{'fraction_non_alphanumeric': 0.016304347826086956, 'fraction_numerical': 0.0, 'mean_word_length': 5.625899280575539, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
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.