A Data-Driven Algorithm for Model-Free Control Synthesis

2026-02-13

Technology Category

Search and Optimization: Learning to SearchIntelligent Robots: Learning & Optimization for ROBNatural Language Processing: Learning & Optimization for NLP

Application Category

Economics, Online Markets and Human Computation: Cost models of using LLMs in production systemsSearch and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for rankingSemantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Presented is an algorithm to synthesize the optimal infinite-horizon LQR feedback controller for continuous-time systems. The algorithm does not require knowledge of the system dynamics but instead uses only a finite-length sampling of arbitrary input-output data. The algorithm is based on a constrained optimization problem that enforces a necessary condition on the dynamics of the optimal value function along any trajectory. In addition to calculating the standard LQR gain matrix, a feedforward gain can be found to implement a reference tracking controller. This paper presents a theoretical justification for the method and shows several examples, including a validation test on a real scale aircraft.

Problem

• model-free control
• LQR
• data-driven
• continuous-time systems
• reference tracking

Innovation

• data-driven control
• model-free LQR
• optimal control synthesis
• value function constraint
• reference tracking