Stabilization Control of the Differential Mobile Robot Using Lyapunov Function and Extended Kalman Filter
This work addresses stabilization control for differential mobile robots, which is an incremental improvement in robotics navigation.
The paper tackles the problem of navigating a differential mobile robot to a desired destination from any initial pose by designing a control model with state estimation using an extended Kalman filter and stabilization control via Lyapunov functions, achieving asymptotic stability and robustness as validated through simulations and experiments.
This paper presents the design of a control model to navigate the differential mobile robot to reach the desired destination from an arbitrary initial pose. The designed model is divided into two stages: the state estimation and the stabilization control. In the state estimation, an extended Kalman filter is employed to optimally combine the information from the system dynamics and measurements. Two Lyapunov functions are constructed that allow a hybrid feedback control law to execute the robot movements. The asymptotical stability and robustness of the closed loop system are assured. Simulations and experiments are carried out to validate the effectiveness and applicability of the proposed approach.