Adaptive Model Estimation of Vibration Motion for a Nanopositioner With Moving Horizon Optimized Extended Kalman Filter

Fast, reliable online estimation and model adaptation is the first step towards high-performance model-based nanopositioning control and monitoring systems. This paper considers the identification of parameters and the estimation of states of a nanopositioner with a variable payload based on the novel moving horizon optimized extended Kalman filter (MHEKF). The MHEKF is experimentally tested and verified with measured data from the capacitive displacement sensor. The payload, attached to the nanopositioner's sample platform, suddenly changes during the experiment triggering the transient motion of the vibration signal. The transient is observed through the load dependent parameters of a single-degree-of-freedom vibration model, such as spring, damping, and actuator gain constants. The platform, before and after the payload change, is driven by the excitation signal applied to the piezoelectric actuator. The information regarding displacement and velocity, together with the system parameters and a modeled force disturbance, is estimated through the algorithm involving the iterative sequential quadratic programming (SQP) optimization procedure defined on a moving horizon window. The MHEKF provided superior performance in comparison with the benchmark method, extended Kalman filter (EKF), in terms of faster convergence.