Adaptive neural network preset time fault tolerant control of omnidirectional mobile robot with input saturation based on state constraints

• Published in: Nonlinear dynamics Vol. 113; no. 17; pp. 23285 - 23302
• Main Authors: Liang, Jiajian, Liang, Zhongwei, Su, Zhiwen, Liang, Jiaqiao, Zeng, Xinan, Deng, Li, Zhang, Weipeng, Zou, Tao, Yuan, Yang
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Nature B.V 01.09.2025
• Subjects: Actuators; Complexity; Constraints; Control methods; Control theory; Controllers; Design; Disturbance observers; Fault tolerance; Kinematics; Neural networks; Obstacle avoidance; Robot control; Robot dynamics; Robots; Tracking; Velocity; Wheels
• ISSN: 0924-090X; 1573-269X
• DOI: 10.1007/s11071-025-11355-1

This paper addresses the trajectory tracking and obstacle avoidance control problem of a Mecanum-wheeled mobile robot (MMR) subject to state constraints, input saturation, actuator faults and unknown disturbances. To this end, a prescribed-time adaptive neural network (NN) fault-tolerant control scheme incorporating a disturbance observer and input saturation estimator (ISE) is proposed. A novel potential function is first constructed. based on which a virtual control law is developed by integrating the potential function and the disturbance observer. This law enables effective handling of state constraints, suppression of unknown disturbances, and safe obstacle avoidance. Subsequently, an adaptive NN-based fault-tolerant control method is formulated to guarantee prescribed-time convergence of the MMR. The design explicitly considers time-varying factors, unknown gain losses, and bias faults in the actuators. A bounded estimation approach is employed to address unknown disturbances and fault effects, significantly reducing the controller’s complexity. Furthermore, to mitigate the derivative explosion induced by high-order error terms in the virtual control and to respect input saturation limits, an input saturation estimator (ISE) is designed. Simulation results demonstrate that the proposed method significantly enhances trajectory tracking precision and obstacle avoidance performance of the MMR in complex environments.