Major challenges of controlling human–robot collaboration (HRC)-oriented modular robot
manipulators (MRMs) include the estimation of human motion intention while cooperating …

In this article, off-policy reinforcement learning (RL) algorithm is established to solve the
discrete-time N-player nonzero-sum (NZS) games with completely unknown dynamics. The …

This paper proposes a new approach to address robust control design for nonlinear
continuous-time systems with unmatched uncertainties. First, we transform the robust control …

In this paper, a new data-driven reinforcement learning method based on Monte Carlo
simulation is developed to solve the optimal control problem of unmanned aerial vehicle …

In this paper, optimal tracking control for nonzero-sum games of multi-player continuous-
time nonlinear systems is investigated by using a novel reinforcement learning scheme …

In this paper, the optimal consensus control problem of the discrete-time multi-agent systems
with switching topologies and input delays is investigated by adopting the adaptive dynamic …

In this paper, a fuzzy logic nonzero-sum game-based distributed approximated optimal
control scheme is presented for modular robot manipulators (MRMs) with human-robot …

The use of probabilistic hesitant fuzzy elements (PHFEs) to represent the alliance status in
cooperative games can describe the player's participation levels and associated probability …

This paper considers the online optimal consensus control problem for unknown linear
discrete-time (DT) multi-agent systems (MASs). Based on time-based adaptive dynamic …

This paper investigates the optimal control of continuous-time multi-controller systems with
completely unknown dynamics using data-driven adaptive dynamic programming (DD …