Safe Output-Feedback Adaptive Optimal Control of Input-Constrained Control-Affine Nonlinear Systems

Abstract

In this paper, a novel online, safe output-feedback, critic-only, adaptive optimal control framework is developed for safety-critical control of partially observable systems. The developed framework ensures system stability and safety, regardless of the lack of full-state measurements, while learning and implementing a near-optimal controller. The approach leverages linear matrix inequality-based observer design methods to efficiently search for observer gains for effective state estimation. Then, approximate dynamic programming is used to develop an approximate controller that uses simulated experiences to guarantee the safety and stability of the closed-loop system. Safety is enforced by adding a recentered robust Lyapunov-like barrier function to the cost function that effectively enforces safety constraints, even in the presence of state estimation errors. Lyapunov-based stability analysis is used to guarantee uniform ultimate boundedness of the trajectories of the closed-loop system and ensure safety. Simulation studies are performed to demonstrate the effectiveness of the developed method through two real-world safety-critical scenarios, specifically one ensuring that the state trajectories of a given system remain within a given set, and the other ensuring that the system avoids an obstacle.