Fault-tolerant adaptive control of nonlinear base-isolated buildings using EMRAN

Abstract This paper presents a direct adaptive fault-tolerant neural control scheme for the active control of nonlinear hysteretic base-isolated buildings using the recently developed Extended Minimal Resource Allocation Network (EMRAN). EMRAN is a learning algorithm in which the structure of the neural controller is adapted on-line based on the input–output data. EMRAN starts with no hidden neurons and calculates the number of hidden neurons based on growing/pruning criteria. If the criteria are not met, then the parameters of the network are adjusted using an Extended Kalman Filter (EKF). The constants associated with the growing/pruning criteria and EKF are estimated using Genetic Algorithm (GA) optimization. The advantage of the proposed control architecture is its ability to learn on-line with no a priori training. Most of the existing studies in structural control using neural networks require computationally intensive off-line training. Consequently, once the network parameters are learnt, the parameters remain fixed. Such procedures require an accurate mathematical model of the system. These issues are addressed in the current controller scheme by utilizing the on-line adaptation capabilities of the neural networks. The advantages of on-line adaptation are demonstrated using the controller’s capability to handle actuator failures and system uncertainties. Performance of the proposed control scheme is evaluated using the recently developed nonlinear three-dimensional base-isolated benchmark structure incorporating lateral–torsional superstructure behavior and the biaxial interaction of the nonlinear bearings in the isolation layer. Results show that the proposed controller scheme can achieve the desired performance objectives under both partial actuator failure conditions and large uncertainties associated with the system’s parameters.

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