Abstract
Fuzzy logic controllers (FLC) have long been successfully implemented for effective tracking control and regulation of nonlinear dynamical systems. The optimization of parameters of a fuzzy logic controller is the focus of research in the domain of evolutionary computation (EC). The parameters include membership functions and rule sets. In this chapter we solve this optimization problem using three different algorithms. These are simple genetic algorithm (SGA), differential evolution (DE) algorithm and univariate marginal distribution algorithm (UMDA). Like simple genetic algorithm, differential evolution is an exceptionally simple, fast, and robust population based search algorithm that is able to locate near-optimal solutions to difficult problems. In contrast, univariate marginal distribution algorithm is a purely probabilistic search strategy over the possible solution space. We have selected two nonlinear control system problems for application. In the domain of process control, control of pH poses a difficult problem because of inherent nonlinearities and frequently changing process dynamics. The efficacy of DE over SGA is shown through the design of FLC for a pH neutralization process. This result is also verified through successful implementation on a laboratory scale pH plant setup. The next problem is the control of an one-link robot manipulator. The fuzzy model of the robot inverse dynamics in conjunction with a fuzzy PD (proportional plus derivative) controller is optimized using univariate marginal distribution algorithm and compared with SGA. Fuzzy UMDA model is found to be more accurate as compared to fuzzy SGA model. However, fuzzy SGA and UMDA controllers do fare well on equal footing and their performances are accurate and robust to model uncertainties.
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Behera, L. (2004). Parametric Optimization of a Fuzzy Logic Controller for Nonlinear Dynamical Systems using Evolutionary Computation. In: New Optimization Techniques in Engineering. Studies in Fuzziness and Soft Computing, vol 141. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39930-8_19
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DOI: https://doi.org/10.1007/978-3-540-39930-8_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-05767-0
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