Nonlinear structural control using neural networks

Recently, Ghaboussi and Joghataie presented a structural control method using neural networks, in which a neurocontroller was developed and applied for linear structural control when the response of the structure remained within the linearly elastic range. One of the advantages of the neural networks is that they can learn nonlinear as well as linear control tasks. In this paper, we study the application of the previously developed neurocontrol method in nonlinear structural control problems. First, we study the capabilities of the linearly trained neurocontrollers in nonlinear structural control. Next, we train a neurocontroller on the nonlinear data and study its capabilities. These studies are done through numerical simulations, on models of a three-story steel frame structure. The control is implemented through an actuator and tendon systemin the first floor. The sensor is assumed to be a single accelerometer on the first floor. The acceleration of the first floor as well as the ground acceleration are used as feedback. In the numerical simulations we have considered the actuator dynamics and used a coupled model of the actuator-structure system. A realistic sampling period and an inherent time delay in the control loop have been used.