Nonlinear Model Predictive Control for a Self-Balancing Wheelchair

Abstract

The self-balancing wheelchair is characterized by its small size, flexibility, and strong ground adaptability. To expand the control range of the pitch angle and enhance the stability of the self-balancing wheelchair, this paper presents a nonlinear model predictive control (NMPC) method for challenges in online motion planning and control. We apply the NMPC formulation with the nonlinear state equation as a constraint that captures the accurate kinematics and dynamics of the wheelchair. We utilize direct transcription to discretize the formulation and convert it into a nonlinear programming problem. We establish a control loop that includes a PD controller to apply NMPC to a wheelchair. We conduct simulations under different state commands and external forces and demonstrate that the proposed method can achieve fast convergence speeds, strong robustness, and high stability motions under extreme pitch angles.

Keywords

• Nonlinear model predictive control
• nonlinear optimization
• self-balance control
• trajectory optimization