mirror of
https://github.com/NickNair/Adaptive-PID-controller.git
synced 2026-02-06 02:01:36 +08:00
93 lines
2.0 KiB
Python
93 lines
2.0 KiB
Python
import numpy as np
|
|
|
|
class Actor:
|
|
|
|
def __init__(self , aw , av , au , gamma ,h = 3 ):
|
|
|
|
# Initialize all parameters
|
|
|
|
self.X = np.zeros((3,1))
|
|
self.h = h
|
|
self.wh = np.zeros( (h,3) )
|
|
|
|
|
|
# actor
|
|
self.K =np.zeros( (3,1) )
|
|
|
|
|
|
# actor
|
|
self.w = np.zeros( (3, h) )
|
|
|
|
|
|
|
|
|
|
self.output = np.zeros( (h,1) )
|
|
|
|
# Learning Rates
|
|
|
|
# actor
|
|
self.aw = aw
|
|
|
|
|
|
# both
|
|
self.au = au
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def HiddenLayer(self):
|
|
|
|
# Description : Takes in the state vector at a given time step and computes the output vector for the next layer
|
|
|
|
output = 1/(1 + np.exp(self.wh.dot(self.X)) )
|
|
|
|
self.output = output
|
|
|
|
def OutputLayer(self):
|
|
|
|
# Description : Takes in output from Hiddenlayer and computes Ki,Kp and Kd values
|
|
|
|
# actor
|
|
|
|
self.K = self.w.dot(self.output)
|
|
|
|
# print(self.K)
|
|
|
|
|
|
def Update1(self,y_ref,yt_0,yt_1,yt_2,yt_3,V,Vprev):
|
|
|
|
# Update Params for next episode
|
|
|
|
|
|
# both
|
|
del_TD = 0.5 * ( y_ref - yt_0 )**2 + self.gamma*V - Vprev
|
|
|
|
|
|
# actor
|
|
|
|
# Update w matrix
|
|
self.w[0] = self.w[0] - self.aw * del_TD*(yt_1 - yt_2)*self.output.T
|
|
self.w[1] = self.w[1] + self.aw * del_TD*self.X[0,0]*self.output.T
|
|
self.w[2] = self.w[2] + self.aw * del_TD*(yt_1 - 2*yt_2 + yt_3)*self.output.T
|
|
|
|
|
|
|
|
def Update2(self,y_ref,yt_0,yt_1,yt_2,yt_3,V,Vprev,v_prev):
|
|
|
|
# Update Params for next episode
|
|
|
|
del_TD = 0.5 * ( y_ref - yt_0 )**2 + self.gamma*V - Vprev
|
|
|
|
for i in range(self.h):
|
|
self.wh[i,0] = self.wh[i,0] + self.au*del_TD*v_prev[0][i]*self.output[i]*( 1 - self.output[i] )*self.X[0]
|
|
self.wh[i,1] = self.wh[i,1] + self.au*del_TD*v_prev[0][i]*self.output[i]*( 1 - self.output[i] )*self.X[1]
|
|
self.wh[i,2] = self.wh[i,2] + self.au*del_TD*v_prev[0][i]*self.output[i]*( 1 - self.output[i] )*self.X[2]
|
|
|
|
|
|
|
|
|
|
|