Parameter Sensitivity Reduction of Nonlinear Model Predictive Control for Discrete-time Systems

Receding horizon methods have become very popular in the last decades for the approximate solution of optimal control problems, model predictive control (MPC) being a popular choice. MPC is to a large extent a model-based feedforward technique and as such its performance is rather sensitive to the model quality. This has prompted much interest in the search of methods to make MPC more robust against deviations such as uncertain parameters. This article presents a way to incorporate additional sensitivity terms into the optimization problem in order to reduce the cost function's sensitivity to the model parameters. For tackling the problem Continuation/GMRES (C/GMRES), a method to solve receding horizon nonlinear optimal control problems in an efficient way, was chosen, however, the general formulation is not restricted to this particular method. The potential performance of the approach is shown by means of simulation examples.