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

Based on the interval type-2 fuzzy (IT2F) approach, this article investigates the fault
detection filter design problem for a class of nonhomogeneous higher level Markov jump …

This article studies the disturbance observer-based adaptive fuzzy finite-time control issue of
strict-feedback nonlinear systems. Specifically, to meet practical application requirement, the …

This paper reviews recent developments in learning-based adaptive optimal output
regulation that aims to solve the problem of adaptive and optimal asymptotic tracking with …

This article considers a problem of tracking, convergence of disturbance observer (DO)
based optimal control design for uncertain surface vessels (SVs) with external disturbance …

This paper presents a reliable intelligent path following control method for a robotic airship
subject to sensor faults. First, an adaptive backstepping sliding mode controller is designed …

In this paper, an event-triggered optimal tracking control of discrete-time multi-agent systems
is addressed by using reinforcement learning. In contrast to traditional reinforcement …

In this article, a novel reinforcement learning (RL) method is developed to solve the optimal
tracking control problem of unknown nonlinear multiagent systems (MASs). Different from …

In this paper, the problems of output tracking control and filtering are investigated for Takagi-
Sugeno fuzzy-approximation-based nonlinear descriptor systems in the discrete-time …

This paper is concerned with finite-time H∞ filtering problem for networked state-dependent
uncertain systems with event-triggered mechanism and multiple attacks, which consists of …