Search results
C.C. Hang's 85 research works with 7,252 citations and 6,094 reads, including: Adaptive neural control of nonlinear time-delay systems with unknown virtual control coefficients
Chang-Chieh Hang received the Ph.D. degree in control engineering from the University of Warwick, Coventry, U.K., in 1973. From 1974 to 1977, he was a Computer and Systems Technologist with the Shell Eastern Petroleum Company, Singapore, and the Shell International Petroleum Company, The Netherlands.
Jul 1, 1992 · AUTOMATIC TUNING AND ADAPTATION FOR PID CONTROLLERS-A SURVEY K.J. Astrom*, T. Hagglund*, c.c. Hang** and W.K. Ho** *Department ofAutomatic Control, Lund Institute ofTechnology, Lund, Sweden **Department ofElectrical Engineering, National University ofSingapore, Singapore Ab.troct Adaptive techniques such as gain scheduling, automatic tuning and continuous adaptation have been used in ...
- Karl Johan Åström, Tore Hägglund, Chang C. Hang, Weng Kuen Ho
- 1992
Jul 1, 1993 · Australia. 1993 TUNING OF PID CONTROLLERS BASED ON GAIN AND PHASE MARGIN SPECIFICATIONS W.K. Ho, C.C. Hang and L.S. Cao Department ofElectrical Engineering, National University ofSingapore, 10 Kent Ridge Crescent, Singapore 0511 Abstract. An analytical method is proposed to tune the PIIPID controller to pass through two design points on the ...
- W.K. Ho, C.C. Hang, L.S. Cao
- 1993
Affiliations: [National University of Singapore, Singapore, SG]. Need Help? US & Canada: +1 800 678 4333 Worldwide: +1 732 981 0060 Contact & Support
A new Smith predictor for control of a process with an integrator and long dead-time is proposed in this note. The controller decouples the setpoint response from the load response. This simplifies both design and tuning. Simulation results obtained by controlling a typical process show that the new controller has superior performance compared to previous algorithms.< <ETX xmlns:mml="http ...
Mar 9, 2013 · S.S. Ge, C.C. Hang, T.H. Lee, Tao Zhang Springer Science & Business Media , Mar 9, 2013 - Science - 282 pages Recent years have seen a rapid development of neural network control tech niques and their successful applications.
