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Lateral Control for UGVs by ADRC With Modeling Compensation and Expanded Disturbance Observation | IEEE Journals & Magazine | IEEE Xplore

Lateral Control for UGVs by ADRC With Modeling Compensation and Expanded Disturbance Observation


Abstract:

By improving observer estimation accuracy, an active disturbance rejection control (ADRC) strategy is proposed for an unmanned ground vehicle (UGV), where dynamic perform...Show More

Abstract:

By improving observer estimation accuracy, an active disturbance rejection control (ADRC) strategy is proposed for an unmanned ground vehicle (UGV), where dynamic performance is enhanced by disturbance suppression during trajectory tracking. In the strategy, part information of the lateral control model is added to the state matrix, so as to compensate estimation bias of UGV states from an extended state observer (ESO). Then, a free variable for estimating higher-order disturbance states is integrated into the highest-order variable of the ESO, and ESO accuracy is thus improved for total disturbance estimation. To achieve accurate suppression of complex disturbance, estimates of UGV total disturbance are added to the control law according to feedback linearization theories. System stability is proved by theoretical analysis, and estimation accuracy for system states and total disturbance are enhanced by above improvement. Finally, comparative tests are conducted under multiple cases to verify the performance of the proposed strategy.
Published in: IEEE Transactions on Industrial Electronics ( Early Access )
Page(s): 1 - 13
Date of Publication: 12 March 2025

ISSN Information:

School of Control Science and Engineering, and Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems, Tiangong University, Tianjin, China
School of Control Science and Engineering, and Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems, Tiangong University, Tianjin, China
School of Control Science and Engineering, and Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems, Tiangong University, Tianjin, China
School of Control Science and Engineering, and Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems, Tiangong University, Tianjin, China
Institute of Robotics and Automatic Information Systems, and Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin, China

School of Control Science and Engineering, and Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems, Tiangong University, Tianjin, China
School of Control Science and Engineering, and Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems, Tiangong University, Tianjin, China
School of Control Science and Engineering, and Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems, Tiangong University, Tianjin, China
School of Control Science and Engineering, and Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems, Tiangong University, Tianjin, China
Institute of Robotics and Automatic Information Systems, and Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin, China
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