Functional Dual Adaptive Control with Recursive Gaussian Process Model

12th European Workshop on Advanced Control and Diagnosis

Prüher, Jakub^1 ; Král, Ladislav^1

University of West Bohemia, Pilsen, Czech Republic^1

关键词: Computational demands;    Computational requirements;    Dual adaptive controls;    Functional uncertainty;    Gaussian process models;    Gaussian process regression;    Gaussian process regression model;    Stochastic dynamic systems;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/659/1/012006/pdf
DOI  :  10.1088/1742-6596/659/1/012006

来源: IOP

【 摘 要 】

The paper deals with dual adaptive control problem, where the functional uncertainties in the system description are modelled by a non-parametric Gaussian process regression model. Current approaches to adaptive control based on Gaussian process models are severely limited in their practical applicability, because the model is re-adjusted using all the currently available data, which keeps growing with every time step. We propose the use of recursive Gaussian process regression algorithm for significant reduction in computational requirements, thus bringing the Gaussian process-based adaptive controllers closer to their practical applicability. In this work, we design a bi-criterial dual controller based on recursive Gaussian process model for discrete-time stochastic dynamic systems given in an affine-in-control form. Using Monte Carlo simulations, we show that the proposed controller achieves comparable performance with the full Gaussian process-based controller in terms of control quality while keeping the computational demands bounded.