Designing MIMO controller by neuro-traveling particle swarm optimizer approach

Su, Chwen-Tzeng; Wong, Jui-Tsung

doi:10.1016/j.eswa.2006.01.023

Cited by 18 publications

(7 citation statements)

References 20 publications

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“…Various researchers, such as Shi et al [32], Meissner et al [21], Chung-Song et al Linhai et al [20,33], and Jia [34] discuss cross-coupled algorithms in their research on PID controllers. Some notable investigations have been attempted to use evolutionary computation (EC) algorithms such as genetic algorithm [10,35], particle swarm optimization [36], covariance matrix adaptation evolution state [37], and diferential search algorithm (DSA) [38] to optimize the single-objective process (i.e., minim [39]). Many researchers have made signifcant contributions to the use of fuzzy systems in MIMO (multiple input multiple output) control systems to improve input/ output membership functions and controls [12,17,[40][41][42][43][44].…”

Section: Twin Rotor and Pid Controllers-a Reviewmentioning

confidence: 99%

Design of Cross‐Coupled Nonlinear PID Controller Using Single‐Objective Evolutionary Algorithms

Sivadasan¹,

Iruthayarajan

Stonier

et al. 2023

Mathematical Problems in Engineering

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The effectiveness of evolutionary algorithms (EAs) such as differential search algorithm (DSA), Real-Coded genetic algorithm with simulated binary crossover (RGA-SBX), particle swarm optimization (PSO), and chaotic gravitational search algorithm (CGSA) on the optimal design of cross-coupled nonlinear PID controllers is compared in this paper. A cross-coupled multivariable PID controller structure for the binary distillation column was developed with two inputs and two outputs. EA simulations are run to lower IAE using two stopping criteria, namely, maximum number of functional evaluations (Fevalmax) and Fevalmax plus PID parameter and IAE tolerance. Over 20 separate trials were used to compare the performances of several EAs using statistical measures such as best, mean, standard deviation of outcomes, and average calculation time. This article presents the design of a cross-coupled nonlinear PID controller using single-objective evolutionary algorithms. Using evolutionary algorithms (EAs) with a multicrossover strategy, the results achieved by various EAs are compared to previously reported results. The results of a multivariable cross-coupled system clearly show that a single-objective nonlinear PID controller performs better. Simulations further show that all four techniques evaluated are suitable for PID controller tweaking off-line. However, only the single-objective evolutionary algorithms are acceptable for online PID tuning due to their higher consistency and shorter computation time.

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Section: Twin Rotor and Pid Controllers-a Reviewmentioning

confidence: 99%

Design of Cross‐Coupled Nonlinear PID Controller Using Single‐Objective Evolutionary Algorithms

Sivadasan¹,

Iruthayarajan

Stonier

et al. 2023

Mathematical Problems in Engineering

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“…Among the methods used to control MIMO processes, PID controllers have been deployed extensively due to their less complexity, high performance and easy implementation (Xiong et al, 2007;García-Alvarado et al, 2005;Halevi et al, 1997;Ruiz-López et al, 2006). Also some numerical search strategies are proposed in the literature, to design MIMO controllers by minimizing suitable cost functions (Chang, 2007;Hsin-Chieh et al, 2008;Su and Wong, 2007).…”

Section: Introductionmentioning

confidence: 99%

Colonial competitive algorithm

Gargari

Hashemzadeh

Rajabioun

et al. 2008

International Journal of Intelligent Computing and Cybernetics

212

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Purpose-This paper aims to describe colonial competitive algorithm (CCA), a novel socio-politically inspired optimization strategy, and how it is used to solve real world engineering problems by applying it to the problem of designing a multivariable proportional-integral-derivative (PID) controller. Unlike other evolutionary optimization algorithms, CCA is inspired from a socio-political process-the competition among imperialists and colonies. In this paper, CCA is used to tune the parameters of a multivariable PID controller for a typical distillation column process. Design/methodology/approach-The controller design objective was to tune the PID controller parameters so that the integral of absolute errors, overshoots and undershoots be minimized. This multi-objective optimization problem is converted to a mono-objective one by adding up all the objective functions in which the absolute integral of errors is emphasized to be reduced as long as the overshoots and undershoots remain acceptable. Findings-Simulation results show that the controller tuning approach, proposed in this paper, can be easily and successfully applied to the problem of designing MIMO controller for control processes. As a result not only was the controlled process able to significantly reduce the coupling effect, but also the response speed was significantly increased. Also a genetic algorithm (GA) and an analytical method are used to design the controller parameters and are compared with CCA. The results showed that CCA had a higher convergence rate than GA, reaching to a better solution. Originality/value-The proposed PID controller tuning approach is interesting for the design of controllers for industrial and chemical processes, e.g. MIMO evaporator plant. Also the proposed evolutionary algorithm, CCA, can be used in diverse areas of optimization problems including, industrial planning, resource allocation, scheduling, decision making, pattern recognition and machine learning.

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“…One the other hand, controlling Multi Input Multi Output systems is not straight forward due to the coupling and interactions between channels. In last several decades, designing controllers for MIMO systems has attracted a lot of research interests (Christen et al, 1997: Roffel et al, 2000: Su et al, 2007. Among the proposed MIMO controllers, decentralized PID controllers have been deployed extensively due to their less complexity, high performance and easy implementation (Halevi et al, 1997: Su et al, 2007: Xiong et al, 2007.…”

Section: Introductionmentioning

confidence: 99%