基于自适应模糊神经网络控制器的网络控制系统

Network control system based on adaptive fuzzy neural network controller

网络控制系统中存在着时延、丢包、网络干扰等问题。针对网络控制系统中存在恶化系统的控制性能,甚至导致系统不稳定的因素,提出了一种基于自适应模糊神经网络控制器的网络控制系统,它能根据系统的实际输出与期望输出误差,利用自适应模糊控制和神经网络自学习的原理进行控制参数的自行调整,以符合控制系统的实际要求,同时,分析了网络延时,丢包率及网络干扰因素对系统性能的影响。利用True Time工具箱建立了包含自适应模糊神经网络控制器的网络控制系统的仿真模型,并将其分别与基于常规PID控制器的网络控制系统和基于模糊参数PID控制器的网络控制系统进行了比较。实验结果表明,在相同的网络环境下,基于自适应模糊神经网络控制器的网络控制系统的控制效果比基于常规的PID控制器和基于模糊参数PID控制器的要好,且具有较好的抗干扰能力和鲁棒性能。

Time delay, loss of the data packet and disturbance exist in the network control system. For solving this problem, a new network system based on adaptive fuzzy neural network controller is proposed. According to the error of the system between the actual output and the predicted output, this controller can be adjusted to conform to the requirements of the system by the principle of adaptive fuzzy control and self-learning of neural network control. And the main factors influencing the performance of this system, including time delay, loss rate of data packets and disturbance, are analyzed. A simulation model is established by the True Time toolbox with adaptive fuzzy neural network controller for network control system. Compared with the conventional PID controller and PID controller based on fuzzy control, the experimental results show that under the same network environment, adaptive fuzzy neural network controller has the better effect than the conventional PID controller and PID controller based on fuzzy control. Most importantly, the controller designed has better anti-jamming capability and robust performance.