Nonlinear Model Predictive Control for a Self-balancing Wheelchair

• Published in: IEEE access Vol. 12; p. 1
• Main Authors: Liu, Chen, Qin, Kairong, Xin, Guiyang, Li, Chengbao, Wang, Sibo
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01-01-2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Balancing; Equations of state; Kinematics; Motion planning; Motion stability; Nonlinear control; Nonlinear control systems; Nonlinear model predictive control; nonlinear optimization; Nonlinear programming; Optimal control; Pitch (inclination); Planning; Predictive control; Robots; self-balance control; Torque; Trajectory optimization; Vectors; Wheelchairs
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3368853

The self-balancing wheelchair is characterized by its small size, fiexibility, 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.