| Smart structure technology has found more and more applications in vibration control, noise reduction, health monitoring, aerodynamic flow control, etc. Most smart structures, due to their considerable flexibility, distributed sensors and actuators, require a relatively high order model. The control system must also be capable of handling complexity, uncertainty, nonlinearity, and variations with time. These demand the development of suitable identification and control techniques for the application of smart structure.; Several identification and control techniques for active vibration control of nonlinear and time-varying smart structures are developed and validated experimentally for active vibration control of nonlinear and time-varying smart structures.; Three identification and modeling techniques, finite element/state space model, controlled autoregressive integrated moving average model with augmented upper diagonal identification for the adaptive parameter identification and neural network autoregressive external input model with recursive Levenberg-Marquardt optimization method for neural network online learning, are investigated. A simple effective controller, direct adaptive neural network controller is developed and implemented experimentally for the active vibration suppression. Two model based control systems, adaptive generalized predictive control system based on controlled autoregressive integrated moving average model and neural adaptive predictive control system based on neural network autoregressive external input model, are studied. Experimental performances of each model-based controller are also investigated and the comparison is made between the two adaptive generalized predictive control systems. Linear quadratic regulator based on finite element/state space model is also included to have a baseline for comparison.; Finite element/state space modeling approach is a cost-effective method for the application of smart structures. There is no need to construct expensive experimental setup before the finalization of the product. Direct adaptive neural network control is simple in concept and implementation. With online adaptation, it can deal with the uncertainty and time variation of smart structure. Without considering the control effort, the direct adaptive neural network control is not an optimal controller. Adaptive generalized predictive control and neural adaptive predictive control are optimal controllers, which take both the control result and control effort into consideration. Experimental results show that, with a nonlinear model representation of the smart structure, neural adaptive predictive control is more effective than adaptive generalized predictive control, which is based on a linear model (controlled autoregressive integrated moving average). However, with nonlinear optimization involved, neural adaptive predictive control is much more computationally expensive than adaptive generalized predictive control. |
