Model-free data driven control for trajectory tracking of an amplified piezoelectric actuator

Micro/nanopositioning systems commonly use piezoelectric actuators due to their high stiffness, fast response and ultra-high precision. However, three main factors affect their tracking performance, namely hysteresis, creep, and structural vibrations. To overcome these limitations, this paper proposes a new combined feedback and feedforward control strategy. Unlike most existing control algorithms for micro/nanopositioning systems, the new controller is a model-free learning-based capable of smoothly tracking continuous reference signals. It is further endowed with an ability to prevent fallacious learning associated with sensor noise and reference signal discontinuities. The paper also provides complete proofs for the convergence of the tracking error and boundedness of the control signals. Experimental trajectory tracking results obtained using the proposed controller applied on a commercially available amplified piezoelectric actuator verify the theoretical findings.