Synthesis of Feedback Controller for Nonlinear Control Systems with Optimal Region of Attraction

2019/11/10 by Ayan Chakraborty, Chakraborty, Ayan, Indranil Saha +1
Engineering · #Adaptive Control of Nonlinear Systems #Aerospace Engineering and Control Systems #Extremum Seeking Control Systems #FOS: Computer and information sciences #FOS: Electrical engineering #FOS: Mathematics #Optimization and Control (math.OC) #Robotics (cs.RO) #Systems and Control (eess.SY) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.1911.03870

openalex publication_date 2019/11/10 · openalex created_date 2019/11/22 · openalex updated_date 2026/07/28

Abstract

We propose a framework for synthesizing a feedback control policy that maximizes the region of attraction (ROA) of a closed-loop nonlinear dynamical system. Our synthesis technique relies on stochastic optimization, which involves computation of an objective function capturing the ROA for a feedback control law. We employ a machine learning technique based on deep neural network to estimate the ROA for a given feedback controller. Overall, our technique is capable of synthesizing a controller co-optimizing traditional control objectives like LQR cost together with ROA. We demonstrate the efficacy of our technique through exhaustive experiments carried out on various nonlinear systems.

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