A universal construction of Artstein's theorem on nonlinear stabilization
Systems & Control Letters
A menu of designs for reinforcement learning over time
Neural networks for control
Robust adaptive control
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Neuro-Dynamic Programming
Control of exploitation-exploration meta-parameter in reinforcement learning
Neural Networks - Computational models of neuromodulation
Brief paper: Image based visual servo control for a class of aerial robotic systems
Automatica (Journal of IFAC)
Brief paper: An adaptive neuro-fuzzy tracking control for multi-input nonlinear dynamic systems
Automatica (Journal of IFAC)
Brief paper: An adaptive optimization scheme with satisfactory transient performance
Automatica (Journal of IFAC)
Large scale nonlinear control system fine-tuning through learning
IEEE Transactions on Neural Networks
Local compliance estimation via positive semidefinite constrained least squares
IEEE Transactions on Robotics
Issues on Stability of ADP Feedback Controllers for Dynamical Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
High-order neural network structures for identification of dynamical systems
IEEE Transactions on Neural Networks
H∞ stability conditions for fuzzy neural networks
Advances in Fuzzy Systems
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Learning mechanisms that operate in unknown environments should be able to efficiently deal with the problem of controlling unknown dynamical systems. Many approaches that deal with such a problem face the so-called exploitation-exploration dilemma where the controller has to sacrifice efficient performance for the sake of learning "better" control strategies than the ones already known: during the exploration period, poor or even unstable closed-loop system performance may be exhibited. In this paper, we show that, in the case where the control goal is to stabilize an unknown dynamical system by means of state feedback, exploitation and exploration can be concurrently performed without the need of sacrificing efficiency. This is made possible through an appropriate combination of recent results developed by the author in the areas of adaptive control and adaptive optimization and a new result on the convex construction of control Lyapunov functions for nonlinear systems. The resulting scheme guarantees arbitrarily good performance in the regions where the system is controllable. Theoretical analysis as well as simulation results on a particularly challenging control problem verify such a claim.