Back to Search
Start Over
Towards Robust Data-Driven Control Synthesis for Nonlinear Systems with Actuation Uncertainty
- Publication Year :
- 2020
-
Abstract
- Modern nonlinear control theory seeks to endow systems with properties such as stability and safety, and has been deployed successfully across various domains. Despite this success, model uncertainty remains a significant challenge in ensuring that model-based controllers transfer to real world systems. This paper develops a data-driven approach to robust control synthesis in the presence of model uncertainty using Control Certificate Functions (CCFs), resulting in a convex optimization based controller for achieving properties like stability and safety. An important benefit of our framework is nuanced data-dependent guarantees, which in principle can yield sample-efficient data collection approaches that need not fully determine the input-to-state relationship. This work serves as a starting point for addressing important questions at the intersection of nonlinear control theory and non-parametric learning, both theoretical and in application. We validate the proposed method in simulation with an inverted pendulum in multiple experimental configurations.<br />Comment: 8 pages, 2 figures, submitted to Conference on Decision & Control (CDC) 2021
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2011.10730
- Document Type :
- Working Paper