What is he best known for?
The fields of study he is best known for:
- Control theory
- Artificial intelligence
- Control engineering
Chang-Chieh Hang focuses on Control theory, Adaptive control, PID controller, Control engineering and Control theory.
His work is connected to Multivariable calculus, Nonlinear system, Control system and Decoupling, as a part of Control theory.
His Adaptive control study integrates concerns from other disciplines, such as Nonlinear control, Artificial neural network, Feedback linearization, Lyapunov function and Adaptive system.
The concepts of his PID controller study are interwoven with issues in Process control, Automatic tuning and Control.
His work carried out in the field of Process control brings together such families of science as Model predictive control and Internal model.
His work on Gain scheduling as part of general Control engineering research is frequently linked to Process modeling, bridging the gap between disciplines.
His most cited work include:
- Stable Adaptive Neural Network Control (606 citations)
- Automatic Tuning and Adaptation for PID Controllers—A Survey (590 citations)
- Refinements of the Ziegler-Nichols tuning formula (576 citations)
What are the main themes of his work throughout his whole career to date?
Control theory, PID controller, Control engineering, Control theory and Adaptive control are his primary areas of study.
Chang-Chieh Hang works mostly in the field of Control theory, limiting it down to topics relating to Artificial neural network and, in certain cases, Algorithm, as a part of the same area of interest.
Chang-Chieh Hang interconnects Frequency response, Process control, Heuristics and Multivariable calculus in the investigation of issues within PID controller.
His work focuses on many connections between Control engineering and other disciplines, such as Knowledge base, that overlap with his field of interest in Heuristic.
His work in Control theory addresses subjects such as Dead time, which are connected to disciplines such as Integrator.
His Adaptive control study integrates concerns from other disciplines, such as Lyapunov stability, Feedback linearization, Lyapunov function, Robust control and Adaptive system.
He most often published in these fields:
- Control theory (69.44%)
- PID controller (31.25%)
- Control engineering (29.86%)
What were the highlights of his more recent work (between 2007-2015)?
- Disruptive innovation (11.11%)
- Marketing (10.42%)
- Control theory (69.44%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Disruptive innovation, Marketing, Control theory, Innovation management and Industrial organization.
The concepts of his Disruptive innovation study are interwoven with issues in Process management and Knowledge management, Technology management.
In his research, Niche market and Bottom of the pyramid is intimately related to Reverse innovation, which falls under the overarching field of Marketing.
His Control theory study focuses on PID controller in particular.
His biological study spans a wide range of topics, including Full state feedback, Variation, Controller design and Delay time.
His studies in Industrial organization integrate themes in fields like Value and Competitive advantage.
Between 2007 and 2015, his most popular works were:
- Developing Disruptive Products for Emerging Economies: Lessons From Asian Cases (81 citations)
- Government׳s role in disruptive innovation and industry emergence: The case of the electric bike in China (59 citations)
- Creating technology candidates for disruptive innovation: Generally applicable R&D strategies (48 citations)
In his most recent research, the most cited papers focused on:
- Control theory
- Artificial intelligence
- Marketing
Chang-Chieh Hang spends much of his time researching Marketing, Disruptive innovation, Innovation management, China and Multinational corporation.
His Marketing study combines topics in areas such as Delphi method and Knowledge management.
His research ties Technology management and Disruptive innovation together.
His Innovation management research includes themes of Economic growth and Consumer behaviour.
There are a combination of areas like Automotive industry, Practical implications, Government, Transition economy and New energy integrated together with his China study.
Many of his studies on Reverse innovation involve topics that are commonly interrelated, such as Industrial organization.
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.
