A Data-Driven Algorithm for Model-Free Control Synthesis
2026-02-13 • Robotics
Robotics
AI summaryⓘ
The authors developed a method to create the best long-term controller for a system without needing to know how the system works internally. Instead, their method only uses a limited set of input and output data from the system. They solve a special optimization problem that ensures the controller behaves optimally over time. Their approach not only finds the usual feedback control settings but also adds a way to follow desired reference signals. The authors support their method theoretically and demonstrate it with examples, including tests on a real airplane.
Linear Quadratic Regulator (LQR)feedback controlcontinuous-time systemsoptimizationvalue functionreference trackingsystem identificationinput-output datacontroller synthesis
Authors
Sean Bowerfind, Matthew R. Kirchner, Gary Hewer
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.