Generalized Extended State Observer Approach to Robust Tracking Control for Wheeled Mobile Robot with Skidding and Slipping

Kang, Hyo-Seok; Kim, Yong‐Tae; Hyun, Chang‐Ho; Park, Mignon

doi:10.5772/55738

Cited by 59 publications

(41 citation statements)

References 33 publications

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“…Shojaei et al [70] designed a control algorithm, robust under parametric and nonparametric uncertainty, which combines an inverse dynamics control and an adaptive robust PID control. Finally, other contributions that employ the dynamic model only of the WMR mechanical structure in the control design can be found in [71][72][73][74][75][76][77][78][79][80].…”

Section: Dynamic Modelmentioning

confidence: 99%

Tracking Control for Mobile Robots Considering the Dynamics of All Their Subsystems: Experimental Implementation

et al. 2017

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The trajectory tracking task in a wheeled mobile robot (WMR) is solved by proposing a three-level hierarchical controller that considers the mathematical model of the mechanical structure (differential drive WMR), actuators (DC motors), and power stage (DC/DC Buck power converters). The highest hierarchical level is a kinematic control for the mechanical structure; the medium level includes two controllers based on differential flatness for the actuators; and the lowest hierarchical level consists of two average controllers also based on differential flatness for the power stage. In order to experimentally validate the feasibility of the proposed control scheme, the hierarchical controller is implemented via a Σ-Δ-modulator in a differential drive WMR prototype that we have built. Such an implementation is achieved by using MATLAB-Simulink and the real-time interface ControlDesk together with a DS1104 board. The experimental results show the effectiveness and robustness of the proposed control scheme.

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Section: Dynamic Modelmentioning

confidence: 99%

Tracking Control for Mobile Robots Considering the Dynamics of All Their Subsystems: Experimental Implementation

et al. 2017

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“…The WMR was regarded as nonlinear discrete-time dynamic system in the motion control when the skidding and slipping phenomena existed [23]. For another robust tracking controller in presence of skidding and slipping, a desired virtual velocity controller based on disturbance observer (DOB) was developed in [24], a generalized extended state observer [25] and fuzzy disturbance observer [26] were developed. In the controller, the skidding and slipping was regarded as disturbance and thus the dynamics equation was modified.…”

Section: Introductionmentioning

confidence: 99%

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

Wang

Zhou

et al. 2019

Energies

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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.

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“…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. Later in their work, Kang et al [25] proposes a robust tracking controller based on the fuzzy disturbance observer for a WMR with unknown skidding and slipping.…”

Section: Introductionmentioning

confidence: 99%

Neural Network-Based Adaptive Motion Control for a Mobile Robot with Unknown Longitudinal Slipping

Wang

Liu

Zhao

et al. 2019

Chin. J. Mech. Eng.

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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. which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

show abstract

Generalized Extended State Observer Approach to Robust Tracking Control for Wheeled Mobile Robot with Skidding and Slipping

Cited by 59 publications

References 33 publications

Tracking Control for Mobile Robots Considering the Dynamics of All Their Subsystems: Experimental Implementation

Tracking Control for Mobile Robots Considering the Dynamics of All Their Subsystems: Experimental Implementation

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

Neural Network-Based Adaptive Motion Control for a Mobile Robot with Unknown Longitudinal Slipping

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