Steering Angle Tracking Control Considering the Negative Effect of Unsprung Mass for the Kingpin Steering System Based on Interval Type-2 Fuzzy Logic Approach

The integration of steering, driving, and other subsystems at the wheel end is an emerging trend for improving vehicle chassis performance. The kingpin steering system, featuring a mechanically decoupled four-wheel design, is a preferred solution for in-wheel integration. However, this integration inevitably increases the vehicle's unsprung mass, which under random road excitations leads to dynamic fluctuations in self-aligning torque and system uncertainty, significantly reducing steering angle tracking accuracy. To overcome these challenges, this paper proposes a steering angle tracking control method based on interval type-2 fuzzy logic and variable-boundary tube model predictive control (IT2FL-VBTMPC). The IT2FL is employed to construct a three-dimensional membership function space that establishes a real-time mapping between the self-aligning torque and the disturbance boundaries of the kingpin steering system, thereby estimating the disturbance boundaries online. Based on this, a VB-TMPC is designed, which couples the nominal steering system model with the real-time disturbance boundary estimates. Within this framework, a baseline MPC controller generates the optimal reference trajectory for the front wheel steering angle, while a tube robust controller compensates for the system uncertainties introduced by the negative effects of unsprung mass, thus enhancing steering angle tracking accuracy. Experimental results demonstrate that the proposed IT2FL-VB-TMPC reduces the maximum tracking error and the average tracking error by 38.8% and 38.5%, thereby verifying the effectiveness of the proposed method.