CAutoCSD-evolutionary search and optimisation enabled computer automated control system design

This paper attempts to set a unified scene for various linear time-invariant (LTI) control system design schemes, by transforming the existing concept of “computer-aided control system design” (CACSD) to novel “computer-automated control system design” (CAutoCSD). The first step towards this goal is to accommodate, under practical constraints, various design objectives that are desirable in both time and frequency domains. Such performance-prioritised unification is aimed at relieving practising engineers from having to select a particular control scheme and from sacrificing certain performance goals resulting from pre-commitment to such schemes. With recent progress in evolutionary computing based extra-numeric, multi-criterion search and optimisation techniques, such unification of LTI control schemes becomes feasible, analytical and practical, and the resultant designs can be creative. The techniques developed are applied to, and illustrated by, three design problems. The unified approach automatically provides an integrator for zero-steady state error in velocity control of a DC motor, and meets multiple objectives in the design of an LTI controller for a non-minimum phase plant and offers a high-performance LTI controller network for a non-linear chemical process.

[1]  Kay Chen Tan,et al.  Evolutionary methods for modelling and control of linear and nonlinear systems , 1997 .

[2]  Bruce A. Francis,et al.  Feedback Control Theory , 1992 .

[3]  D.J. Murray-Smith,et al.  Performance based linear control system design by genetic evolution with simulated annealing , 1995, Proceedings of 1995 34th IEEE Conference on Decision and Control.

[4]  Karl Johan Åström,et al.  PID Controllers: Theory, Design, and Tuning , 1995 .

[5]  Hiroshi Kashiwagi,et al.  M-Sequence and its Applications , 1993 .

[6]  Yun Li,et al.  Multi-objective design of networking control systems for nonlinear plants and stability analysis , 2002 .

[7]  D. P. Atherton,et al.  Automatic tuning of optimum PID controllers , 1993 .

[8]  Richard Magat,et al.  Best Book Award , 2001 .

[9]  Kay Chen Tan,et al.  Performance-based control system design automation via evolutionary computing , 2001 .

[10]  Yun Li,et al.  Learning fuzzy control by evolutionary and advantage reinforcements , 1998 .

[11]  Xiaohong Guan,et al.  Adopting a Minimum Commitment Principle for Computer Aided Geometric Design Systems , 1996 .

[12]  Yun Li,et al.  Artificial evolution of neural networks and its application to feedback control , 1996, Artif. Intell. Eng..

[13]  D. Graham,et al.  The synthesis of "optimum" transient response: Criteria and standard forms , 1953, Transactions of the American Institute of Electrical Engineers, Part II: Applications and Industry.

[14]  Kim Chwee Ng Switching control systems and their design automation via genetic algorithms , 1995 .

[15]  William S. Levine,et al.  The Control Handbook , 2005 .