In order to deal with optimal setpoint tracking (OST) problems, a discounted cost function
has been introduced in the existing work. However, the optimal tracking controllers …

In this paper, we present an approximate optimal dynamic output feedback control learning
algorithm to solve the linear quadratic regulation problem for unknown linear continuous …

Reinforcement learning technique was developed recently as an interesting topic in
designing adaptive optimal controllers. This technique explicitly provided a feasible solution …

Conventionally, the finite-horizon linear quadratic tracking (FHLQT) problem relies on
solving the time-varying Riccati equations and the time-varying non-causal difference …

In this paper, we propose novel data-driven optimal dynamic controller design frameworks,
via both state-feedback and output-feedback, for solving optimal output regulation problems …

The research focuses on the data-driven optimal time-varying formation-containment control
problem for a group of heterogeneous spacecrafts that are simultaneously subjected to …

We present the development of a bias compensating reinforcement learning (RL) algorithm
that optimizes thermal comfort (by minimizing tracking error) and control utilization (by …

This article proposes an online reinforcement learning with pretraining (ORLPT) method for
multizone ventilation control. The proposed ORLPT method contains two stages, which are …

This brief discusses the direct data-driven optimal set-point tracking (OST) control problem of
linear discrete-time systems. A cost function without discount factor is defined in terms of …

This article introduces a novel reinforcement learning (RL) method for wave energy
converters (WECs), which directly generates linear noncausal optimal control (LNOC) …