Study Of Fuzzy Control For Intelligent Truss Based On Genetic Algorithm

In the paper, dynamic equation and modal equation based on modal truncation is established by taking intelligent truss structure as the research object. The previous frequency of the structure is solved by ANSYS modal analysis, according to the frequency, modal control equations of the intelligent truss structure taken as controlled object in vibration control simulation stage is established.Intelligent truss structure will generate free vibration damping under incentive load. Although the free vibration can decay automatically under damping itself, but the big vibration displacement and slow decay rate have destructive effect for structure. Fuzzy control is a kind of control that does not depend on accurate mathematical model,and it is widely used in structural vibration control field. In order to minimize damage, increase vibration decay speed, the paper designs a fuzzy controller for intelligent truss structure, and verifies its reasonable control effect by an example.The key of fuzzy controller design is fuzzy rules, whose performance determines the control effect of fuzzy system. General fuzzy rules are obtained from expert experience, in which much subjectivity exists, so the rules are not the best. The genetic algorithms which have strong global optimization ability are very suitable for the optimizing process of fuzzy controller. This paper has improved genetic algorithm, and a method optimizing fuzzy rules by improved genetic algorithm is proposed. The optimization process of fuzzy control rules has a detailed introduction, and the genetic coding method, the design of fuzzy rules and membership functions as well as the selection of fitness function is confirmed. Finally, three simulation model of 83 intelligent truss structure is built by Fuzzy logic Toolbox (Fuzzy Toolbox) and Matlab/Simulink, which contain the intelligent truss simulation model under free vibration, in control of fuzzy controller and fuzzy controller optimized by GA. The reasonable of fuzzy controller is verified by comparing the simulation result between free vibration and in control of fuzzy controller, and the effectiveness of GA optimizing fuzzy controller is verified by comparing the simulation result between fuzzy rules optimized by GA and those un-optimized.