Browsing by Subject "Nonlinear control systems"

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    Nonlinear vibrations of a beam-spring-mass system
    (1994) Pakdemirli M.; Nayfeh A.H.
    The nonlinear response of a simply supported beam with an attached spring-mass system to a primary resonance is investigated, taking into account the effects of beam midplane stretching and damping. The spring-mass system has also a cubic nonlinearity. The response is found by using two different perturbation approaches. In the first approach, the method of multiple scales is applied directly to the nonlinear partial differential equations and boundary conditions. In the second approach, the Lagrangian is averaged over the fast time scale, and then the equations governing the modulation of the amplitude and phase are obtained as the Euler-Lagrange equations of the averaged Lagrangian. It is shown that the frequency-response and force-response curves depend on the midplane stretching and the parameters of the spring-mass system. The relative importance of these effects depends on the parameters and location of the spring-mass system. © 1994 ASME.
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    Optimal tuning of PI speed controller coefficients for electric drives using neural network and genetic algorithms
    (2005) Ustun S.V.; Demirtas M.
    This paper presents a method of tuning Proportional Integral (PI) controller coefficients in the off-line control of a nonlinear system. In this method, the first step is the identification of the system via Artificial Neural Networks (ANNs), using maximum overshoot and settling time obtained from the application circuit for different Kp-Ki pairs. With this in mind, multi-layer ANN, which uses back-propagation of the error algorithm, was used as the learning algorithm. In the second step, the purpose is to find the optimum controller coefficients using the ANN model as the objective function via Genetic Algorithms (GAs). A Digital Signal Processor (DSP-TMS320C50) was used to carry out control applications. The C++ language was used for ANN and GA, and and the Assembly language was used for the DSP. It is determined that maximum overshoot and settling time are very small if the system is controlled by control parameters obtained from the optimization process that uses GA.