Adaptive fuzzy APSO based inverse tracking-controller with an application to DC motors
This paper introduces the use of the adaptive particle swarm optimization (APSO) for adapting the weights of fuzzy neural networks (FNN) on line. The fuzzy neural network is used for identification of the dynamics of a DC motor with nonlinear load torque. Then the motor speed is controlled using an inverse controller to follow a required speed trajectory. The parameters of the DC motor are assumed unknown as well as the nonlinear load torque characteristics. In the first stage a nonlinear fuzzy neural network (FNN) is used to approximate the motor control voltage as a function of the motor speed samples. In the second stage, the above mentioned approximator is used to calculate the control signal (the motor voltage) as a function of the speed samples and the required reference trajectory. Unlike the conventional back-propagation technique, the adaptation of the weights of the FNN approximator is done on-line using adaptive particle swarm optimization (APSO). The APSO is based on the least squares error minimization with random initial condition and without any off-line pre-training. Simulation results are presented to prove the effectiveness of the proposed control technique in achieving the tracking performance.
fuzzy neural networks (fnn) particle swarm optimization (pso) least squares (ls) system identification inverse control (ic)