The conventional Iterative Learning Control (ILC) process can potentially excite rich frequency contents based on past error history and injects them into the updated learning process. Nevertheless, the learnable error signals should be extracted, and the non-learnable error signals should be separated by adaptive bandwidth filter before the updated command is injected into next repetition. This paper proposes a new particle swarm optimization (PSO) algorithm by combining several hybrid terms adopted from literatures for the new synergy properties of local and global searches based on cognitive and social terms. This algorithm is used to adjust the three proportional-integral-derivative controller gains, the Anticipatory Iterative Learning Control (AILC) learning gain, and the cutoff bandwidth of the Butterworth filter associated with AILC. Developed hybrid PSO algorithm is devised for the updating velocity terms. The new synthesis AILC control law with adaptive learning gain, bandwidth-tuning filter and PID control gains were optimized by PSO and facilitated the improvement of the learning process and positioning accuracy. Numerical simulations were conducted and compared with the literature's P-type AILC for a linear synchronous motor. The tracking error through the adaptive learning processes was reduced successfully by shaping a new updated input trajectory at every repetition. The experimental results confirm the effectiveness of the new PSO-AILC for ultra-fine positioning in a one-axis linear motor.
All Science Journal Classification (ASJC) codes
- Statistics and Probability
- Artificial Intelligence