What is he best known for?
The fields of study he is best known for:
- Control theory
- Artificial intelligence
- Machine learning
His primary areas of study are Control theory, Iterative learning control, Adaptive control, Nonlinear system and Linearization.
His Control theory course of study focuses on Mathematical optimization and Active learning.
His research integrates issues of Control engineering and Iterative method in his study of Iterative learning control.
His Adaptive control research includes themes of Nonlinear control, Stability, Discrete time and continuous time, Model free and Partial derivative.
His Linearization research is multidisciplinary, incorporating elements of Data-driven and Feedback linearization.
The study incorporates disciplines such as Management science, State and Model based control in addition to Data-driven.
His most cited work include:
- From model-based control to data-driven control: Survey, classification and perspective (522 citations)
- Data-Driven Model-Free Adaptive Control for a Class of MIMO Nonlinear Discrete-Time Systems (281 citations)
- A Novel Data-Driven Control Approach for a Class of Discrete-Time Nonlinear Systems (262 citations)
What are the main themes of his work throughout his whole career to date?
Zhongsheng Hou mainly investigates Control theory, Nonlinear system, Iterative learning control, Adaptive control and Linearization.
His Control theory research is multidisciplinary, relying on both Control engineering and Data-driven.
His Nonlinear system study combines topics in areas such as Control system, Stability, Robustness and Affine transformation.
Zhongsheng Hou focuses mostly in the field of Iterative learning control, narrowing it down to topics relating to Tracking error and, in certain cases, Bounded function.
Zhongsheng Hou combines subjects such as Algorithm design, Attitude control, Model free, PID controller and Linear motor with his study of Adaptive control.
The study incorporates disciplines such as Model predictive control, Feedback linearization, Data model, Multi-agent system and Algorithm in addition to Linearization.
He most often published in these fields:
- Control theory (84.32%)
- Nonlinear system (52.26%)
- Iterative learning control (49.48%)
What were the highlights of his more recent work (between 2018-2021)?
- Control theory (84.32%)
- Nonlinear system (52.26%)
- Iterative learning control (49.48%)
In recent papers he was focusing on the following fields of study:
Zhongsheng Hou focuses on Control theory, Nonlinear system, Iterative learning control, Linearization and Adaptive control.
His work in Control theory tackles topics such as Data-driven which are related to areas like Compensation.
In his work, Linear system is strongly intertwined with Algorithm, which is a subfield of Nonlinear system.
The Iterative learning control study combines topics in areas such as Multi-agent system, Trajectory and Feed forward.
Zhongsheng Hou works mostly in the field of Linearization, limiting it down to topics relating to State observer and, in certain cases, Partial derivative.
His Adaptive control study incorporates themes from Model free, Computer simulation and Attitude control.
Between 2018 and 2021, his most popular works were:
- On Model-Free Adaptive Control and Its Stability Analysis (57 citations)
- Model Free Adaptive Iterative Learning Consensus Tracking Control for a Class of Nonlinear Multiagent Systems (52 citations)
- An Improved Data-Driven Point-to-Point ILC Using Additional On-Line Control Inputs With Experimental Verification (26 citations)
In his most recent research, the most cited papers focused on:
- Control theory
- Artificial intelligence
- Machine learning
Zhongsheng Hou mostly deals with Control theory, Nonlinear system, Iterative learning control, Linearization and Data-driven.
His work on Control theory deals in particular with Adaptive control, Control theory, Trajectory, PID controller and Stability.
His Nonlinear system study integrates concerns from other disciplines, such as Control system and Vehicle dynamics.
His biological study spans a wide range of topics, including Algorithm, Multi-agent system and Tracking error.
His studies deal with areas such as Discrete time and continuous time, State observer, Model predictive control and Partial derivative as well as Linearization.
His Data-driven research includes themes of Function and Mathematical optimization.
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