Nonlinear Model Predictive Control with Moving Horizon Estimation of a Pendubot system

In this paper we present and investigate a complex control framework based on Nonlinear Model Predictive Control (NMPC) to achieve the unstable equilibria, and Moving Horizon Estimation (MHE) to estimate the actual state and parameters of a Pendubot. This fast, under-actuated nonlinear mechatronic system apparently poses a challenging benchmark problem that might benefit from a nonlinear optimization scheme. To overcome the related computational difficulties we make use of the ACADO Code Generation tool allowing to export a highly efficient Gauss-Newton real-time iteration algorithm tailored to the nonlinear model dynamics while respecting imposed constraints. We show simulation results illustrating the overall control performance of the closed-loop system as well as the advantages of the nonlinear MHE-based NMPC scheme.