Iterative Learning Applied to Hydraulic Pressure Control

Abstract

This paper addresses a performance limiting phenomenon that may occur in the pressure control of hydraulic actuators subjected to external velocity disturbances. It is demonstrated that under certain conditions a severe peaking of the control error may be observed that significantly degrades the performance of the system due to the presence of nonlinearities. The phenomenon is investigated numerically and experimentally using a system that requires pressure control of two hydraulic cylinders. It is demonstrated that the common solution of feed forwarding the velocity disturbance is not effective in reducing the peaking that occurs as a result of this phenomenon. To improve the system performance, a combination of feedback and iterative learning control (ILC) is proposed and evaluated. The operating conditions require that ILC be applied in combination with a feedback controller, however the experimental system inherently suffers from limit cycle oscillations under feedback due to the presence of valve hysteresis. For this reason the ILC is applied in combination with a feedback controller designed to eliminate limit cycle oscillations based on describing function analysis. Experimental results demonstrate the efficacy of the solution where the feedback controller successfully eliminates limit cycle oscillations and the ILC greatly reduces the peaking of the control error with reductions in the RMS and peak-to-peak amplitude of the error by factors of more than 30 and 19, respectively. Stability of the proposed solution is demonstrated analytically in the frequency domain and verified on the experimental system for long periods of continuous operation.

Diagnostics of life support systems with limited statistical data on failures

“…The recurrence relations (8) take the form 1 Relations (19) are applied from the second step. The binary transformation(16) gives a physical meaning to the image components: a positive value ij s indicates the predominance in the sample by the i-th failure of such elements in which the values of the j-th diagnostic parameter do not exceed the range (15), and Vice versa at a negative value. This greatly simplifies the analysis of the images.However, binary transformation leads to some coarsening of statistical information.…”