This paper presents a partially model-free adaptive optimal control solution to the
deterministic nonlinear discrete-time (DT) tracking control problem in the presence of input …

In this paper, an output-feedback solution to the infinite-horizon linear quadratic tracking
(LQT) problem for unknown discrete-time systems is proposed. An augmented system …

By the virtue of its highly nonlinear magnetic characteristics, the Switched Reluctance
Machine (SRM) poses a formidable challenge for digital current regulators operating at a …

In this article, a novel Q-learning scheduling method for the current controller of a switched
reluctance motor (SRM) drive is investigated. The Q-learning algorithm is a class of …

In this paper motivated by recently discovered neurocognitive models of mechanisms in the
brain, a new reinforcement learning (RL) method is presented based on a novel critic neural …

This paper presents an online learning algorithm based on integral reinforcement learning
(IRL) to design an output‐feedback (OPFB) H∞ tracking controller for partially unknown …

Discrete‐time adaptive control algorithms can be executed directly in embedded code unlike
their continuous‐time counterparts, which require discretization. However, their designs …

In this note, optimal tracking control for uncertain continuous-time nonlinear system is
investigated by using a novel reinforcement learning (RL) scheme. The uncertainty here …

Otimal tracking control of discrete‐time non‐linear systems is investigated in this paper. The
system drift dynamics is unknown in this investigation. Firstly, in the light of the discrete‐time …

This paper proposes a model-free solution for solving the optimal regulation problem for a
discrete-time linear time-invariant system that unlike previous methods, presents a …