Stochastic Feedback Control of Systems with Unknown Nonlinear Dynamics
This work addresses control of systems with unknown dynamics, but it appears incremental as it builds on existing LQG and linearization methods without claiming major breakthroughs.
The paper tackles the stochastic optimal control problem for systems with unknown nonlinear dynamics by designing a Linear Quadratic Gaussian controller based on a nominal trajectory and linearized system identified from experimental data, with a computational example illustrating performance.
This paper studies the stochastic optimal control problem for systems with unknown dynamics. First, an open-loop deterministic trajectory optimization problem is solved without knowing the explicit form of the dynamical system. Next, a Linear Quadratic Gaussian (LQG) controller is designed for the nominal trajectory-dependent linearized system, such that under a small noise assumption, the actual states remain close to the optimal trajectory. The trajectory-dependent linearized system is identified using input-output experimental data consisting of the impulse responses of the nominal system. A computational example is given to illustrate the performance of the proposed approach.