Inverse kinematic optimization and MPC on a four-link hydraulic manipulator

Abstract

Operating individual joints on a hydraulic manipulator can be tedious and unproductive with limited experience. Therefore, this thesis presents a method to ease operator strain and lessen experience requirements by enabling cartesian control of the robot tool. This is achieved with differential closed-loop inverse kinematics and linear model predictive motion control. References are generated with a pseudo-inverse Jacobian algorithm formulated as a quadratic program with a secondary null-space objective to exploit redundant joints. A sensitivity analysis of a non-linear hydraulic model resulted in a linear first-order model which was used to design an MPC that tracks the references. Results from a simulated robot show robust tracking performance even when the robot is exposed to noise and unmodeled disturbances. The designed null-space objective manages to control null-space motion and the CLIK is shown to reject unmodeled disturbances.