This paper develops an off-policy reinforcement learning (RL) algorithm to solve optimal
synchronization of multiagent systems. This is accomplished by using the framework of …

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

In this paper, a novel off-policy interleaved Q-learning algorithm is presented for solving optimal
control problem of affine nonlinear discrete-time (DT) systems, using only the measured …

This article investigates adaptive robust controller design for discrete-time (DT) affine
nonlinear systems using an adaptive dynamic programming. A novel adaptive interleaved …

Rougher flotation, composed of unit processes operating at a fast time scale and economic
performance measurements known as operational indices measured at a slower time scale, …

This paper investigates distributed control protocols design for uncertain nonlinear multi-agent
systems with the goal of achieving the optimal consensus. The critical challenges …

Background To assess the feasibility and efficacy of using 125 I seed implantation under
intraoperative ultrasound guidance for unresectable pancreatic carcinoma. Methods Fourteen …

Glioma is the most common primary malignant tumor of the central nervous system in adults,
characterized by high mortality, low cure rate and high recurrence rate. Among gliomas, …

This paper studies the operational optimal control problem for the industrial flotation process,
a key component in the mineral processing concentrator line. A new model-free data-…

The practical industrial operation systems are not ideally immune to the effect of unmodeled
dynamics and the industrial processes generally are operated at multitime-scales, which …