Nonlinear system identification and control using a real-coded genetic algorithm

Chang, Wei‐Der

doi:10.1016/j.apm.2005.11.024

Cited by 115 publications

(60 citation statements)

References 14 publications

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“…Genetic algorithm (GA; Goldberg, 1989), simulated annealing (SA; Kirkpatrick et al, 1983), ant colony optimization (ACO; Dorigo et al, 1996), and particle swarm optimization (PSO; Kennedy and Eberhart, 1995) are four well-known classes of such global optimization methods. The heuristic algorithms are widely used in solving system identification and filter modeling problems (Valarmathi et al, 2009;Chang, 2007;Eftekhari and Katebi, 2008;Chen and Luk, 1999;Howell and Gordon, 2001;Karaboga et al, 2004;Kalinli and Karaboga, 2005;Das and Konar, 2007;Lin et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

Filter modeling using gravitational search algorithm

Rashedi

Nezamabadi‐pour

Saryazdi

2011

Engineering Applications of Artificial Intelligence

318

118

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Section: Introductionmentioning

confidence: 99%

Filter modeling using gravitational search algorithm

Rashedi

Nezamabadi‐pour

Saryazdi

2011

Engineering Applications of Artificial Intelligence

318

118

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“…There are three main types of selection methods: fitness proportionate selection, ranking method and tournament selection. Tournament selection [20][21][22] is used in this work. In tournament selection, 'n' individuals are selected randomly from the population, and the best of the 'n' is inserted into the new population for further genetic processing.…”

Section: Reproductionmentioning

confidence: 99%

Real-coded genetic algorithm for system identification and controller tuning

Valarmathi

Devaraj

Radhakrishnan

2009

Applied Mathematical Modelling

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a b s t r a c tThis paper presents an application of real-coded genetic algorithm (RGA) for system identification and controller tuning in process plants. The genetic algorithm is applied sequentially for system identification and controller tuning. First GA is applied to identify the changes in system parameters. Once the process parameters are identified, the optimal controller parameters are identified using GA. In the proposed genetic algorithm, the optimization variables are represented as floating point numbers. Also, cross over and mutation operators that can directly deal with the floating point numbers are used. The proposed approach has been applied for system identification and controller tuning in nonlinear pH process. The simulation results show that the GA based approach is effective in identifying the parameters of the system and the nonlinearity at various operating points in the nonlinear system.

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“…Therefore, designing the controller which is less dependent on the crisp dynamic modeling of the system is a crucial task. In recent years, biological intelligent computations have been saliently used in the various areas of investigations [2]. Application of fuzzy logic, neural networks, and evolutionary algorithms in solving rigid problems particularly when dealing with uncertainties, result in a strong tendency toward this approaches [3].…”

Section: Introductionmentioning

confidence: 99%

Applying a Novel Fuzzy-PI Controller on the Model of Continuous Stirred Tank Reactor

Yazdani¹,

Movahed²,

MahmoudZadeh³

2011

IJMLC

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Abstract-Continuous Stirred Tank Reactor (CSTR)presents a significant role in the most of chemical process industries. The inherent nonlinear dynamic of CSTR results in various difficulties of stabilizing and controlling of the process. Likewise, functional changes with elapsing of the time and different disturbances associated with real environment are the other hints which must put into perspective. In this paper, a novel strategy based on the combination of fuzzy gain scheduling and a socio-political optimization algorithm, imperialist competitive algorithm, has been employed to design an intelligent Fuzzy-PI controller so that it surmounts the problem of system's parameter variation throughout the controlling of CSTR temperature. The controller has two inputs which are error and derivative of error and two outputs that are the proportional gain and integral gain. The controller is updated continuously to track the changes which are occurred in the system. To obtain the high performance control, imperialist competitive algorithm was employed to determine the optimum membership functions of the input and output variables. The performance of the proposed controller has been consider based on the Sum of the Square Error (SSE) and Integral Absolute Error(IAE) Criteria. In addition, conventional PI controller and classic online PI controller were applied to the system for the performance comparison. The results clearly indicate the robustness of Fuzzy-PI controller in dealing with uncertainties throughout the tracking of the reference signal. Fuzzy-PI controller also embodies an outstanding accuracy of tracking which leads to the least tracking error criterion.

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Nonlinear system identification and control using a real-coded genetic algorithm

Cited by 115 publications

References 14 publications

Filter modeling using gravitational search algorithm

Filter modeling using gravitational search algorithm

Real-coded genetic algorithm for system identification and controller tuning

Applying a Novel Fuzzy-PI Controller on the Model of Continuous Stirred Tank Reactor

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