Wavelet Adaptive Control System And Its Application To Active Noise Control

With the development of society, the topic of noise control has been received considerable attention. Either in civil or military domain, noise is polluting the environment, doing harm to our health. Even our lives are badly endangered, so the noise control is becoming important and urgent. The works in this domain have been widely investigated all over the world.As far as the active noise control system, the adaptive control strategy is generally adopted, and that the results are comparatively satisfying. However, the adaptive control also is not perfect in many aspects, for instance, the deficiencies in many adaptive algorithms are extraordinarily distinct. Consequently it is promising and significant to improve the performance of adaptive algorithm by means of new methods. Aimed at this matter, the control algorithm and control way are investigated in this paper.On the whole, the work in the paper is made up of the following part:1. Firstly, the several LMS (Least Mean Square, LMS) algorithms are analyzed and their performance is compared by the simulation results, which offer gist for the improvement of algorithm.2. Combining the Wavelets and LMS algorithm, the paper brings forward two classes of Wavelets Adaptive Algorithm (WAF) with different structure, viz., D-LMS algorithm and DR-LMS algorithm. Theoretically investigation on each algorithm is made in details. In order to achieve the real-time of WAF, the fast use of WALLAT algorithm is brought out in the paper. To study on the performance of WAF, the simulations of system identification with D-LMS algorithm and DR-LMS algorithm are made. The simulations indicate that the performance of WAF is greatly improved, which offer the powerful and efficient method for design of control system.Based on Filtered-X LMS algorithm and random adaptive control, the Wavelet Adaptive Control System (WACS) is constructed in the paper. The wavelet adaptive controller is developed; the adaptive equation of the parameter matrix of each controller is presented in details. Because, in adaptive control, the precision and real-time of system identification can have direct effect on control quality, the method of system identification is3. vital. In the paper, the parallel on-line system identification is applied in control system, which effectively solves the problem in system identification.4. Finally, active noise control with the wavelet adaptive control system (WACS) is explored. Corresponding hardware and software of wavelet adaptive controller are developed. The real-time experiment with WACS shows that noise with simple or complex frequency components can be greatly and rapidly reduced, which indicates that introducing Wavelet to LMS algorithm has improved the performance of LMS and WACS.