Abstract
Combining P-type iterative learning (IL) control, fuzzy logic control and artificial bee colony (ABC) algorithm, a new optimal fuzzy IL controller is designed for active vibration control of piezoelectric smart structures. In order to accelerate the learning speed of feedback gain, the fuzzy logic controller is integrated into the ANSYS finite element (FE) models by using APDL (ANSYS Parameter Design Language) approach to adjust adaptively the learning gain of P-type IL control. For improving the performance and robustness of the fuzzy logic controller as well as diminishing human intervention in the operation process, ABC algorithm is used to automatically identify the optimal configurations for values in fuzzy query table, fuzzification parameters and defuzzification parameters, and the main program of ABC algorithm is operated in MATLAB. The active vibration equations are driven from the FE equations for the dynamic response of a linear elastic piezoelectric smart structure. Considering the vibrations generated by various external disturbances, the optimal fuzzy IL controller is numerically investigated for a clamped piezoelectric smart plate. Results demonstrate that the proposed control approach makes the feedback gain has a fast learning speed and performs excellent in vibration suppression. This is demonstrated in the results by comparing the new control approach with the P-type IL control.
Highlights
As an intelligent control strategy, iterative learning (IL) control has a simple controller structure and doesn’t require accurate system model
The optimal fuzzy IL control system for vibration suppression of piezoelectric smart structures can be designed by applying MATLAB and ANSYS
By using ANSYS Parameter Design Language (APDL) approach, P-type IL controller and fuzzy logic controller is incorporated into the ANSYS finite element (FE) model to simulate the vibration control actions of the piezoelectric smart structure
Summary
As an intelligent control strategy, iterative learning (IL) control has a simple controller structure and doesn’t require accurate system model. The effectiveness of fuzzy logic controller depends substantially on appropriate configurations of the membership functions, selections of fuzzy rules and proper operations of the fuzzification and defuzzification, which are all based on the knowledge and experience of researchers and problems being considered [18] Different optimization technique such as genetic algorithm (GA) [19], ant colony optimization (ACO) [20], and particle swarm optimization (PSO) [21,22,23] have widely been used to find optimum controller parameters for improving the performance of dynamic systems. The values of fuzzy output variable can be chosen directly from the fuzzy query table in the process of active vibration control, and the optimal values in the fuzzy query table are searched by ABC algorithm It is the way in which all actuators can affect each other positively so that the optimum control effectiveness can be achieved.
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