Application Study of Newton-Raphson-Type Nonlinear MPC for Hydrostatic Transmissions Under Disturbance and System Uncertainty

Abstract

Model predictive control is well established and has a huge practical relevance in many industrial applications, especially for chemical or thermal plants. This paper presents the design and the implementation of a nonlinear model predictive control aiming at an accurate tracking control of desired output trajectories under disturbances and uncertainties for a nonlinear hydrostatic transmission system with multiple control inputs, which represents a fast mechatronic system. The benefit of this solution is that it can be easily adapted to either velocity tracking control or torque tracking control -- which is not the case with alternative model-based approaches. The control design is based on a numerical optimization within a moving horizon using the Newton-Raphson method in combination with the optimization-over-some variables technique. The unmeasurable system state variables as well as the system disturbances are reconstructed by an unscented Kalman filter which is well suited for nonlineaer systems subject to process and measurement noise. The proposed control scheme is investigated by simulations and experimentally validated on a test rig at the Chair of Mechatronics, University of Rostock. The results indicates the robustness of the proposed control structure by a high tracking accuracy despite system disturbances and uncertainties.