Measured-state driven warm-start strategy for linear MPC

Model Predictive Control (MPC) is an optimization-based control technique which involves solving an optimization problem in every sampling instant. As a consequence, MPC is very computationally demanding, compared to traditional control techniques. Thus, much effort in academia and also in industry is aimed at finding a way to decrease the computation time. One approach is to find such a starting point for the iterative solver that it finds the optimum in less iterations - this technique is called warm-start or hot-start. This paper brings attention back to the common warm-start technique as well as an alternative - realized via the Linear Quadratic Regulator (LQR) static feedback. The paper proposes a natural combination of both of these warm-start techniques to achieve improved results.