Predictive control by local linearization of a Takagi-Sugeno fuzzy model

Roubos, Johannes A.; Babuška, Robert; Bruijn, P.M.; Verbruggen, H.B.

doi:10.1109/fuzzy.1998.687455

Cited by 23 publications

(12 citation statements)

References 12 publications

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“…In [16] a backpropagation neural network algorithm was used to adjust parameters of the PID controller. The authors of [17] utilized Sugeno fuzzy model as the model structure for a linear model based predictive control of the liquid level.…”

Section: Co-published By Atlantis Press and Taylor And Francismentioning

confidence: 99%

NN-SANARX model based control of a multi tank liquid-level system

Belikov¹,

Petlenkov²

2015

IJCIS

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This paper is devoted to application of neural network with a specific restricted connectivity structure for identification and control of a real process. Parameters of the identified model are used to design a controller based on dynamic feedback linearization. The designed neural network based controller is verified on mathematical model within MATLAB/Simulink environment and applied to the real-time control of a laboratory plant. Water tank system was chosen as a case study to illustrate applicability of the proposed approach. Several experimental results as well as comparison with some alternative methods are provided.

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Section: Co-published By Atlantis Press and Taylor And Francismentioning

confidence: 99%

NN-SANARX model based control of a multi tank liquid-level system

Belikov¹,

Petlenkov²

2015

IJCIS

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“…The optimization problem given by (15) is a constrained nonlinear and nonconvex optimization problem, the solution of which is difficult and generally expensive in computing time. Different approaches were investigated to solve this problem, such as the numerical optimization techniques [14,15], the metaheuristic based optimization algorithms [16][17][18], the linearization of the process fuzzy model [19], and the use of particular model structures to obtain a convex form for the cost function [20].…”

Section: Design Of the Linear Control Lawmentioning

confidence: 99%

Parameter Optimization of MIMO Fuzzy Optimal Model Predictive Control By APSO

2017

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This paper introduces a new development for designing a Multi-Input Multi-Output (MIMO) Fuzzy Optimal Model Predictive Control (FOMPC) using the Adaptive Particle Swarm Optimization (APSO) algorithm. The aim of this proposed control, called FOMPC-APSO, is to develop an efficient algorithm that is able to have good performance by guaranteeing a minimal control. This is done by determining the optimal weights of the objective function. Our method is considered an optimization problem based on the APSO algorithm. The MIMO system to be controlled is modeled by a Takagi-Sugeno (TS) fuzzy system whose parameters are identified using weighted recursive least squares method. The utility of the proposed controller is demonstrated by applying it to two nonlinear processes, Continuous Stirred Tank Reactor (CSTR) and Tank system, where the proposed approach provides better performances compared with other methods.

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“…The objective of the controller in the level control is to maintain a level set point at a given value and be able to accept new set point values dynamically. The conventional proportional-integralderivative (PID) is commonly utilized in controlling the level, but the parameter of those controllers must be turned by tuning method either in time response or frequency response to meet their required performances [1,2]. On the other hand, the neural controller is also popularly implemented in many practical industrial automation applications.…”

Section: Introductionmentioning

confidence: 99%