Cooperative optimisation control of trajectory tracking for wheeled mobile robots integrating nonlinear disturbance observer and event-triggered mechanism

Wheeled mobile robots (WMR) face challenges in complex dynamic environments. Addressing the performance bottlenecks of traditional model predictive control (MPC) caused by model mismatch and high computational complexity, this study proposes a collaborative optimisation framework that integrates nonlinear disturbance observation and event-triggered mechanisms. First, a hierarchical nonlinear disturbance observer (NDO) is designed to estimate matched and unmatched disturbances through dual channels in the dynamic and kinematic layers, respectively. Second, the disturbance estimates are embedded as feedforward compensation terms in the MPC rolling optimisation to establish an MPC-NDO collaborative control framework, thereby enhancing disturbance suppression capabilities. An event-triggered mechanism is introduced to reduce the trigger rate, thereby alleviating unnecessary computational burdens. Finally, experimental results show that, through the collaborative optimisation of the hierarchical nonlinear disturbance observer and the event-triggered mechanism, the framework in this paper effectively resolves the contradiction among control accuracy, robustness, and computational efficiency.