Department of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.
Department of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.
ISA Trans. 2017 Sep;70:298-307. doi: 10.1016/j.isatra.2017.04.010. Epub 2017 Jun 2.
This paper proposes a novel indirect adaptive fuzzy wavelet neural network (IAFWNN) to control the nonlinearity, wide variations in loads, time-variation and uncertain disturbance of the ac servo system. In the proposed approach, the self-recurrent wavelet neural network (SRWNN) is employed to construct an adaptive self-recurrent consequent part for each fuzzy rule of TSK fuzzy model. For the IAFWNN controller, the online learning algorithm is based on back propagation (BP) algorithm. Moreover, an improved particle swarm optimization (IPSO) is used to adapt the learning rate. The aid of an adaptive SRWNN identifier offers the real-time gradient information to the adaptive fuzzy wavelet neural controller to overcome the impact of parameter variations, load disturbances and other uncertainties effectively, and has a good dynamic. The asymptotical stability of the system is guaranteed by using the Lyapunov method. The result of the simulation and the prototype test prove that the proposed are effective and suitable.
本文提出了一种新的间接自适应模糊小波神经网络（IAFWNN）来控制交流伺服系统的非线性、负载变化范围大、时变和不确定干扰。在提出的方法中，自回归小波神经网络（SRWNN）被用于为 TSK 模糊模型的每个模糊规则构造自适应自回归后件。对于 IAFWNN 控制器，在线学习算法基于反向传播（BP）算法。此外，改进的粒子群优化（IPSO）用于自适应调整学习率。自适应 SRWNN 识别器的辅助为自适应模糊小波神经网络控制器提供了实时梯度信息，有效地克服了参数变化、负载干扰和其他不确定性的影响，具有良好的动态性能。通过使用 Lyapunov 方法保证了系统的渐近稳定性。仿真和原型测试的结果证明了所提出的方法是有效和合适的。
