Fuzzy logic-assisted robust sliding mode control for precise trajectory tracing of tracked mobile robots

Precise trajectory tracking is essential for achieving autonomous locomotion of a tracked robot, necessitating an advanced controller to manage system nonlinearity, terrain uncertainty, and track slippage. To address these challenges, we proposed an improved dynamic model-based fuzzy sliding mode control (FSMC) approach. In this approach, initially, a controlled input velocity vector was created to achieve an asymptotically stable tracking error. Then, a sliding mode control with a fuzzy logic-based switching law was developed to ensure the robot's real velocity converged to the controlled velocity input. The more robust and chatter-free responses were observed due to the integration of the fuzzy switching law and control gain optimisation using a Genetic Algorithm. Comparative analysis shows that FSMC outperforms proportional-integral-derivative (PID) control, traditional sliding mode control (SMC), and non-singular integral-based SMC (NISMC). Finally, the proposed control system was experimentally validated through real-time experiments with a tracked robot under various terrain conditions.