A Library Tailored for Real-Time Implementation of Model Predictive Control

Abstract

This paper presents a software library developed to implement model predictive control (MPC) strategies in real-time. By using MPC, an appropriate optimal control law is implicitly obtained. Furthermore, the system physical constraints on state and inputs are dealt with in a straightforward way. The library supports MPC for linear, nonlinear and linearized systems. For linear systems, the optimization problem is solved by using quadratic programming, while for the nonlinear case, sequential quadratic programming (SQP) is used. The linearized version enables the use of the faster quadratic programming algorithm for the nonlinear case. Experimental results show that the control signal computation can effectively be performed under the real-time requirements.