Adaptive Sliding Mode Neural Control for Mecanum-Wheeled Omnidirectional Vehicle With Output Constraints

In this paper, a mecanum-wheeled omnidirectional vehicle (MWOV) with four wheels subject to external disturbances is investigated. Considering the specific characteristics of MWOV, its kinematic and dynamic models are established. A barrier function (BF) is introduced to address the position constraints of the mobile robot and is integrated with sliding mode control (SMC). The traditional sliding mode surface is improved with the aid of BF to achieve better robustness and dynamic response. Furthermore, utilizing the backstepping approach, an adaptive sliding mode neural control (SMNC) strategy is developed. A neural network is utilized to estimate unknown system parameters and disturbances. The stability of the MWOV system under the proposed controller is demonstrated through the Lyapunov direct method. Finally, numerical simulations are conducted to confirm the practicality and efficacy of the designed control scheme.