Remember Me
Or use your Academic/Social account:


Or use your Academic/Social account:


You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.


Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message


Verify Password:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Tan, K.C.; Li, Y. (2001)
Publisher: Elsevier Science
Languages: English
Types: Article
Subjects: QA75, QA76
This paper develops an evolutionary algorithm (EA) based methodology for computer-aided control system design (CACSD)\ud automation in both the time and frequency domains under performance satisfactions. The approach is automated by efficient\ud evolution from plant step response data, bypassing the system identification or linearization stage as required by conventional\ud designs. Intelligently guided by the evolutionary optimization, control engineers are able to obtain a near-optimal ‘‘off-thecomputer’’\ud controller by feeding the developed CACSD system with plant I/O data and customer specifications without the need of\ud a differentiable performance index. A speedup of near-linear pipelineability is also observed for the EA parallelism implemented on\ud a network of transputers of Parsytec SuperCluster. Validation results against linear and nonlinear physical plants are convincing,\ud with good closed-loop performance and robustness in the presence of practical constraints and perturbations.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • [1] [2] [3] [4] [5] [6] [7] Y. Li, K. C. Tan, K. C. Ng and D. J. Murray-Smith, “Performance based linear control system design by genetic evolution with simulated annealing,” Proc. 34th IEEE CDC, New Orleans, 731-7363, 1995.
    • Y. Li, K. C. Tan and C. Marionneau, “Direct design of uniform LTI controllers from plant I/O data using a parallel evolutionary algorithm,” Int. Conf. on Control'96, Special Session on Evolutionary Algorithms for Control Engineering, University of Exeter, UK, 680-686, 1996.
    • K. C. Tan and Y. Li, “Multi-objective genetic algorithm based time and frequency domain design unification of control systems,” IFAC Int. Sym. on Artificial Intelligence in Real-Time Control, Kuala Lumpur, Malaysia, 61-66, 1997.
    • Ph.D. Thesis, Dept. of Electronics and Electrical Eng., University of Glasgow, UK, 1997.
    • 2nd Asia-Pacific Conference on Control and Measurement, Chongqing, China, pp. 17-22, 1995.
    • Y. Li, K. C. Ng, K. C. Tan, D. J. Murray-Smith, G. J. Gray, K. C. Sharman and E. W. McGookin, “Automation of linear and nonlinear control systems design by evolutionary computation,” Proc. IFAC Youth Automation Conf., Beijing, China, pp. 53-58, 1995.
    • A. J. Chipperfield and P. J. Fleming, “Gas turbine engine controller design using multiobjective genetic algorithms,” Proc. First IEE/IEEE Int. Conf. on GAs in Eng. Syst.: Innovations and Appl., Univ. of Sheffield, 214-219, 1995.
    • Y. Chiang and M. G. Safonov, Robust Control Toolbox. The MathWorks, Inc, 1992.
    • J. C. Doyle, B. Francis and A. Tannenbaum, Feedback Control Theory. Macmillan Publishing Company, New York, 1992.
  • No related research data.
  • No similar publications.

Share - Bookmark

Download from

Cite this article