What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Control theory
- Artificial neural network
His primary areas of study are Artificial neural network, Mathematical optimization, Optimal control, Control theory and Dynamic programming.
The Artificial neural network study combines topics in areas such as Stability, Control theory and Computer network.
His work in the fields of Mathematical optimization, such as Bellman equation, intersects with other areas such as Approximation error.
His research integrates issues of Iterative method, Adaptive control, Nonlinear system, Intelligent control and Reinforcement learning in his study of Optimal control.
His Nonlinear system research is multidisciplinary, incorporating elements of Dynamical system and Algorithm.
His study in Dynamic programming is interdisciplinary in nature, drawing from both Iterative learning control, Control, Dual control theory and Discrete time and continuous time.
His most cited work include:
- Adaptive Dynamic Programming: An Introduction (614 citations)
- Neural-Network-Based Near-Optimal Control for a Class of Discrete-Time Affine Nonlinear Systems With Control Constraints (462 citations)
- A Comprehensive Review of Stability Analysis of Continuous-Time Recurrent Neural Networks (378 citations)
What are the main themes of his work throughout his whole career to date?
Artificial neural network, Control theory, Optimal control, Nonlinear system and Dynamic programming are his primary areas of study.
His Artificial neural network research is multidisciplinary, incorporating perspectives in Algorithm, Bounded function and Adaptive system.
His Optimal control study contributes to a more complete understanding of Mathematical optimization.
His Mathematical optimization research incorporates elements of Q-learning, Convergence and Discrete time and continuous time.
His work deals with themes such as Stability, Zero-sum game, Observer, Actuator and System dynamics, which intersect with Nonlinear system.
His Dynamic programming research integrates issues from Intelligent control, Tracking error, Approximation algorithm and Bellman equation.
He most often published in these fields:
- Artificial neural network (54.67%)
- Control theory (48.57%)
- Optimal control (45.52%)
What were the highlights of his more recent work (between 2016-2021)?
- Optimal control (45.52%)
- Artificial neural network (54.67%)
- Control theory (48.57%)
In recent papers he was focusing on the following fields of study:
Derong Liu focuses on Optimal control, Artificial neural network, Control theory, Dynamic programming and Nonlinear system.
Optimal control is a subfield of Mathematical optimization that he tackles.
The study incorporates disciplines such as Lyapunov stability, Stability, Tracking error, Adaptive system and Trajectory in addition to Artificial neural network.
His research investigates the connection between Control theory and topics such as Control that intersect with issues in Tracking.
His Dynamic programming study combines topics from a wide range of disciplines, such as Bellman equation, Convergence, Discrete time and continuous time, Approximation algorithm and Monotonic function.
His work on Adaptive control as part of general Nonlinear system research is often related to Scheme, thus linking different fields of science.
Between 2016 and 2021, his most popular works were:
- Adaptive Critic Nonlinear Robust Control: A Survey (128 citations)
- Mixed Iterative Adaptive Dynamic Programming for Optimal Battery Energy Control in Smart Residential Microgrids (86 citations)
- Residential energy scheduling for variable weather solar energy based on adaptive dynamic programming (77 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Control theory
- Artificial neural network
His main research concerns Optimal control, Artificial neural network, Control theory, Nonlinear system and Dynamic programming.
His Optimal control research is multidisciplinary, relying on both Gradient descent, Differential game, Function, Upper and lower bounds and Adaptive system.
His Artificial neural network study combines topics in areas such as Control engineering, Tracking error, Electric power system and Trajectory.
His studies in Nonlinear system integrate themes in fields like Control theory, Mathematical optimization, Robustness and Reinforcement learning.
His Mathematical optimization research includes themes of Scheduling, Job shop scheduling, Discrete time and continuous time and State space.
His biological study spans a wide range of topics, including Convergence, Zero-sum game, Microgrid and Bellman equation.
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