The Effect of Iterative Learning Control on the Force Control of a Hydraulic Cushion

Abstract

An iterative learning control (ILC) algorithm is presented for the force control circuit of a hydraulic cushion. A control scheme consisting of a PI controller, feed-forward (FF) and feedback-linearization is first derived. The uncertainties and nonlinearities of the proportional valve, the main system actuator, prevent the accurate tracking of the pressure reference signal. Therefore, an extra ILC FF signal is added to counteract the valve model uncertainties. The unknown valve dynamics are attenuated by adding a fourth-order low-pass filter to the iterative learning control design, which is split up into two second-order low-pass filters to carry out forward and backward filtering and obtain zero-phase filtering. The addition of the ILC signal presents significant improvements in terms of settling time and overshoot of the pressure signal in the cylinder.

Combining reinforcement learning and conventional control to improve automatic guided vehicles tracking of complex trajectories

“…Iterative learning has been widely used to model learning processes (Trojaola et al, 2020). Reinforcement learning is one of these computational learning approaches.…”