Dynamic modeling and nonlinear model predictive control of hybrid actuator systems

Abstract

Hybrid actuators can provide better performances than hydraulic or electromechanical actuators alone; however, control of these hybrid systems is more challenging. This paper presents the application of model predictive control (MPC) on a hybrid actuator system to coordinate multiple control objectives. MPC has advantages over traditional controllers such as a PID in controlling non-linear multi-input, multi-output systems. MPC proactively updates the control inputs in a specific time interval by observing the current state of the system, predicting the future behavior, and solving an optimization problem repeatedly with consideration of pre-defined control objectives. In this paper, a high-fidelity dynamic model of the hybrid actuator system will be developed and a simplified, reduced-order model will be derived for use in the MPC formulation process. The detailed process of designing an MPC controller, which may consist of mathematical formulation of an optimization problem and numerical methods to find the optimal solution, will be discussed in detail. Simulation results will be presented to compare the performances of the MPC with PID controllers, and the effects of different controller parameters on the system performances will be discussed.