A Method to Find an Appropriate Input Motion Using a Given Motion on Ground Surface

Some seismic design codes require to determine earthquake motions at ground surface, although we need sometimes a motion at engineering basement of ground. For example, it is necessary for optimal design of structures to introduce effects of soil-structure interaction and to input ground motions to engineering basement, which should satisfy the motions at surface required by a design code. This is simplified as a problem to find a possible input signal of a known non-linear-single-input and single-output (SISO) system with a given output signal. This study proposes a very simple algorithm to find a possible input signal for a known SISO system satisfying a given output signal from the system. The proposed algorithm searches for a possible input signal, whose system response is similar in shape to the given output signal, without any system identifications. Thus, it is not an inversion technique; it is an algorithm used to search for one of the possible input signals. Herein, the proposed algorithm is described and its performance is demonstrated using numerical examples.

Artificial Neural Network vs PID Controller for Magnetic Levitation System

This research investigates the acceptability of the Artificial Neural Networks (ANN) over the PID Controller for the control of the Magnetic Levitation System (MLS). In the field of advanced control systems, this system identifies as a feedback-controlled, single input- single output (SISO) system. This SISO system used a PID controller for vertical trajectory controlling of a metal sphere in airspace by using an electromagnetic force that directed to upward. The vertical position of the metal sphere controlled according to the applied magnetic force generated by a powerful electromagnet and the electromagnetic force controlled by varying the supply voltage. To control this nonlinear system, we develop a multilayer artificial neural network by using Matlab software and integrate that with the physical magnetic levitation model. According to specific initial conditions, the actual responses of the magnetic levitation system with artificial neural network compares the desire response of the metal sphere. The ability of control this nonlinear system by using neural networks validate by comparing results obtained by the PID controller and artificial neural network.