Adaptive Sliding Mode Trajectory Tracking Control for WMR Considering Skidding and Slipping via Extended State Observer

Abstract

When the wheeled mobile robot (WMR) is required to perform specific tasks in complex environment, i.e., on the forestry, wet, icy ground or on the sharp corner, wheel skidding and slipping inevitably occur during trajectory tracking. To improve the trajectory tracking performance of WMR under unknown skidding and slipping condition, an adaptive sliding mode controller (ASMC) design approach based on the extended state observer (ESO) is presented. The skidding and slipping is regarded as external disturbance. In this paper, the ESO is introduced to estimate the lumped disturbance containing the unknown skidding and slipping, parameter variation, parameter uncertainties, etc. By designing a sliding surface based on the disturbance estimation, an adaptive sliding mode tracking control strategy is developed to attenuate the lumped disturbance. Simulation results show that higher precision tracking and better disturbance rejection of ESO-ASMC is realized for linear and circular trajectory than the ASMC scheme. Besides, experimental results indicate the ESO-ASMC scheme is feasible and effective. Therefore, ESO-ASMC scheme can enhance the energy efficiency for the differentially driven WMR under unknown skidding and slipping condition.

Energy Saving Control Approach for Trajectory Tracking of Autonomous Mobile Robots

“…It is difficult to determine the adaptive H 2 closed-form solution and solve the nonlinear time-varying differential equations in Eqs. ( 5) and (14). Therefore, this result is a great achievement for the trajectory tracking of AMRs because the adaptive H 2 closed-form solution can be directly derived from Eq.…”

“…For trajectory tracking control design, AMRs must be capable of converging the tracking errors of the real trajectory and the desired trajectory as close to zero as possible while considering the influence of modeling uncertainties. A survey of the literature revealed that many studies have focused on the trajectory tracking control of AMRs, for example, trajectory tracking control through backstepping [7][8][9][10], sliding mode control [11][12][13][14], feedback linearization [15][16][17], neural networks [18][19][20][21][22], fuzzy control [23][24][25][26][27][28], and the H 2 [29,30] approach. In practice, it is challenging to implement microchip operation and torque output with low energy consumption by using the aforementioned control algorithm methodologies or extremely complex theoretical structures.…”

Optimal Design and Performance Comparison of a Combined ANFIS-PID with Back Stepping Technique, Using Various Meta-Heuristic Algorithms to Solve Wheeled Mobile Robot Trajectory Tracking Problem

“…However, the underestimating of the complete dynamics knowledge produce insufficient control technique, a torque controller is essential in many applications, like highspeed motions and large transportation [11]. For that reason, a sliding mode controller is introduced in references [12][13][14], an online real parameters identification of the WMR dynamics is suggested by Martins et al [15], an adaptive back-stepping controller in reference [16], using an adaptive neural network controller for treating unmodeled dynamics in references [17,18], fuzzy logic controller has been applied in reference [19,20].…”

Neural Network Based Adaptive Dynamic Surface Control for Omnidirectional Mobile Robots Tracking Control with Full-state Constraints and Input Saturation

“…Lu et al [7] proposed a neural network (NN) adaptive sliding mode control method, and NN is utilized to estimate the various uncertainties and disturbances dynamically. Wang et al [24] proposed an adaptive sliding mode controller for wheeled mobile robots, where the lumped disturbances containing the unknown skidding and slipping, parameter variation, parameter uncertainties are regarded as external disturbance and estimated by the extended state observer. However, the high frequency vibration problem in sliding mode is not conducive to robots.…”

“…1) Different from the trajectory tracking controllers developed for OMRs in [2][3][4]6,7,24], where the constraints of position and velocity state variables in the movement process are not considered, the proposed BLF based adaptive approach can ensure the robot moves within the desired safety area and velocity range, which is important and useful in practical application.…”