文件名称:zhaoxiaopu
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位置指令为幅值为1.0的阶跃信号,r(k)=1.0。网络结构为1-4-1,高斯函数的参数值取Cj=[-2 -1 1 2]T ,B=[0.5 0.5 0.5 0.5]T 。
网络权值学习参数为η=0.30,α=0.05 。PID控制各参数的初RBF网络控制,被控对象为G(s)=
取采样时间为1ms,采用Z变换进行离散化,离散化后的被控对象为
y(k)=-den(2)*y(k-1)-den(3)*y(k-2)+num(2)*u(k-1)+num(3)*u(k-2)
始值为,kp=20, kd=0.3, ki=0.1。
-Position command for the step signal amplitude of 1.0, r (k) = 1.0. The network structure is 1-4-1, the Gaussian function parameters taken Cj = [-2-1 1 2] T, B = [0.5 0.5 0.5 0.5] T. Weight learning parameter η = 0.30, α = 0.05. PID control parameters at the beginning of each RBF network control, the controlled object is G (s) = take the sampling time is 1ms, using the Z transform discrete, discretized controlled object is y (k) =-den (2)* y (k-1)-den (3)* y (k-2)+num (2)* u (k-1)+num (3)* u (k-2) initial value, kp = 20, kd = 0.3, ki = 0.1.
网络权值学习参数为η=0.30,α=0.05 。PID控制各参数的初RBF网络控制,被控对象为G(s)=
取采样时间为1ms,采用Z变换进行离散化,离散化后的被控对象为
y(k)=-den(2)*y(k-1)-den(3)*y(k-2)+num(2)*u(k-1)+num(3)*u(k-2)
始值为,kp=20, kd=0.3, ki=0.1。
-Position command for the step signal amplitude of 1.0, r (k) = 1.0. The network structure is 1-4-1, the Gaussian function parameters taken Cj = [-2-1 1 2] T, B = [0.5 0.5 0.5 0.5] T. Weight learning parameter η = 0.30, α = 0.05. PID control parameters at the beginning of each RBF network control, the controlled object is G (s) = take the sampling time is 1ms, using the Z transform discrete, discretized controlled object is y (k) =-den (2)* y (k-1)-den (3)* y (k-2)+num (2)* u (k-1)+num (3)* u (k-2) initial value, kp = 20, kd = 0.3, ki = 0.1.
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zhaoxiaopu.m
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