Lie-algebra Learning for Mobile Robots Tracking Control with Model Uncertainty

This paper presents a novel Lie-algebra learning approach for differential wheeled robots (DWRs) trajectory tracking with uncertainty in the kinematic model. The approach is motivated by the fundamental property of group affine systems, which convert the state space from group space to vector space locally and derive a state-independent error kinematic model. Following the controllability analysis of the Lie-algebra optimal control problem, we design a suitable tracking scenario for the data collection and learning process. The analysis of the optimal Lie-algebra tracking control facilitates the development of the learning control algorithm to handle different trajectory tracking scenarios. Simulation experiments validate the efficiency of the proposed method and demonstrate the advantages of our control method over existing approaches.