Adaptive Control of Hydraulic Drive System: Real Time Steady-State Estimation Using Kalman Filter

Abstract

This thesis investigates how an adaptive controller can be developed for a hydraulic drive system. The currently used controller consists of a feed-forward component and a PI component. The feed-forward component consists of tables describing the steady-states of the vehicle, where the desired value is acquired through linear interpolation between the table points. The problem being that the system behaviour changes with time, leading the table values to become inaccurate and as a consequence the controller cannot control the system to satisfaction. The goal of this thesis is to solve this adaptivity problem and develop a controller that can handle the changes to the system over time. This is achieved by implementing a Kalman filter-based algorithm that updates the steady-state values of the feed-forward part of the controller to correct values, based on continuous measurements from the system. Relevant tables values are chosen as Kalman states which are updated based on measurements from the system and then reinserted into the tables. Two further development concepts are investigated. In the first the Kalman filter is updated with a batch of measurements and in the second a 3rd order polynomial is used to find interpolated values instead of linear interpolation. The conclusion is that the above mentioned algorithm results in an efficient system controller that maintains a comfortable driving experience. It is shown in this thesis that updated table values provide better control performance and that this contributes to the adaptivity of the controller.