Identification of Disturbed Control Systems

2019/05/15 by Li, Chanying
#FOS: Mathematics #Optimization and Control (math.OC)

paper · doi:10.48550/arxiv.1905.06232

Abstract

This paper studies the identification of nonlinearly parameterized control systems in given experiments. Several identifiability criteria are established and an implementable algorithm is proposed for practicality with the convergence rate explicitly computed.

Related

• Identification For Control Based on Neural Networks: Approximately Linearizable Models
• A Dual System-Level Parameterization for Identification from Closed-Loop Data
• On Data-Driven Surrogate Modeling for Nonlinear Optimal Control
• Nonlinear System Identification: A User-Oriented Roadmap
• A Bird's Eye View of Nonlinear System Identification