温室机器人驱动控制系统的算法研究

为了使机器人的行动能适应温室的复杂情况,提高机器人的运动控制精度,将模拟PID控制算法离散化,对编码反馈误差进行归一化处理,并将其输入3层的BP神经网络,研究了隐含层加权系数的计算方法,完成并验证了模糊BP神经网络PID控制系统的算法。结果证明,在同一指令时间内,模糊神经网络控制器能够很好地完成角度指令的零误差调节,与常规PID控制器相比较,模糊神经网络控制器的超调量显著减小。

Research on Algorithm of Driving and Controlling System of Greenhouse Robot

In order to make the robot adapt to the complex situation of greenhouse, improve the precision of move controlling, analog PID was discrete, the feedback error of coding was normalized, and three-layer BP neural network was inputted in the research, the calculating method of hidden-layer addition coefficient was introduced, algorithm of BP neural network fuzzy PID controlling was completed and verdict. The result of test showed in the same instruction time, neural network fuzzy controller could achieve zero error measurement about angle instruction, and neural network fuzzy controller remarkably minish overtop value compared with the general PID controller.