Self‐organized PID control design using DNA computing approach

Abstract A new design and realization method solving for the constrained multi‐objective problem via a self‐organizing PID control design using the DNA computing algorithm is proposed. Requirements of stability robustness and optimality related to the H ∞ and H 2 performance specifications are imposed as the objectives. The algorithm uses a coding method originating from the structure of biological DNA molecules to map parameters as well as the structure of PID controllers into DNA strings. Structured mutation operators are proposed to modify the control structure during the computation process. Simulations and experiments are performed to verify the performance and applicability of our design.

[1]  Bor-Sen Chen,et al.  A genetic approach to mixed H/sub 2//H/sub /spl infin// optimal PID control , 1995 .

[2]  Nancy Forbes,et al.  Imitation of Life: How Biology Is Inspiring Computing , 2004 .

[3]  L M Adleman,et al.  Molecular computation of solutions to combinatorial problems. , 1994, Science.

[4]  Yongsheng Ding,et al.  DNA genetic algorithm for design of the generalized membership-type Takagi-Sugeno fuzzy control system , 2000, Smc 2000 conference proceedings. 2000 ieee international conference on systems, man and cybernetics. 'cybernetics evolving to systems, humans, organizations, and their complex interactions' (cat. no.0.

[5]  T. Furuhashi,et al.  DNA coding method and a mechanism of development for acquisition of fuzzy control rules , 1996, Proceedings of IEEE 5th International Fuzzy Systems.

[6]  William L. Luyben,et al.  Essentials of Process Control , 1996 .

[7]  Walter Cedeño,et al.  A DNA implementation of the Max 1s problem , 1999 .

[8]  D. Wood,et al.  DNA Computing Implementing Genetic Algorithms , 1999 .

[9]  A novel approach to H/sub 2//H/sub /spl infin// robotic control with state estimation feedback , 1998, Proceedings of the 1998 IEEE International Conference on Control Applications (Cat. No.98CH36104).

[10]  Tae-Yong Kuc,et al.  An intelligent missile autopilot using genetic algorithm , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[11]  Carlo C. Maley,et al.  DNA Computation: Theory, Practice, and Prospects , 1998, Evolutionary Computation.

[12]  Theodorus J.A. de Vries,et al.  Linear motor motion control using a learning feedworward controller , 1997 .

[13]  Zuwairie Ibrahim,et al.  DNA Computing Approach to Semantic Knowledge Representation , 2005, Int. J. Hybrid Intell. Syst..

[14]  Kalyanmoy Deb,et al.  Optimization for Engineering Design: Algorithms and Examples , 2004 .

[15]  Akira Todoroki,et al.  Permutation genetic algorithm for stacking sequence design of composite laminates , 2000 .

[16]  W. Luyben.  Tuning proportional-integral-derivative controllers for integrator/deadtime processes , 1996 .

[17]  Reinhold Steinhauser,et al.  Application of vector performance optimization to a robust control loop design for a fighter aircraft , 1983 .

[18]  Yongsheng Ding,et al.  DNA genetic algorithms for design of fuzzy systems , 2000, Ninth IEEE International Conference on Fuzzy Systems. FUZZ- IEEE 2000 (Cat. No.00CH37063).

[19]  Estiko Rijanto.  Quasi mixed /spl Hscr//sub 2///spl Hscr//sub /spl infin// controller for a high precision positioning device , 2000, 6th International Workshop on Advanced Motion Control. Proceedings (Cat. No.00TH8494).