Active Vibration Control Of Flexible Linkage Mechanisms Using Neural Networks

On the basis of Kineto-ElastoDynamics (KED), Modern Control Theory and Neural Networks (NN), this dissertation studies, in a systematic way, the theory and the methods of the NN based Active Vibration Control of Flexible Linkage Mechanisms whose flexible links are incorporated with piezoceramic actuators and strain gauge sensors. Some achievements acquired are as the follows:(1) To meet the demands of the identification and control of nonlinear dynamic systems, the Dynamic Recurrent Neural Networks (DRNN) are applied in this study, which integrate the nonlinear mapping abilities of the Feedforward NN with the dynamic evolution capabilities of the Feedback NN.(2) An improved learning algorithm is proposed for DRNN whose search steps can be regulated adaptively. The learning speed of DRNN is increased remarkably and the convergence of the algorithm is certified theoretically.(3) The Pruning Algorithms are made use of in the dissertation to determine the suitable topology of NN. The partial derivatives of the learning error to the outputs of the hidden neurons are defined as the sensitivities of DRNN. Those redundant neurons are discarded progressively and the generalization capability of DRNN is improved finally.(4) With the help of the experimental samples, a DRNN identifier is trained off-line utilizing the compound identification method. The nonlinear dynamic model is achieved for the experimental mechanism. Simulation results indicate that the DRNN identifier is more accurate than the traditional KED theoretical model.(5) With reference to the parameter estimation problem, a linearized model of the experimental mechanism is established, whose state equation is determined by means of the continuous Hopfield Neural Networks (HNN). In addition, a novel HNN method is put forward to deal with the estimation problem of the state-space expressions of general linear dynamic systems.(6) This dissertation makes a detailed study of the NN based adaptive control of nonlinear dynamic systems. Two types of DRNN controllers are designed using the experimental samples, which are named the open-loop controller and the close-loop controller respectively. The experimental mechanism is controlled on-line by means of the NN based Direct Self-Tuning Control strategy and the NN based Indirect Adaptive Control strategy. The dynamic response of the experimental mechanism is improved significantly. The crest strain of the flexible coupler is reduced 50 percent or so.(7) Taking advantage of the Lyapunov Theorems, a simplified method is proposed in this dissertation to testify the local stabilization of the experimental control system, which can be used for reference in the stability analysis of general NN based nonlinear control systems.