Bouchard M, Paillard B, Le Dinh C T
School of Information Technology and Engineering, University of Ottawa, Ottawa, Ontario K1N 6N5, Canada.
IEEE Trans Neural Netw. 1999;10(2):391-401. doi: 10.1109/72.750568.
Active control of sound and vibration has been the subject of a lot of research in recent years, and examples of applications are now numerous. However, few practical implementations of nonlinear active controllers have been realized. Nonlinear active controllers may be required in cases where the actuators used in active control systems exhibit nonlinear characteristics, or in cases when the structure to be controlled exhibits a nonlinear behavior. A multilayer perceptron neural-network based control structure was previously introduced as a nonlinear active controller, with a training algorithm based on an extended backpropagation scheme. This paper introduces new heuristical training algorithms for the same neural-network control structure. The objective is to develop new algorithms with faster convergence speed (by using nonlinear recursive-least-squares algorithms) and/or lower computational loads (by using an alternative approach to compute the instantaneous gradient of the cost function). Experimental results of active sound control using a nonlinear actuator with linear and nonlinear controllers are presented. The results show that some of the new algorithms can greatly improve the learning rate of the neural-network control structure, and that for the considered experimental setup a neural-network controller can outperform linear controllers.
声音和振动的主动控制近年来一直是大量研究的主题，并且应用实例现在也很多。然而，非线性主动控制器的实际应用却很少。在主动控制系统中使用的执行器表现出非线性特性的情况下，或者在要控制的结构表现出非线性行为的情况下，可能需要非线性主动控制器。一种基于多层感知器神经网络的控制结构先前被作为非线性主动控制器引入，其训练算法基于扩展的反向传播方案。本文为相同的神经网络控制结构引入了新的启发式训练算法。目标是开发收敛速度更快（通过使用非线性递归最小二乘算法）和/或计算负荷更低（通过使用计算代价函数瞬时梯度的替代方法）的新算法。给出了使用具有线性和非线性控制器的非线性执行器进行主动声音控制的实验结果。结果表明，一些新算法可以大大提高神经网络控制结构的学习率，并且对于所考虑的实验装置，神经网络控制器可以优于线性控制器。
