Soft-Constrained MPC Optimized by DBO: Anti-Disturbance Performance Study of Wheeled Bipedal Robots

In disturbance scenarios, wheeled bipedal robots (WBRs) require effective control algorithms to restore balance. To address the trade-off between computational burden and control precision, and to enhance anti-disturbance capability, this paper proposes a soft-constrained Model Predictive Control (MPC) algorithm with optimized horizon parameters tailored to the hardware of the WBR. A cost function is designed, and the Dung Beetle Optimizer (DBO) is employed to optimize the MPC’s prediction and control horizons. An experimental platform is built, and impact and load disturbance experiments are conducted. The experimental results show that, under impact disturbances, the pitch angle and displacement overshoot with optimized MPC are reduced by 58.57% and 42.20%, respectively, compared to unoptimized LQR. Under load disturbances, the pitch angle and displacement overshoot are reduced by 17.09% and 15.53%, respectively, with both disturbances converging to the equilibrium position.