dc.contributor.author Kong, Felix Honglim dc.date.accessioned 2019-09-11T01:00:18Z dc.date.available 2019-09-11T01:00:18Z dc.date.issued 2019-09-11 dc.identifier.uri http://hdl.handle.net/2123/21088 dc.description.abstract Iterative Learning Control is a control strategy to improve performance over repeated attempts at a certain task. By recording the signals generated from the previous attempt, a feedforward control signal can be generated to improve performance in the next attempt. The use of information from the previous iteration means that ILC is a feedback controller in the iteration domain. As with any feedback controller, there is the possibility of divergence, or instability, resulting in unbounded control signals. For nonlinear systems, convergence criteria for ILC systems are known for several specific cases, including the popular noncausal adjoint- and Newton-step update laws. In this thesis we develop a more general framework for certifying the convergence of nonlinear, noncausal ILC systems which includes, but is not limited to, the aforementioned specific cases. We do so using contraction theory, resulting in a convex convergence certificate, which is amenable to numerical computation. en_AU The other major topic in this thesis is the application of ILC to dynamic walking robots. Dynamic walking robots have the potential for versatile, efficient, and lifelike locomotion, but are often difficult to control, due to underactuation and undermodelling. ILC is known to be robust to undermodelling; however, ILC cannot be applied directly to dynamic walking robots, due to underactuation. We propose a variant of ILC suitable for dynamic walking robots that uses a phase variable as an index variable instead of using time. Phase-indexing'' ILC allows better control of dynamic walking robots, including learning to perform more prescribed motions more accurately, and in a more energy efficient way. Hardware experiments on a 2 degree of freedom compass-gait walking robot and simulation results on the compass-gait and 5-link dynamic walking robot verify the efficacy of the proposed method. dc.publisher University of Sydney en_AU dc.publisher Faculty of Engineering and IT en_AU dc.publisher School of Aerospace, Mechanical and Mechatronic Engineering en_AU dc.publisher The Australian Centre for Field Robotics en_AU dc.rights The author retains copyright of this thesis. It may only be used for the purposes of research and study. It must not be used for any other purposes and may not be transmitted or shared with others without prior permission. en_AU dc.subject Dynamic walking robots en_AU dc.title Phase-indexed Iterative Learning Control for Bipedal Dynamic Walking en_AU dc.type PhD Doctorate en_AU dc.type.pubtype Doctor of Philosophy Ph.D. en_AU dc.description.disclaimer Access is restricted to staff and students of the University of Sydney . UniKey credentials are required. Non university access may be obtained by visiting the University of Sydney Library. en_AU
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