2023 - Research.com Rising Star of Science Award
2022 - Research.com Rising Star of Science Award
His primary areas of investigation include Mathematical optimization, Optimal control, Nonlinear system, Artificial neural network and Control theory. His study in Dynamic programming and Bellman equation is carried out as part of his Mathematical optimization studies. Ding Wang combines subjects such as Lyapunov function, Robust control, Iterative method, Intelligent control and Adaptive control with his study of Optimal control.
His study in Nonlinear system is interdisciplinary in nature, drawing from both Stability, Algorithm and Dynamical system. His Artificial neural network research is multidisciplinary, relying on both Control theory and Linear-quadratic-Gaussian control. His research integrates issues of Nash equilibrium and Reinforcement learning in his study of Control theory.
Ding Wang focuses on Artificial neural network, Nonlinear system, Optimal control, Control theory and Mathematical optimization. His Artificial neural network research incorporates themes from Discrete time and continuous time, Control engineering, Control, Intelligent control and Reinforcement learning. His studies deal with areas such as Differential game, Approximation algorithm and System dynamics as well as Nonlinear system.
His Optimal control research includes themes of Stability, Dynamic programming and Adaptive control. The various areas that Ding Wang examines in his Dynamic programming study include Dual control theory and Bellman equation. The concepts of his Mathematical optimization study are interwoven with issues in Algorithm and Computational intelligence.
Ding Wang mainly focuses on Artificial neural network, Optimal control, Nonlinear system, Control theory and Mathematical optimization. His study in Artificial neural network is interdisciplinary in nature, drawing from both Control theory, Discrete time and continuous time and Adaptive system. Ding Wang has included themes like Dynamical systems theory, Dynamic programming, Robustness and Bellman equation in his Optimal control study.
His work on Stability theory and Exponential stability as part of general Nonlinear system study is frequently linked to Uniform boundedness, therefore connecting diverse disciplines of science. His Control theory study frequently draws connections between related disciplines such as Tracking. His Iterative method study, which is part of a larger body of work in Mathematical optimization, is frequently linked to Population and Process, bridging the gap between disciplines.
Ding Wang spends much of his time researching Nonlinear system, Control theory, Artificial neural network, Optimal control and Stability. The Nonlinear system study combines topics in areas such as Control theory, Dynamic programming and Adaptive system. The study incorporates disciplines such as Stability criterion, Exponential stability and Trajectory in addition to Control theory.
His Dynamic programming research is multidisciplinary, relying on both Recurrent neural network and Actuator, Robustness, Adaptive learning, Artificial intelligence. His work deals with themes such as Approximation algorithm, Robust control and Electric power system, which intersect with Optimal control. His Stability research is multidisciplinary, incorporating perspectives in Discrete time and continuous time, State observer and Bellman equation.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Optimal control of unknown nonaffine nonlinear discrete-time systems based on adaptive dynamic programming
Ding Wang;Derong Liu;Qinglai Wei;Dongbin Zhao.
Adaptive Dynamic Programming for Control: Algorithms and Stability
Huaguang Zhang;Derong Liu;Yanhong Luo;Ding Wang.
Decentralized Stabilization for a Class of Continuous-Time Nonlinear Interconnected Systems Using Online Learning Optimal Control Approach
Derong Liu;Ding Wang;Hongliang Li.
IEEE Transactions on Neural Networks (2014)
Reinforcement-Learning-Based Robust Controller Design for Continuous-Time Uncertain Nonlinear Systems Subject to Input Constraints
Derong Liu;Xiong Yang;Ding Wang;Qinglai Wei.
IEEE Transactions on Systems, Man, and Cybernetics (2015)
Neural-Network-Based Optimal Control for a Class of Unknown Discrete-Time Nonlinear Systems Using Globalized Dual Heuristic Programming
Derong Liu;Ding Wang;Dongbin Zhao;Qinglai Wei.
IEEE Transactions on Automation Science and Engineering (2012)
Adaptive Critic Nonlinear Robust Control: A Survey
Ding Wang;Haibo He;Derong Liu.
IEEE Transactions on Systems, Man, and Cybernetics (2017)
Model-Free Optimal Tracking Control via Critic-Only Q-Learning
Biao Luo;Derong Liu;Tingwen Huang;Ding Wang.
IEEE Transactions on Neural Networks (2016)
Neural-Network-Based Online HJB Solution for Optimal Robust Guaranteed Cost Control of Continuous-Time Uncertain Nonlinear Systems
Derong Liu;Ding Wang;Fei-Yue Wang;Hongliang Li.
IEEE Transactions on Systems, Man, and Cybernetics (2014)
Finite-horizon neuro-optimal tracking control for a class of discrete-time nonlinear systems using adaptive dynamic programming approach
Ding Wang;Derong Liu;Qinglai Wei.
Adaptive Neural State-Feedback Tracking Control of Stochastic Nonlinear Switched Systems: An Average Dwell-Time Method
Ben Niu;Ding Wang;Naif D. Alotaibi;Fuad E. Alsaadi.
IEEE Transactions on Neural Networks (2019)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: