Adaptive Fixed-Time Tracking Control of Cart–Pendulum Robotic Systems with Bias Actuator Dynamics

Abstract

This research addresses the challenge of precise trajectory tracking for cart–pendulum robotic systems affected by unknown nonlinear actuator dynamics. We introduce a novel control framework that combines neural network modeling with adaptive parameter estimation to handle these complex dynamics. By characterizing state-dependent actuator behavior through custom-designed linear filters and adaptive laws, our approach identifies system parameters with high precision. We then develop an innovative fixed-time adaptive sliding mode controller that guarantees convergence within a predetermined timeframe regardless of initial conditions. Lyapunov stability analysis confirms that tracking errors converge to a small neighborhood around zero within the specified time bounds, with the size of the neighborhood determined by the design parameters. Simulation studies on a watermelon transportation robot validate our approach’s practical effectiveness, demonstrating improved tracking accuracy and robustness against actuator disturbances compared with conventional methods.