2021/09/13 by Soumyabrata Talukder, Talukder, Soumyabrata, Ratnesh Kumar +1 · 2 citations
Computer Science · Engineering · #Control Systems and Identification #FOS: Computer and information sciences #FOS: Electrical engineering #Fault Detection and Control Systems #Machine Learning (cs.LG) #Neural Networks and Applications #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2109.05710
openalex publication_date 2021/09/13 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
Stability certification and identifying a safe and stabilizing initial set are two important concerns in ensuring operational safety, stability, and robustness of dynamical systems. With the advent of machine-learning tools, these issues need to be addressed for the systems with machine-learned components in the feedback loop. To develop a general theory for stability and stabilizability of a neural network (NN)-controlled nonlinear system subject to bounded parametric variation, a Lyapunov-based stability certificate is proposed and is further used to devise a maximal Lipschitz bound for the NN controller, and also a corresponding maximal region-of-attraction (RoA) inside a given safe operating domain. To compute such a robustly stabilizing NN controller that also maximizes the system's long-run utility, a stability-guaranteed training (SGT) algorithm is proposed. The effectiveness of the proposed framework is validated through an illustrative example.