Tochukwu Elijah Ogri

Tochukwu Elijah Ogri, Muzaffar Qureshi, Zachary I. Bell et al.

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.

Trivikram Satharasi, Tochukwu E. Ogri, Muzaffar Qureshi et al.

This paper develops a sparsity-promoting integral concurrent learning (SP-ICL) adaptation law for a linearly parametrized uncertain nonlinear control-affine system. The unknown parameters are learned using ICL with sparsity-promoting $\ell_1$ regularization. The use of $\ell_1$ regularization for sparsity promotion is common in system identification and machine learning; however, unlike existing approaches, this paper develops an online parameter update law that integrates the regularization penalty with ICL via sliding modes. Using the SP-ICL update law, we show via non-smooth Lyapunov analysis that the trajectories of the closed-loop system are ultimately bounded. Simulations verify the effectiveness of the sparsity penalty in the SP-ICL update law on recovering sparse dynamics during trajectory tracking.