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Fuzzy controller training using particle swarm optimization for nonlinear system control.

Cihan Karakuzu¹

¹Department of Electronics & Telecommunications Engineering, Engineering Faculty, Kocaeli University, 41040, Izmit-Kocaeli, Turkey.

ISA Transactions

|November 3, 2007

Summary

This study introduces particle swarm optimization (PSO) to train Takagi-Sugeno (TS) fuzzy controllers without requiring system derivatives. The PSO-trained fuzzy controller demonstrates effective control performance for nonlinear systems.

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Area of Science:

Control Engineering
Computational Intelligence
Nonlinear System Dynamics

Background:

Takagi-Sugeno (TS) fuzzy controllers are widely used for modeling nonlinear systems.
Training TS fuzzy controllers often requires gradient-based methods, limiting applicability when system models are unknown or derivatives are unavailable.
Particle Swarm Optimization (PSO) is a metaheuristic optimization algorithm inspired by social behavior.

Purpose of the Study:

To propose and describe an effective utilization of particle swarm optimization (PSO) for training Takagi-Sugeno (TS)-type fuzzy controllers.
To evaluate the performance of the proposed fuzzy training method on highly nonlinear systems.
To demonstrate the suitability of the PSO-based training for real-time implementation, particularly for systems with unknown models.

Main Methods:

Utilizing Particle Swarm Optimization (PSO) to optimize all parameters of a Takagi-Sugeno (TS) fuzzy controller.
Implementing the PSO-based training method without the need for partial derivatives with respect to controller parameters.
Conducting performance evaluations through simulations on two nonlinear systems: a continuous stirred tank reactor (CSTR) and a Van der Pol (VDP) oscillator.

Main Results:

The proposed PSO-based fuzzy training method successfully optimized controller parameters.
Simulation results demonstrated good control performance for both the CSTR and VDP oscillator systems.
The absence of derivative requirements makes the method suitable for systems with unknown dynamics.

Conclusions:

Particle Swarm Optimization (PSO) provides an effective and derivative-free approach for training Takagi-Sugeno (TS) fuzzy controllers.
The developed method exhibits robust control performance on complex nonlinear systems.
This technique enhances the applicability of fuzzy control in scenarios with unknown system models and supports real-time implementation.