Objectives The availability of large and varied Electroencephalogram (EEG) datasets,
rapidly advances and inventions in deep learning techniques, and highly powerful and …

In this article, the problem of adaptive backstepping finite-time tracking control is
investigated for a class of strict-feedback nonlinear systems with unmodeled dynamics and …

The use of reinforcement learning (RL) for dynamic obstacle avoidance (DOA) algorithms
and path planning (PP) has become increasingly popular in recent years. Despite the …

Recommender systems provide personalized service support to users by learning their
previous behaviors and predicting their current preferences for particular products. Artificial …

The problem of fuzzy-based adaptive event-triggered tracking control is investigated for a
class of non-strict-feedback nonlinear systems within fixed-time interval in this paper. Fuzzy …

This article investigates the model-free fault-tolerant containment control problem for
multiagent systems (MASs) with time-varying actuator faults. Depending on the relative state …

This article proposes a fault-tolerant adaptive multigradient recursive reinforcement learning
(RL) event-triggered tracking control scheme for strict-feedback discrete-time multiagent …

This paper considers the human-in-the-Ioop leader-following consensus control problem of
multi-agent systems (MASs) with unknown matched nonlinear functions and actuator faults …

This article focuses on the distributed cooperative compound tracking issue of the vehicular
platoon. First, a definition, called compound tracking control, is proposed, which means that …

This article considers the problem of finite-time adaptive fuzzy prescribed performance
control (PPC) via output-feedback for nonstrict-feedback nonlinear systems. The fuzzy state …