Hybrid PSO-tuned PID and hysteresis-observer based control for piezoelectric micropositioning stages

Abstract

Piezo-actuated micropositioning stages consist of a piezoelectric actuator that operates a positioning system. Hysteresis nonlinearity is one of the significant variables limiting the positioning precision of these stages. This paper introduces a technique of developing a hybrid controller for a precise positioning tracking of a piezoelectric micropositioning system. Bouc-Wen nonlinear hysteresis model is utilized to denote the hysteresis nonlinear phenomenon of the piezo-actuated system. A hysteresis observer-based feedforward controller is designed based on Luenberger observer. This feedforward controller is then coupled with a particle swarm optimization (PSO)-based proportional-integral-derivative (PID) feedback controller to form a hybrid controller. A new fitness function is used to compute the optimal PID gains. This fitness function is intended to reduce the overshoot, steady-state error, and the rise and settling times. The findings of this work indicate that using the developed controller structure can significantly decrease the hysteresis effect. In addition, the proposed structure shows the ability to reduce the error is to 0.046% of the maximum displacement range. Such performance demonstrates that the proposed hybrid control structure is efficient for precise micropositioning applications.Piezo-actuated micropositioning stages consist of a piezoelectric actuator that operates a positioning system. Hysteresis nonlinearity is one of the significant variables limiting the positioning precision of these stages. This paper introduces a technique of developing a hybrid controller for a precise positioning tracking of a piezoelectric micropositioning system. Bouc-Wen nonlinear hysteresis model is utilized to denote the hysteresis nonlinear phenomenon of the piezo-actuated system. A hysteresis observer-based feedforward controller is designed based on Luenberger observer. This feedforward controller is then coupled with a particle swarm optimization (PSO)-based proportional-integral-derivative (PID) feedback controller to form a hybrid controller. A new fitness function is used to compute the optimal PID gains. This fitness function is intended to reduce the overshoot, steady-state error, and the rise and settling times. The findings of this work indicate that using the developed controller structure can significantly decrease the hysteresis effect. In addition, the proposed structure shows the ability to reduce the error is to 0.046% of the maximum displacement range. Such performance demonstrates that the proposed hybrid control structure is efficient for precise micropositioning applications.