الفهرس 
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Abstract
Active control methods can be used to eliminate undesired vibrations in engineering structures. Using piezoelectric smart structures for the active vibration control has great potential in engineering applications. In the actively controlled system, control forces are generated using an external energy source and applied to the structure through actuators according to a prescribed control algorithm. Active systems have the advantage of strong control capacity and can be designed to influence a number of vibration modes.
The objective of this work is to develop a new dynamic model for the vibration control using the finite element analysis using ANSYS software and applying classical and intelligent control algorithms on the developed dynamic model and comparing their effects on system performance using SIMULINK software.
The finite element method (FEM) is used to derive the introduced flexible beam. Frequency analysis (modal and harmonic) of the model is performed using ANSYS software. The theory behind the extraction of flexible beam model from the finite element model is introduced. The eigenvalues and eigenvectors resulting from this analysis in each of the four cases are integrated into MATLAB, which is used to derive the state space model of the system. PID controller was designed based on the derived model.
Finite element analysis was performed using ANSYSin 4 different cases: free vibration, free vibration with a PZT patch attached to the flexible beam, flexible beam with attached PZT patch subjected to a tip load of 2.5 N 5 N in opposite directions. In each of the four cases mentioned earlier, the state space model of the beam was extracted in MATLAB from the result of its frequency analysis done in ANSYS.A PID controller based on the model obtained from the first case in a MATLAB based environment.The responses of the model to the designed PID controller in each of the 4 cases mentioned earlier were obtained in SIMULINK environment.Controller was tested on 4 cases each to 4 different desired positions.
The PID controller designed in the first case is effective when applied to the other three cases. There are limited variations in rise time with an average value of approximately 0.88 seconds. Overshoot percentage is quite high value decreasing with each case, reaching a minimum of 42 % at the fourth case and an overall average overshoot value of approximately 53 % (noting that this is still a high overshoot value). Minimum variations are noticed in settling time with an overall average of approximately 3.23 seconds.
To achieve better system performance, three more intelligent controller systems were considered: PD like Fuzzy, PID-AT (PID auto-tuning) and STFC (self-tuning fuzzy controller). In comparison with PID, PD like Fuzzy and PID-AT controllers, STFC exhibits minimum overshoot (1.48 %), rise time (0.24 sec), and settling time (0.6 sec). The classical PID controller exhibits the maximum overshoot, the maximum settling time and the maximum rise time which shows the improvement in system performance with the integration of more intelligent controller types (PD like Fuzzy, PID-AT and STFC).
The three studied intelligent controller types based on fuzzy logic (PD like Fuzzy, PID-AT and STFC) achieved much improved system performance with the STFC showing the best system performance.