Abstract
When the mobile robot performs certain motion tasks in complex environment, wheel slipping inevitably occurs due to the wet or icy road and other reasons, thus directly influences the motion control accuracy. To address unknown wheel longitudinal slipping problem for mobile robot, a RBF neural network approach based on whole model approximation is presented. The real-time data acquisition of inertial measure unit (IMU), encoders and other sensors is employed to get the mobile robot’s position and orientation in the movement, which is applied to compensate the unknown bounds of the longitudinal slipping using the adaptive technique. Both the simulation and experimental results prove that the control scheme possesses good practical performance and realize the motion control with unknown longitudinal slipping.
Highlights
With the development of robotic applications, higher requirements have been presented for the motion control problem of mobile robots
Many intelligent control technologies, such as kinematic∕torque control method using backstepping [4], a modified input–output linearization method [5], a data-based tracking control [6, 7], a sliding-mode based tracking algorithms [8,9,10], neural networks tracking control method [11, 12], extended state observer based nonlinear tracking and obstacle avoidance control method [13], disturbance observer-based robust trajectory tracking method [14], based on this assumption have been proposed for the robotics research
Based on the dynamic model, a neural network approach based on whole model approximation for the motion control of mobile robots under unknown longitudinal slipping occurrence is proposed
Summary
With the development of robotic applications, higher requirements have been presented for the motion control problem of mobile robots. For the wheeled mobile robot under unknown skidding and slipping conditions, one previous study [23] presented an adaptive tracking control scheme with torque saturation. Regarding skidding and slipping as disturbances, Kang et al [24] proposed a robust tracking approach using a generalized extended state observer at kinematic and dynamic level. When existing the skidding, slipping and input disturbance, Chen [28] proposed a robust tracking control scheme based on the disturbance observer for wheeled mobile robots. Based on the dynamic model, a neural network approach based on whole model approximation for the motion control of mobile robots under unknown longitudinal slipping occurrence is proposed. We observe that when longitudinal slipping impact are considered in the dynamic model, it becomes more complicated
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