NEURAL PREDICTIVE FORCE CONTROL FOR A HYDRAULIC ACTUATOR: SIMULATION AND EXPERIMENT

Abstract

This paper proposes a neural-based predictive control algorithm for online control of a force-acting industrial hydraulic actuator. In the algorithm, a multilayer feedforward neural network is employed to modeling the highly nonlinear hydraulic actuator. The nonlinear neural model is instantaneously linearized at each sampling point. Estimated parameters from the linearized model are used in the generalized predictive control (GPC) algorithm to control the contact force. Simulation and experimental results show that the neural-based predictive controller can adapt to different environments and keep the contact force in a desired value despite high nonlinearity and uncertainty in the hydraulic actuator system.

The experiments were conducted at the Experimental Robotics and Tele-Operation Laboratory at the University of Manitoba. The author thanks Drs. Nariman Sepehri, Kamayar Ziaei, and David Niksefat for their many helpful discussions and technical supports to the research work.