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• W1515326613 abstract "This work proposes a new method for simultaneous probabilistic identificationand control of an observable, fully-actuated mechanical system. Identificationis achieved by conditioning stochastic process priors on observations ofconfigurations and noisy estimates of configuration derivatives. In contrast toprevious work that has used stochastic processes for identification, weleverage the structural knowledge afforded by Lagrangian mechanics and learnthe drift and control input matrix functions of the control-affine systemseparately. We utilise feedback-linearisation to reduce, in expectation, theuncertain nonlinear control problem to one that is easy to regulate in adesired manner. Thereby, our method combines the flexibility of nonparametricBayesian learning with epistemological guarantees on the expected closed-looptrajectory. We illustrate our method in the context of torque-actuated pendulawhere the dynamics are learned with a combination of normal and log-normalprocesses." @default.
• W1515326613 created "2016-06-24" @default.
• W1515326613 creator A5039607149 @default.
• W1515326613 creator A5053811056 @default.
• W1515326613 creator A5058617210 @default.
• W1515326613 date "2013-11-18" @default.
• W1515326613 modified "2023-09-27" @default.
• W1515326613 title "Stochastic processes and feedback-linearisation for online identification and Bayesian adaptive control of fully-actuated mechanical systems" @default.
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• W1515326613 hasPublicationYear "2013" @default.
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