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This paper investigates the problem of the high precision tracking control of piezoelectric actuators (PEAs) without using the inverse of the uncertain hysteresis. Based on fuzzy system approximator and particle swarm optimization (PSO) algorithm, a proposed enhanced  adaptive controller is developed. The proposed controller provides fast and robust adaptation simultaneously with guaranteed desired transient performance. Moreover, it has a simple form and requires fewer adaptation parameters. The adaptation gain is determined via PSO algorithm. The proposed controller is tested on a lab-scale PEA system.Experimental results with comparative studies with different techniques have been developed. The simulation results reveal that the proposed controller outperforms the other controllers in terms of normalized root-mean-square and maximum tracking errors for different frequencies.

In this paper, a new method for designing a fractional-order proportional-integral-derivative (FO-PID) controller with an application to an induction motor drive is proposed. In the proposed method, the motor drive is modeled using an autoregressive with exogenous input (ARX) model whose parameters are experimentally identified using real I/O data. A genetic algorithm is then used to find the FO-PID parameters. To guarantee the robustness of the controller against load variations, minimax optimization is adopted. To validate the results, the proposed controller is applied to a real-life motor drive using a hardware-in-the-loop (HIL) simulator. The experimental results show that the proposed controller significantly improves the time response of the induction motor compared to a conventional PID controller. It also shows it is robust against motor load variations.