Genetic algorithms in learning fuzzy hierarchical control of distributed parameter systems
Mohammad-R. Akbarzadeh-T, Kishan Kumar Kumbla, Mohammad Ali Jamshidi · 2002
A GA-optimized, two-level, fuzzy hierarchical controller which utilizes both spatial and temporal measured data for control of distributed parameter systems is discussed in this paper. A distributed model of a single uniformly flexible link illustrates a possible control configuration. A Dynamic Fuzzy package is developed in C for interfacing with the simple genetic algorithm (SGA) which is a C translation of Goldberg's SGA in Pascal. Dynamic Fuzzy allows for creating new rules, disposing of old undesirable rules and tuning of the membership functions. Dynamic Fuzzy's memory management and data handling is also designed to reduce memory access time for real-time applications. Specifically, the real time hardware aspects of such a hybrid control is discussed using the Texas Instrument's TMS320C30 DSP chip and the DSP Research's TIGER 30 board.