A Hybrid Approach for Trajectory Control Design

Abstract-- This work presents a methodology to design trajectory tracking feedback control laws, which embed nonparametric statistical models, such as Gaussian Processes (GPs). The aim is to minimize unmodeled dynamics such as undesired slippages. The proposed approach has the benefit of avoiding complex terramechanics analysis to directly estimate from data the robot dynamics on a wide class of trajectories. Experiments in both real and simulated environments prove that the proposed methodology is promising. In the last decades, an increasing interest has been devoted to the design of high performance path tracking. In the literature, three main approaches to face this problem have emerged: (i) model-based and adaptive control [1]-[5]; (ii) Gaussian Processes or stochastic nonlinear models for reinforcement learning of control policies [6], [7], and (iii) nominal models and data-driven estimation of the residual [8], [9].