What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Control theory
- Statistics
Tianyou Chai spends much of his time researching Control theory, Control engineering, Control theory, Nonlinear system and Adaptive control.
Many of his studies on Control theory apply to Artificial neural network as well.
Tianyou Chai interconnects Control system, Decoupling, Ball mill and Fuzzy logic in the investigation of issues within Control engineering.
His Control theory research includes elements of Process control, Model predictive control and Control-Lyapunov function.
His Nonlinear system study incorporates themes from Stability, Multiplicative function, Inverse problem and Inverse function.
His study in Adaptive control is interdisciplinary in nature, drawing from both Fuzzy control system, Lyapunov redesign, Mathematical optimization, Integrator and Adaptive system.
His most cited work include:
- Global finite-time stabilization of a class of switched nonlinear systems with the powers of positive odd rational numbers (309 citations)
- Neural-Network-Based Terminal Sliding-Mode Control of Robotic Manipulators Including Actuator Dynamics (307 citations)
- Decentralized Fault Diagnosis of Large-Scale Processes Using Multiblock Kernel Partial Least Squares (205 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Control theory, Nonlinear system, Control engineering, Artificial neural network and Control theory.
In his research, Setpoint is intimately related to Process control, which falls under the overarching field of Control theory.
In his research on the topic of Nonlinear system, Robustness is strongly related with Mathematical optimization.
His work in Control engineering tackles topics such as Intelligent control which are related to areas like Process engineering.
Artificial neural network is a subfield of Artificial intelligence that Tianyou Chai investigates.
The Adaptive control study combines topics in areas such as Linear system, Multivariable calculus, Robust control, Adaptive neuro fuzzy inference system and Adaptive system.
He most often published in these fields:
- Control theory (50.29%)
- Nonlinear system (25.24%)
- Control engineering (26.40%)
What were the highlights of his more recent work (between 2018-2021)?
- Control theory (50.29%)
- Artificial neural network (18.50%)
- Mathematical optimization (16.18%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Control theory, Artificial neural network, Mathematical optimization, Nonlinear system and Algorithm.
His study in Actuator, Control theory, Control system, Tracking error and Lyapunov function are all subfields of Control theory.
Tianyou Chai focuses mostly in the field of Artificial neural network, narrowing it down to topics relating to Optimal control and, in certain cases, Discrete time and continuous time, Process control, Reinforcement learning and Q-learning.
His Mathematical optimization research focuses on Control and how it connects with Singular perturbation.
His research integrates issues of Identification, PID controller, Dynamic programming and Bellman equation in his study of Nonlinear system.
His research in Algorithm tackles topics such as Data modeling which are related to areas like Multivariate random variable, Model predictive control and Least squares.
Between 2018 and 2021, his most popular works were:
- Off-Policy Interleaved $Q$ -Learning: Optimal Control for Affine Nonlinear Discrete-Time Systems (36 citations)
- Heterogeneous Ensemble-Based Infill Criterion for Evolutionary Multiobjective Optimization of Expensive Problems (33 citations)
- Dual-Rate Operational Optimal Control for Flotation Industrial Process With Unknown Operational Model (29 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Control theory
Tianyou Chai mainly focuses on Control theory, Mathematical optimization, Optimization problem, Nonlinear system and Artificial neural network.
His Control theory study deals with Process control intersecting with Setpoint.
His Multi-objective optimization study in the realm of Mathematical optimization connects with subjects such as Symmetric matrix.
His biological study spans a wide range of topics, including Local search, Global optimization and Reinforcement learning.
His Nonlinear system research incorporates elements of Data mining, Identification, Partial derivative and Taylor series.
His study in Artificial neural network is interdisciplinary in nature, drawing from both Prediction interval, Training set, Stochastic process, Bayesian probability and Hyperparameter.
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