{"title":"Adaptive Nonhomogeneous Super-twisting Sliding Mode Control for Aircraft Braking System With Disturbance Compensation","authors":"Zhuangzhuang Wang;Xiaochao Liu;Pengyuan Qi;Shuai Wu","doi":"10.1109/TIE.2024.3488323","DOIUrl":null,"url":null,"abstract":"This article proposes a novel chattering-free adaptive nonhomogeneous supertwisting sliding mode control (ANSSMC)-based aircraft antiskid braking system (ABS) controller with disturbance compensation for an aircraft ABS subjected to complex disturbances. The time-varying disturbances and unmodeled dynamics are estimated by fixed-time extended state observer (FxTESO) and compensated via feedforward way. A new ANSSMC scheme is proposed to address the gain overestimation problem of nonhomogeneous supertwisting sliding mode control (NSSMC), which introduces an adaptive-gain protocol to minimize the control gain while sufficiently counteracting the residual disturbances after FxTESO compensation. Since FxTESO eliminates the major uncertainties in the aircraft ABS, the gain of ANSSMC is greatly reduced, further improving the tracking accuracy and robustness of the proposed controller. A Lyapunov stability analysis proves the finite time convergence of the proposed aircraft ABS controller when existing time-varying uncertainties. Compared with the conventional homogeneous SSMC (HSSMC), the root mean square value of the slip ratio tracking error of the proposed controller is reduced by 58%. Comparative experimental results demonstrate the effectiveness and superior performance of the proposed controller.","PeriodicalId":13402,"journal":{"name":"IEEE Transactions on Industrial Electronics","volume":"72 6","pages":"6155-6165"},"PeriodicalIF":7.2000,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Industrial Electronics","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10754885/","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
This article proposes a novel chattering-free adaptive nonhomogeneous supertwisting sliding mode control (ANSSMC)-based aircraft antiskid braking system (ABS) controller with disturbance compensation for an aircraft ABS subjected to complex disturbances. The time-varying disturbances and unmodeled dynamics are estimated by fixed-time extended state observer (FxTESO) and compensated via feedforward way. A new ANSSMC scheme is proposed to address the gain overestimation problem of nonhomogeneous supertwisting sliding mode control (NSSMC), which introduces an adaptive-gain protocol to minimize the control gain while sufficiently counteracting the residual disturbances after FxTESO compensation. Since FxTESO eliminates the major uncertainties in the aircraft ABS, the gain of ANSSMC is greatly reduced, further improving the tracking accuracy and robustness of the proposed controller. A Lyapunov stability analysis proves the finite time convergence of the proposed aircraft ABS controller when existing time-varying uncertainties. Compared with the conventional homogeneous SSMC (HSSMC), the root mean square value of the slip ratio tracking error of the proposed controller is reduced by 58%. Comparative experimental results demonstrate the effectiveness and superior performance of the proposed controller.
期刊介绍:
Journal Name: IEEE Transactions on Industrial Electronics
Publication Frequency: Monthly
Scope:
The scope of IEEE Transactions on Industrial Electronics encompasses the following areas:
Applications of electronics, controls, and communications in industrial and manufacturing systems and processes.
Power electronics and drive control techniques.
System control and signal processing.
Fault detection and diagnosis.
Power systems.
Instrumentation, measurement, and testing.
Modeling and simulation.
Motion control.
Robotics.
Sensors and actuators.
Implementation of neural networks, fuzzy logic, and artificial intelligence in industrial systems.
Factory automation.
Communication and computer networks.