Recent progress in reinforcement learning and adaptive dynamic programming for advanced control applications
D Wang, N Gao, D Liu, J Li… - IEEE/CAA Journal of …, 2023 - ieeexplore.ieee.org
Reinforcement learning (RL) has roots in dynamic programming and it is called
adaptive/approximate dynamic programming (ADP) within the control community. This paper …
adaptive/approximate dynamic programming (ADP) within the control community. This paper …
Adaptive dynamic programming for networked control systems under communication constraints: A survey of trends and techniques
The adaptive dynamic programming (ADP) technology has been widely used benefiting
from its recursive structure in forward and the prospective conception of reinforcement …
from its recursive structure in forward and the prospective conception of reinforcement …
Event-based adaptive NN fixed-time cooperative formation for multiagent systems
L Cao, Z Cheng, Y Liu, H Li - IEEE Transactions on Neural …, 2022 - ieeexplore.ieee.org
This article focuses on the fixed-time formation control problem for nonlinear multiagent
systems (MASs) with dynamic uncertainties and limited communication resources. Under the …
systems (MASs) with dynamic uncertainties and limited communication resources. Under the …
A brief survey on nonlinear control using adaptive dynamic programming under engineering-oriented complexities
Nonlinear dynamics is frequently encountered in practical applications. Adaptive dynamic
programming (ADP), which is implemented via actor/critic neural networks with excellent …
programming (ADP), which is implemented via actor/critic neural networks with excellent …
Simplified ADP for event-triggered control of multiagent systems against FDI attacks
In this article, an optimal control scheme against false data injection (FDI) attacks for
multiagent systems with unavailable system states is presented. The output data that …
multiagent systems with unavailable system states is presented. The output data that …
Evolving and incremental value iteration schemes for nonlinear discrete-time zero-sum games
In this article, evolving and incremental value iteration (VI) frameworks are constructed to
address the discrete-time zero-sum game problem. First, the evolving scheme means that …
address the discrete-time zero-sum game problem. First, the evolving scheme means that …
Adjustable Iterative -Learning Schemes for Model-Free Optimal Tracking Control
This article puts emphasis on the deterministic value-iteration-based-learning (VIQL)
algorithm with adjustable convergence speed, followed by the application verification on …
algorithm with adjustable convergence speed, followed by the application verification on …
Neural-network-based adaptive event-triggered asymptotically consensus tracking control for nonlinear nonstrict-feedback MASs: An improved dynamic surface …
In this article, the asymptotic tracking control problem for a class of nonlinear multi-agent
systems (MASs) is researched by the combination of radial basis function neural networks …
systems (MASs) is researched by the combination of radial basis function neural networks …
Approximating Nash equilibrium for anti-UAV jamming Markov game using a novel event-triggered multi-agent reinforcement learning
In the downlink communication, it is currently challenging for ground users to cope with the
uncertain interference from aerial intelligent jammers. The cooperation and competition …
uncertain interference from aerial intelligent jammers. The cooperation and competition …
Stability and admissibility analysis for zero-sum games under general value iteration formulation
In this article, the general value iteration (GVI) algorithm for discrete-time zero-sum games is
investigated. The theoretical analysis focuses on stability properties of the systems and also …
investigated. The theoretical analysis focuses on stability properties of the systems and also …