Adaptive, Intelligent Methods for Real Time Structural Control and Health Monitoring

By framing the structural health monitoring and control problem as being one of enhancing structural system intelligence, novel solutions can be achieved through applications of computational strategies that mimic human learning and attempt to replicate human response to sensory feedback. This thesis proposes several new methods which promote adaptive, intelligent decision making by structural systems relying on sensory feedback and actuator compensation. Four significant contributions can be found in this thesis study. The first method employs an adaptable subclass of Artificial Neural Networks (ANNs), called Radial Basis Function Networks (RBFNs) for robust control in the presence of sensory failure. The second method exploits this computationally efficient network to detect and isolate system faults in real time. The third algorithm utilizes an RBFN to effectively linearize the nonlinear actuator dynamics of a Magnetorheological (MR) damper, thereby improving control of the semiactive device. Lastly, an open loop observer is implemented experimentally to both detect damage and act as a trigger for control of the newly developed Adaptive Length Pendulum-Smart Tuned Mass Damper (ALP-STMD).;Some limitation of existing algorithms in the field of real time structural health monitoring and control are that they rely heavily on fixed parameter methods, assume standard linear time invariant assumptions, or mandate accurate modeling of system dynamics. By embedding the proposed reasoning and decision making algorithms into the feedback methodology and design, greater generalization and system adaptivity is possible. Specifically, the proposed methods develop novel solutions for adaptive neural control, fault (sensor failure) tolerant control, real time damage detection, adaptive dynamic inversion, and control applications for STMDs.;The neural network adaptive control formulation is successful in rejecting first mode disturbances despite online sensor failure. It is also capable of improving the performance of a baseline Hinfinity controller in the presence of sensor failure and earthquake ground motion. The proposed fault tolerant controller is validated on a two degree of freedom shear frame subjected to six earthquake records. Furthermore, this application involves the use of piezoelectric patches as sensors and actuators.;The RBFN algorithm in combination with an open loop observer is capable of both detecting and isolating stiffness degradation and recovery in multi-degree of freedom systems in real time. The method is validated on experimental data taken from online damage tests using the Semi-Active Independent Variable Stiffness (SAIVS) device. Other validations involve simulations on a two degree of freedom system and a ten degree of freedom system with both independent and coupled damage case scenarios. In all scenarios, the RBFN is capable of identifying the length of time and degree of freedom in which stiffness variation occurred.;A neural network formulation is developed to perform dynamic inversion for semiactive control of an MR damper. The MR damper acts as a base isolator in a scaled two story building. Both the building and damper models were based on tests performed at Rice University. The control performance of the adaptive RBFN dynamic inversion method is compared to both passive-off and passive-on methods of semiactive control for MR dampers.;The last contribution serves to combine both real time structural health monitoring and control in a proof of concept experimental study. An open loop observer is used to trigger an ALP-STMD device in the presence of base excitation and stiffness damage. The stiffness damage is generated from strategically regulating the current applied to Shape Memory Alloy (SMA) braces in a two degree of freedom shear frame. Once damage exceeds a predefined threshold, the ALP-STMD uses a another SMA to adjust its pendulum length to tune in real time to the dominant pulse present in the base excitation.