Towards Efficient Learning-Based Model Predictive Control via Feedback Linearization and Gaussian Process Regression
Jack Caldwell, Joshua A. Marshall
Abstract
This paper presents a learning-based Model Predictive Control (MPC) methodology incorporating nonlinear predictions with robotics applications in mind. In particular, MPC is combined with feedback linearization for computational efficiency and Gaussian Process Regression (GPR) is used to model unknown system dynamics and nonlinearities. In this method, MPC predicts future states by leveraging a GPR model and optimizes a sequence of inputs over feedback linearized states. The controller was tested in simulation by using a two-link planar robot in the presence of model uncertainty. With respect to trajectory-tracking error, the proposed controller outperformed a conventional Proportional-Derivative Inverse Dynamics controller and a GPR-augmented version. Although a fully nonlinear MPC formulation achieved slightly better performance, the proposed controller had an average control calculation time that was 82× faster.