Control and system identification of legged locomotion with recurrent neural networks

buir.advisorMorgül, Ömer
dc.contributor.authorÇatalbaş, Bahadır
dc.date.accessioned2022-06-14T08:31:58Z
dc.date.available2022-06-14T08:31:58Z
dc.date.copyright2022-06
dc.date.issued2022-06
dc.date.submitted2022-06-13
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.descriptionCataloged from PDF version of article.en_US
dc.descriptionThesis (Ph.D.): Bilkent University, Department of Electrical and Electronics Engineering, İhsan Doğramacı Bilkent University, 2022.en_US
dc.descriptionIncludes bibliographical references (leaves 105-116).en_US
dc.description.abstractIn recent years, robotic systems have gained massive popularity in the industry, military, and daily use for various purposes, thanks to advancements in artificial intelligence and control theory. As an exciting sub-branch of robotics with their differences and opportunities, legged robots have the potential to diversify and spread the use of robotic systems to new fields. Especially, legged locomotion is a desirable ability for mechanical systems where agile mobility and a wide range of motions are required to fulfill the designated task. On the other hand, unlike wheeled robots, legged robot platforms have a hybrid dynamical structure consisting of the flight and contact phases of the legs. Since the hybrid dynamical structure and nonlinear dynamics in the robot model make it challenging to apply control and perform system identification for them, various methods are proposed to solve these problems in the literature. This thesis focuses on developing new neural network-based techniques to apply control and system identification to legged locomotion so that robotic platforms can be designed to move efficiently as animal counterparts do in nature. In the first part of this thesis, we present our works on neural network-based controller development and evaluation studies for bipedal locomotion. In detail, neural controllers, in which long short-term memory (LSTM) type of neuron models are employed at recurrent layers, are utilized in the feedback and feedforward paths. Supervised learning data sets are produced using a biped robot platform controlled by a central pattern generator to train these neural networks. Then, the ability of the neural networks to perform stable gait by controlling the robot platform is assessed under various ground conditions in the simulation environment. After that, the stable walking generation capacity of the neural networks and the central pattern generators are compared with each other. It is shown that the proposed neural networks are more successful gait controllers than the central pattern generator, which is employed to generate data sets used in training. In the second part, we present our studies on the end-to-end usage of neural networks in system identification for bipedal locomotion. To this end, supervised learning data sets are produced using a biped robot model controlled by a central pattern generator. After that, neural networks are trained under series-parallel and parallel system identification schemes to approximate the input-output relations of the biped robot model. In detail, different neural models and neural network architectures are trained and tested in an end-to-end manner. Among neuron models, LeakyReLU and LSTM are found as the most suitable feedforward and recurrent neuron types for system identification, respectively. Moreover, neural network architecture consisting of recurrent and feedforward layers is found to be efficient in terms of learnable parameter numbers for system identification of the biped robot model. The last part discusses the results obtained in the control and system identification studies using neural networks. In the light of acquired results, neural networks with recurrent layers can apply control and systems identification in an end-to-end manner. Finally, the thesis is completed by discussing possible future research directions with the obtained results.en_US
dc.description.degreePh.D.en_US
dc.description.statementofresponsibilityby Bahadır Çatalbaşen_US
dc.format.extentxvi, 129 leaves : color charts ; 30 cm.en_US
dc.identifier.itemidB153369
dc.identifier.urihttp://hdl.handle.net/11693/90921
dc.language.isoEnglishen_US
dc.publisherBilkent Universityen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectRobot locomotion controlen_US
dc.subjectLegged locomotionen_US
dc.subjectBiped roboten_US
dc.subjectSystem identificationen_US
dc.subjectCentral pattern generatoren_US
dc.subjectMachine learningen_US
dc.subjectDeep learningen_US
dc.subjectRecurrent neural networksen_US
dc.subjectLong short-term memoryen_US
dc.titleControl and system identification of legged locomotion with recurrent neural networksen_US
dc.title.alternativeTekrarlayan sinir ağları ile bacaklı lokomosyonun kontrolü ve sistem tanımlanmasıen_US
dc.typeThesisen_US
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