A neural network-based model predictive controller for displacement tracking of piezoelectric actuator with feedback delays

International Journal of Advanced Robotic Systems

article

Zhangming Du¹  Chao Zhou¹  Zhiqiang Cao¹  Shuo Wang¹  Long Cheng¹  Min Tan¹ 

[1] Institute of Automation, Chinese Academy of Sciences;University of Chinese Academy of Sciences;CAS Center for Excellence in Brain Science and Intelligence Technology

关键词: Piezoelectric actuator;    neural network;    displacement tracking;    model predictive control;   

DOI  :  10.1177/17298814211057698

学科分类：社会科学、人文和艺术（综合）

来源: InTech

【 摘 要 】

Piezoelectric actuators are widely used in micro/nanoscale robotic manipulators. Due to its hysteresis and dynamic-related nonlinearity, accurate displacement tracking control of piezoelectric actuator is challenging. Besides, in some low-cost practical systems with low sampling rate, transmission delay causes mismatches between feedback and real displacement, further increasing the challenge in tracking control. In this article, a neural network-based model predictive controller (MPC) is proposed for precise tracking control of piezoelectric actuator’s displacement in situation where feedback is slow and delayed. The prediction model is based on a nonlinear-autoregressive-moving-average-with-exogenous-inputs framework, which outputs entire prediction horizon of future displacement in a single time, and is fulfilled by a multilayer feedforward neural network. An extended Kalman filter-based estimation for displacement is introduced to relieve the influence of feedback delays so as to improve dynamic performance of the controller. Another neural network is trained to provide initial values for MPC to reduce computation costs and improve performance in dynamic tracking. In a series of tracking experiments, the effectiveness of proposed controller is verified.

【 授权许可】

CC BY