of these controllers during the same control process and under the same conditions will be done. The proposed Adaptive and Self Organizing Fuzzy policy has been able to prove its superiority against the remaining controllers. The new Adaptive and Self Organizing Fuzzy Logic Controller starts the control with a very limited information about the controlled process (delay and the monotonicity sign) and without any kind of offline pre-training, the adaptive controller acts online to collect the necessary background to adapt their rules consequents and to self organize their membership functions from the real behavior of the controlled process. During 200 minutes and under the same conditions all the performed controllers have been used to control the room temperature, each simulation has been repeated five times with two different sets of set points. These amounts of results was used as a set of sampling for the statistical tool ANOVA (Analysis of Variance) that can prove and illustrate the validity and the extrapolability of the conclusions extracted from several stages of this work.

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“…[15][16][17] Others have used adaptive and optimal control for better application [18][19][20] and fuzzy systems have also been used for satellite attitude control. [21][22][23] In our case, an adaptive fuzzy controller [24][25][26] is employed. Two major problems are considered in the design process.…”

Section: Introductionmentioning

confidence: 99%

Adjustable Adaptive Fuzzy Attitude Control using Nonlinear SISO Structure of Satellite Dynamics

Moradi

Esmaelzadeh

Ghasemi

2012

Trans. Japan Soc. Aero. S Sci.

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This paper presents a method for three-dimensional attitude stabilization of a satellite. The pitch loop of the satellite is controlled by a momentum wheel; whereas the roll/yaw loops are stabilized using two magnetic torques along their respective axes. In order to design an efficient controller, the stability conditions are considered based on a nonlinear model of system. An adjustable adaptive fuzzy system is proposed as the method to design the controller. The span of membership functions are tuned using errors of fuzzy inputs with respect to their references. Results show that fuzzy sets cover all variations of fuzzy inputs and optimal fuzzy output is gained. The Lyapunov synthesis method is used to prove the stability of the closed-loop system. The efficiency of the controller in converging of the position error to close to zero is also shown using some numerical simulations.

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Direct adaptive fuzzy control with a self-structuring algorithm

Cited by 76 publications

References 26 publications

New time-varying fuzzy sets based on a PSO midpoint of the universe of discourse

New time-varying fuzzy sets based on a PSO midpoint of the universe of discourse

A New Adaptive and Self Organizing Fuzzy Policy to Enhance the Real Time Control Performance

Adjustable Adaptive Fuzzy Attitude Control using Nonlinear SISO Structure of Satellite Dynamics

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