Reinforcement learning (RL) has roots in dynamic programming and it is called
adaptive/approximate dynamic programming (ADP) within the control community. This paper …

This paper is concerned with a novel integrated multi-step heuristic dynamic programming
(MsHDP) algorithm for solving optimal control problems. It is shown that, initialized by the …

This article is concerned with the stability of the closed-loop system using various control
policies generated by value iteration. Some stability properties involving admissibility …

In this article, a new self-learning parallel control method, which is based on adaptive
dynamic programming (ADP) technique, is developed for solving the optimal control …

This article develops a novel online adaptive dynamic programming algorithm with
adjustable convergence rate to address the optimal control problem of nonlinear systems …

With the rapid development of industry, the amount of wastewater discharge is increasing. In
order to improve the efficiency of the wastewater treatment process (WWTP), we often desire …

In problems with complex dynamics and challenging state spaces, the dual heuristic
programming (DHP) algorithm has been shown theoretically and experimentally to perform …

To attenuate the effect of disturbances on control performance, a multi‐step adaptive critic
control (MsACC) framework is developed to solve zero‐sum games for discrete‐time …

In this article, the synchronous fault-tolerant near-optimal control strategy design problem is
studied for a class of discrete-time nonlinear pursuit-evasion (PE) games. In the studied PE …

Globalized dual heuristic programming (GDHP) is the most comprehensive adaptive critic
design, which employs its critic to minimize the error with respect to both the cost-to-go and …