基于改进免疫算法和Elman神经网络的超声马达速度辨识与控制

n improved artificial immune algorithm with a dynamic threshold is presented in this paper. Numerical experiments show that compared with the genetic algorithm and the originally real-valued coding artificial immune algorithm, the improved algorithm possesses high speed of convergence and good performance of preventing the premature convergence. The proposed algorithm is employed to train the network structure, weights, initial inputs of the context units and self-feedback coefficient of the modified Elman network. A novel identifier and controller are constructed successively based on the proposed algorithm. A simulated dynamic system of the ultrasonic motor (USM) is considered as an example of a highly nonlinear system. The novel identifier and controller are applied to perform the speed identification and control of the ultrasonic motors. Numerical results show that both the identifier and controller based on the proposed algorithm possesses not only high convergent precision but also robustness to the external noise.