Self-tuning controllers for nonlinear systems
Automatica (Journal of IFAC)
Dual pole-placement controller with direct adaptation
Automatica (Journal of IFAC)
Prediction with Gaussian processes: from linear regression to linear prediction and beyond
Learning in graphical models
Evaluation of gaussian processes and other methods for non-linear regression
Evaluation of gaussian processes and other methods for non-linear regression
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Brief paper: Dual adaptive control of nonlinear stochastic systems using neural networks
Automatica (Journal of IFAC)
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Gaussian Process prior models, as used in Bayesian non-parametric statistical models methodology are applied to implement a nonlinear adaptive control law. The expected value of a quadratic cost function is minimised, without ignoring the variance of the model predictions. This leads to implicit regularisation of the control signal (caution) in areas of high uncertainty. As a consequence, the controller has dual features, since it both tracks a reference signal and learns a model of the system from observed responses. The general method and its unique features are illustrated on simulation examples.