Data-driven performance-prescribed reinforcement learning control of an unmanned surface vehicle

N Wang, Y Gao, X Zhang - IEEE Transactions on Neural …, 2021‏ - ieeexplore.ieee.org
An unmanned surface vehicle (USV) under complicated marine environments can hardly be
modeled well such that model-based optimal control approaches become infeasible. In this …

Reinforcement learning-based optimal tracking control of an unknown unmanned surface vehicle

N Wang, Y Gao, H Zhao, CK Ahn - IEEE Transactions on …, 2020‏ - ieeexplore.ieee.org
In this article, a novel reinforcement learning-based optimal tracking control (RLOTC)
scheme is established for an unmanned surface vehicle (USV) in the presence of complex …

Observer-based adaptive fuzzy decentralized optimal control design for strict-feedback nonlinear large-scale systems

S Tong, K Sun, S Sui - IEEE Transactions on Fuzzy Systems, 2017‏ - ieeexplore.ieee.org
In this paper, the problem of adaptive fuzzy decentralized optimal control is investigated for a
class of nonlinear large-scale systems in strict-feedback form. The considered nonlinear …

Event-triggered fault-tolerant control for input-constrained nonlinear systems with mismatched disturbances via adaptive dynamic programming

H Zhao, H Wang, B Niu, X Zhao, KH Alharbi - Neural Networks, 2023‏ - Elsevier
In this paper, the issue of event-triggered optimal fault-tolerant control is investigated for
input-constrained nonlinear systems with mismatched disturbances. To eliminate the effect …

Adaptive critic nonlinear robust control: A survey

D Wang, H He, D Liu - IEEE transactions on cybernetics, 2017‏ - ieeexplore.ieee.org
Adaptive dynamic programming (ADP) and reinforcement learning are quite relevant to each
other when performing intelligent optimization. They are both regarded as promising …

Neural network control-based adaptive learning design for nonlinear systems with full-state constraints

YJ Liu, J Li, S Tong, CLP Chen - IEEE transactions on neural …, 2016‏ - ieeexplore.ieee.org
In order to stabilize a class of uncertain nonlinear strict-feedback systems with full-state
constraints, an adaptive neural network control method is investigated in this paper. The …

Reinforcement-learning-based robust controller design for continuous-time uncertain nonlinear systems subject to input constraints

D Liu, X Yang, D Wang, Q Wei - IEEE transactions on …, 2015‏ - ieeexplore.ieee.org
The design of stabilizing controller for uncertain nonlinear systems with control constraints is
a challenging problem. The constrained-input coupled with the inability to identify accurately …

Optimized backstep** for tracking control of strict-feedback systems

G Wen, SS Ge, F Tu - … on neural networks and learning systems, 2018‏ - ieeexplore.ieee.org
In this paper, a control technique named optimized backstep** is first proposed by
implementing tracking control for a class of strict-feedback systems, which considers …

Air-breathing hypersonic vehicle tracking control based on adaptive dynamic programming

C Mu, Z Ni, C Sun, H He - IEEE transactions on neural networks …, 2016‏ - ieeexplore.ieee.org
In this paper, we propose a data-driven supplementary control approach with adaptive
learning capability for air-breathing hypersonic vehicle tracking control based on action …

Improved sliding mode design for load frequency control of power system integrated an adaptive learning strategy

C Mu, Y Tang, H He - IEEE Transactions on Industrial …, 2017‏ - ieeexplore.ieee.org
Randomness from the power load demand and renewable generations causes frequency
oscillations among interconnected power systems. Due to the requirement of synchronism of …