On Stabilizing Control of Gaussian Processes for Unknown Nonlinear Systems. Issue 1 (July 2017)

• Record Type: Journal Article
• Authors: Ito, Yuji; Fujimoto, Kenji; Tadokoro, Yukihiro; Yoshimura, Takayoshi
• Is Part Of: IFAC-PapersOnLine. Volume 50:Issue 1(2017)
• Page Start: 15385
• Page End: 15390
• Publication Date: 2017-07
• Subjects: Bayesian methods -- Nonparametric methods -- Asymptotic stabilization -- Stability of nonlinear systems -- Robust control -- Uncertainty descriptions; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2017.08.1861
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: This paper proposes a novel controller design to stabilize Gaussian process (GP) dynamical models for partially unknown nonlinear systems. The unknown system is identified as the GP model. Because existing GP models are difficult to analyze in terms of controller design, a novel GP identification method is proposed that obtains the state dependent coefficient matrix of the system, making the controller design more efficient. The GP model is represented as a piecewise linear system with a bounded uncertainty that is derived based on the characteristics of the GP. Extending quadratic stability theory derives a novel stabilizing controller for such systems.