Trajectory Tracking Control of Hydraulic Flexible Manipulators Based on Adaptive Robust Model Predictive Control

Aiming at the trajectory tracking control problem caused by the coupling of strong nonlinearity, parameter uncertainty and unknown disturbances in rigid robotic arms, this paper proposes an adaptive robust model predictive control (APRMPC) scheme. This study aims to fill the gap in the existing literature by proposing a dedicated control framework capable of simultaneously and effectively handling parameter uncertainty, unmodeled dynamics, and external disturbances, while ensuring constraint satisfaction. Firstly, a dynamic model of a three-degree-of-freedom robotic arm was established based on the Lagrange equation; secondly, this paper designs a deep integration mechanism of adaptive law and robust predictive control: by designing a parameter adaptive algorithm to estimate the system uncertainty online and feedforward compensate it to the predictive model, the impact of model mismatch is significantly reduced; meanwhile, for the estimated residuals and unknown disturbances, feedback gain was introduced and the control input was designed based on the robust invariant set theory, achieving unified parameter identification, disturbance suppression and rolling optimization within a single framework. This paper strictly proves the feasibility and stability of the control scheme. Finally, the simulation experiments based on MATLAB show that, compared with the traditional MPC and PID methods, the APRMPC algorithm can achieve higher accuracy and stronger robustness in trajectory tracking under various working conditions, effectively resolving the inherent contradiction between the weak robustness of the traditional MPC and the large buffering of sliding mode control, and verifying the value of the proposed scheme in filling the gap in related literature.