SUPERVISION NEURO-FLOUE A APPRENTISSAGE GENETIQUE D’UN PID ROBUSTE
Keywords:
PID flou, algorithme génétique, réseau de neurones, Fuzzy PID, genetic algorithms, neural networksAbstract
This article presents the application of a new generation of fuzzy logic supervisor (FLS) to the highly nonlinear systems. The dominant parameters characterizing the base of fuzzy knowledge: scaling factors of the Input/Output (I/O) variables,membership functions and the rule consequences are optimized by using the Genetic Algorithms (GA). The conventional PID in its improved form, where the coefficients of different actions KP, KI and KD are nonlinear variables. A fuzzy inference
system with multilayer neural network structure with genetic training plays the role of supervisor who allows giving optimal functions to these coefficients.
The fuzzy structure is specified by a combination of the mixed Takagi-Sugeno’s and Mamdani’s fuzzy Reasoning TSM-FR.The mixed integer-binary optimal coding is utilized to construct the chromosomes, which define the same of necessary prevailing parameters for the conception of the desired supervisor. This new fuzzy supervisor stands out by a non standard gain (output scaling factor) which varies linearly with the fuzzy inputs. It becomes similar to the conventional PID controller with non-linearly variable coefficients. Computer simulation indicates that the designed fuzzy supervisor is satisfactory in PID control of a nonlinear system ‘Inverted Pendulum’.
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