Chapter Five - High-precision tracking of piezoelectric actuator using iterative learning control and direct inverse compensation of hysteresis

Abstract

Rate-dependent hysteresis nonlinearity, which is an inherent characteristic of piezoelectric actuators (PEAs), causes a significant challenge in precise motion control of piezoelectric nanopositioning stages using PEA. In this chapter, a control strategy is proposed to compensate for both nonlinear hysteresis and uncompleted modeled error. The main contribution of this work is that our approach combines iterative learning control (ILC) with the inverse of hysteresis model proposed to accomplish the tacking missions. ILC is essentially a feedforward control scheme that fully utilizes the prior control information, and is able to further reduce repeatable tacking error. Different from existing direct inverse compensation method whose implementations rely on the accuracy of the hysteresis model, the proposed control strategy is more effective by adding ILC algorithm to the control loop of the inverse compensator. In this chapter, a Hammerstein structure is applied to model the piezoelectric hysteresis first. Afterwards, the proposed controller is designed and implemented. With only a few iterations, the tacking errors are reduced progressively. Some comparative experiments are also conducted. Results show that the proposed control strategy is superior to proportional integral (PI) control and the PI control combined with inverse compensator in terms of positioning accuracy. Specifically, the tracking errors are decreased by 85.6% and 77.3%, respectively.