Constrained reference learning for continuous-time model predictive tracking control of autonomous systems

摘要

Often, systems need to adapt their behavior to other systems in their surroundings while obeying constraints to achieve good performance or due to safety reasons. We consider repetitive applications, where the reference for the controller stems from noisy sensor data. Including preview information of the reference, e.g. extrapolating from previous cycles or similar instances, can significantly improve the overall tracking performance and ensure constraint satisfaction. We propose a learning-supported predictive controller that uses Gaussian processes as reference generators for its control task. A Gaussian process is used to learn, filter, and predict the references. It learns references tailored to model predictive control, taking into account continuous-time system dynamics and constraints via constrained hyperparameter optimization. We illustrate the benefits concerning approximation and control performance of the informed Gaussian-process training by a cooperative, mobile robot example.