A Novel Heavy Payload High-Resolution Actuator System: Design, Modeling, and Experiments

Abstract

The performance of actuator in nanopositioning system is significant to guarantee the rapidity and accuracy of closed-loop positioning control. However, the current actuators exhibit deficiencies including limited driving force and low accuracy because of the conflicting relationship between stiffness and resolution. In this article, a novel heavy payload high-resolution actuator (HPHRA) system is designed based on hydraulic transmission principle for nanopositioning robotic application. To achieve an accurate model, a compensatory Hammerstein model recognition strategy is proposed to capture the internal different physical characteristics, which is named compensatory nonlinear linear model (CNLM). The linear dynamics is captured by a linear transfer function, and the nonlinear dynamics is captured by a hysteresis PI model with several backlash operators. The residuals between nonlinear linear Hammerstein model and actual position, which is caused by external load, are compensated by a neural network. The CNLM recognition strategy is developed based on the regularized least square algorithm, singular value decomposition, and gradient descent algorithm. Experimental evidence on the HPHRA confirms the efficacy of the proposed CNLM method. The nanopositioning control in a 12-DOF macro–micro double parallel (12-MMDPR) robot under heavy load provides evidence of the HPHRA, CLNM strategy, and composite controller.