Bioinspired Intelligent Control of Autonomous Robots with State Estimation

dc.contributor.advisorYang, Simon X.
dc.contributor.advisorGadsden, S. Andrew
dc.contributor.authorXu, Zhe
dc.date.accessioned2023-05-16T19:48:34Z
dc.date.available2023-05-16T19:48:34Z
dc.date.copyright2023-05
dc.date.created2023-04-14
dc.degree.departmentSchool of Engineeringen_US
dc.degree.grantorUniversity of Guelphen
dc.degree.nameDoctor of Philosophyen_US
dc.degree.programmeEngineeringen_US
dc.description.abstractAutonomous robots have been widely used to perform various tasks in many fields. Developing efficient and robust control methods for autonomous robots operating in diverse environments with hardware limitations is critical. In addition, accurate state estimation for autonomous robots operating under noises is another essential aspect, as this accuracy significantly influences control performance. This thesis aims to develop multiple practical control strategies with state estimation to tackle the challenges in trajectory tracking control of mobile robots, unmanned underwater vehicles, and unmanned aerial vehicles. First, bioinspired intelligent controllers are proposed based on backstepping and sliding mode control techniques, integrated with a neural dynamics to resolve multiple challenging issues, including speed jump, robot velocity constraints, robustness against disturbances, and control smoothness under noises. Then, the adaptive sliding innovation filter is integrated with the proposed robot control algorithms to provide accurate state estimates for robots to operate under noises and offer extra robustness against modeling errors during estimation processes. Furthermore, the formation control of multiple mobile robots is developed, where distributed estimators are designed to estimate the leader robot's state information, then a distributed leader follower formation control strategy is correspondingly proposed. Finally, the stability of the resulting control systems is proved using the Lyapunov stability theory, and extensive comparison results have demonstrated the efficiency and effectiveness of the proposed tracking control and formation control methods.en_US
dc.identifier.citationXu Z., T. Yan, S. X. Yang and S. A. Gadsden, �??Bio-inspired intelligence with applications to robotics: a survey,�?� IET Cyber-Systems and Robotics, vol. 4, no. 3, pp. 153-162. 2022. DOI:10.1049/csy2.12060
dc.identifier.citationXu Z., T. Yan, S. X. Yang and S. A. Gadsden, �??Distributed Leader Follower Formation Control of Mobile Robots based on Bioinspired Neural Dynamics and Adaptive Sliding Innovation Filter,�?� IEEE Transactions on Industrial Informatics, 2023. DOI: 10.1109/TII.2023.3272666
dc.identifier.urihttps://hdl.handle.net/10214/27600
dc.language.isoenen_US
dc.publisherUniversity of Guelphen
dc.rights.licenseAll items in the Atrium are protected by copyright with all rights reserved unless otherwise indicated.
dc.subjectBioinspired neural dynamicsen_US
dc.subjectTracking controlen_US
dc.subjectFormation controlen_US
dc.subjectState estimationen_US
dc.subjectRobot controlen_US
dc.titleBioinspired Intelligent Control of Autonomous Robots with State Estimationen_US
dc.typeThesisen

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