from scipy import log, sqrt, linspace, logspace, meshgrid, zeros from scipy.optimize import minimize def isstable(model): return 2*model[1]*model[2] + model[2]*model[2] - 2*model[0] def tflogmag((f,g,h), w): """ Return the log of the magnitude of the linearized transfer function log[ |T(jw)| ] """ #f,g,h = model.f, model.g, model.h return log((g*g*w*w + f*f)/((f - w*w)**2 + (g+h)**2*w*w))/2 def htflogmag((f,g,h), w): """ Return the log of the magnitude of the linearized headway transfer function log[ |1-T(jw)| ] """ #f,g,h = model.f, model.g, model.h return log((h*h*w*w + w*w*w*w)/((f - w*w)**2 + (g+h)**2*w*w))/2 def kstable(human, robot, w, eta=2): """ Return the maximum number of human cars that can be string-stabilized by a single robot car for an oscillatory perturbation of frequency w """ return -tflogmag(robot,w)/tflogmag(human,w) def ksafe(human, robot, w, eta=2): """ Return the maximum number of human cars that can be "safely" followed by a single robot car for an oscillatory perturbation of frequency w by """ return (log(eta)-htflogmag(robot,w)) / tflogmag(human,w) TOL = 1e-6 def maxkstable(human, robot, eta=2): f,g,h = human maxw = sqrt(2*f - 2*g*h - h*h) return minimize(lambda w : kstable(human, robot, w), 1, bounds=[(TOL,maxw-TOL)])#.fun[0] def maxksafe(human, robot, eta=2): f,g,h = human maxw = sqrt(2*f - 2*g*h - h*h) return minimize(lambda w : ksafe(human, robot, w, eta), 1, bounds=[(TOL,maxw-TOL)])#.fun[0] def maxk(human, robot, eta): return min(maxkstable(human, robot), maxksafe(human, robot, eta)) ''' def bestrobotstable(human): cons = ({'type': 'ineq', 'fun': lambda x: 2*x[1]*x[2] + x[2]*x[2] - 2*x[0] },) return minimize(lambda robot : -maxkstable(human, robot), (0.1, 0, 0.1), bounds=[(0, 0.2), (0, 0), (0, 0.2)], constraints=cons) def bestrobotsafe(human, eta): cons = ({'type': 'ineq', 'fun': lambda x: 2*x[1]*x[2] + x[2]*x[2] - 2*x[0] },) return minimize(lambda robot : -maxksafe(human, robot, eta), (0.1, 0, 0.1), bounds=[(0, 0.2), (0, 0), (0, 0.2)], constraints=cons) ''' def bestrobot(human, eta): #cons = ({'type': 'ineq', 'fun': lambda x: 2*x[1]*x[2] + x[2]*x[2] - 2*x[0] },) return minimize(lambda robot : -maxk(human, robot, eta).fun[0], [0.001, 0, 0.001], #constraints=cons, bounds=[(0, 0.2), (0, 0), (0, 0.2)]) if __name__ == "__main__": from mpl_toolkits.mplot3d import Axes3D from matplotlib import pyplot as plt from cfm import IDM, CFM hmodel = IDM(a=0.3,b=3,t=1.5,s0=2,v0=30) hmodel.go(0.5) human = (hmodel.f, hmodel.g, hmodel.h) maxw = sqrt(2*hmodel.f - 2*hmodel.g*hmodel.h - hmodel.h*hmodel.h) # print "maxw = ", maxw resw = sqrt(-isstable(human)) # print "resw = ", resw robot = (0.018, 0, 0.19) # print 2*robot[0] - 2*robot[1]*robot[2] - robot[2]*robot[2] eta=2 print bestrobot(human, eta) import IPython IPython.embed() ''' ws = linspace(TOL, maxw-TOL, 101) kst = kstable(human, robot, ws) ksf = ksafe(human, robot, ws, eta) # print "kstable = ", kstable(human, robot, ws) plt.plot(ws, kst) plt.plot(ws, ksf) plt.axis([0, maxw, 0, 100]) plt.show() frng = linspace(0, 0.1, 21) hrng = linspace(0, 0.1, 21) fs, hs = meshgrid(frng, hrng) ks = zeros(fs.shape) for fi, f in enumerate(frng): for hi, h in enumerate(hrng): ks[fi, hi] = maxk(human, (f, 0, h), eta) fig = plt.figure() ax = fig.add_subplot(111, projection='3d') ax.plot_surface(fs, hs, ks) plt.show() # print maxkstable(human, robot) # print maxksafe(human, robot, eta) print maxk(human, robot, eta) print print "***" print print bestrobotstable(human) print bestrobotsafe(human, eta) # print bestrobot(human, eta) '''