Multi-constraint model predictive control of flexible joint drive system based on unknown state estimator and constraint adaptive hierarchical planning

To address the issues of low control precision and potentially unsolvable control laws for flexible joint under uncertain disturbance and multiple constraints, a model predictive control (MPC) method based on an unknown state estimator (USE) and constraint adaptive hierarchical planning (CAHP) is proposed. The USE designed based on the low-pass filter can estimate the lumped disturbance without relying on the acceleration signal feedback and the accurate dynamic model, and is compatible with discretized predictive models. Meanwhile, a constraint adaptive hierarchical planning (CAHP) method is designed based on propositional logic. Adaptive weighting coefficients dynamically adjust the priority of constraints like obstacle avoidance and saturation. This enhances the solvability of the control problem while maximizing constraint satisfaction. Simulation results show that the proposed method enables the flexible joint system to achieve high-accuracy trajectory tracking and effective obstacle avoidance under uncertain disturbances, while satisfying all constraint conditions. Compared with the traditional fixed priority MPC method, the USE-CAHP-MPC method improves the control accuracy and robustness of the flexible joint drive system.