How to use the control.matlab function in control

[0., 1., 0., 0.]])
    B = np.array([[1./tau, 0.],
                  [0., 1./tau],
                  [0., 0.],
                  [0., 0.]])

    C = np.eye(4)
    D = np.zeros((4, 2))

    # make simulator with coninuous matrix
    init_xs = np.array([0., 0., 0., 0.])
    plant_cvxopt = FirstOrderSystem(A, B, C, init_states=init_xs)
    plant_scipy = FirstOrderSystem(A, B, C, init_states=init_xs)

    # create system
    sysc = matlab.ss(A, B, C, D)
    # discrete system
    sysd = matlab.c2d(sysc, dt)

    Ad = sysd.A
    Bd = sysd.B

    # evaluation function weight
    Q = np.diag([1., 1., 10., 10.])
    R = np.diag([0.01, 0.01])
    pre_step = 5

    # make controller with discreted matrix
    # please check the solver, if you want to use the scipy, set the MpcController_scipy
    controller_cvxopt = MpcController_cvxopt(Ad, Bd, Q, R, pre_step,
                               dt_input_upper=np.array([0.25 * dt, 0.25 * dt]), dt_input_lower=np.array([-0.5 * dt, -0.5 * dt]),
                               input_upper=np.array([1. ,3.]), input_lower=np.array([-1., -3.]))

def lsim(sys, U = 0.0, T = None, X0 = 0.0):
		U_ = U
		if isinstance(U_, (np.ndarray, list)):
			U_ = U_.T
		return cnt.lsim(sys, U_, T, X0)

8., 0., 0., 0.; \
0., 4., 0., 0.; \
0., 0., 1., 0.')
B = np.matrix('2.; 0.; 0.; 0.')
C = np.matrix('0.5, 0.6875, 0.7031, 0.5')
D = np.matrix('0.')

# The full system
fsys = StateSpace(A,B,C,D)
# The reduced system, truncating the order by 1
ord = 3
rsys = msimp.balred(fsys,ord, method = 'truncate')

# Comparison of the step responses of the full and reduced systems
plt.figure(1)
y, t = mt.step(fsys)
yr, tr = mt.step(rsys)
plt.plot(t.T, y.T)
plt.hold(True)
plt.plot(tr.T, yr.T)

# Repeat balanced reduction, now with 100-dimensional random state space
sysrand = mt.rss(100, 1, 1)
rsysrand = msimp.balred(sysrand,10,method ='truncate')

# Comparison of the impulse responses of the full and reduced random systems
plt.figure(2)
trand, yrand = mt.impulse(sysrand)
trandr, yrandr = mt.impulse(rsysrand)
plt.plot(trand.T, yrand.T,trandr.T, yrandr.T)

# lineared car system
    V = 5.0
    A = np.array([[0., V], [0., 0.]])
    B = np.array([[0.], [1.]])

    C = np.eye(2)
    D = np.zeros((2, 1))

    # make simulator with coninuous matrix
    init_xs_lead = np.array([5., 0., 0.])
    init_xs_follow = np.array([0., 0., 0.])
    lead_car = TwoWheeledSystem(init_states=init_xs_lead)
    follow_car = TwoWheeledSystem(init_states=init_xs_follow)

    # create system
    sysc = matlab.ss(A, B, C, D)
    # discrete system
    sysd = matlab.c2d(sysc, dt)

    Ad = sysd.A
    Bd = sysd.B

    # evaluation function weight
    Q = np.diag([1., 1.])
    R = np.diag([5.])
    pre_step = 15

    # make controller with discreted matrix
    # please check the solver, if you want to use the scipy, set the MpcController_scipy
    lead_controller = MpcController_cvxopt(Ad, Bd, Q, R, pre_step,
                               dt_input_upper=np.array([30 * dt]), dt_input_lower=np.array([-30 * dt]),
                               input_upper=np.array([30.]), input_lower=np.array([-30.]))

self.f_bode.clf()
        plt.figure(self.f_bode.number)
        control.matlab.bode(self.sys, logspace(-2, 2))
        plt.suptitle('Bode Diagram')

        self.f_nyquist.clf()
        plt.figure(self.f_nyquist.number)
        control.matlab.nyquist(self.sys, logspace(-2, 2))
        plt.suptitle('Nyquist Diagram')

        self.f_step.clf()
        plt.figure(self.f_step.number)
        try:
            # Step seems to get intro trouble
            # with purely imaginary poles
            tvec, yvec = control.matlab.step(self.sys)
            plt.plot(tvec.T, yvec)
        except:
            print("Error plotting step response")
        plt.suptitle('Step Response')

        self.canvas_pzmap.draw()
        self.canvas_bode.draw()
        self.canvas_step.draw()
        self.canvas_nyquist.draw()

def redraw(self):
        """ Redraw all diagrams """
        self.draw_pz(self.sys)
        
        self.f_bode.clf()
        plt.figure(self.f_bode.number)
        control.matlab.bode(self.sys, logspace(-2, 2))
        plt.suptitle('Bode Diagram')

        self.f_nyquist.clf()
        plt.figure(self.f_nyquist.number)
        control.matlab.nyquist(self.sys, logspace(-2, 2))
        plt.suptitle('Nyquist Diagram')

        self.f_step.clf()
        plt.figure(self.f_step.number)
        try:
            # Step seems to get intro trouble
            # with purely imaginary poles
            tvec, yvec = control.matlab.step(self.sys)
            plt.plot(tvec.T, yvec)
        except:
            print("Error plotting step response")

def redraw(self):
        """ Redraw all diagrams """
        self.draw_pz(self.sys)
        
        self.f_bode.clf()
        plt.figure(self.f_bode.number)
        control.matlab.bode(self.sys, logspace(-2, 2))
        plt.suptitle('Bode Diagram')

        self.f_nyquist.clf()
        plt.figure(self.f_nyquist.number)
        control.matlab.nyquist(self.sys, logspace(-2, 2))
        plt.suptitle('Nyquist Diagram')

        self.f_step.clf()
        plt.figure(self.f_step.number)
        try:
            # Step seems to get intro trouble
            # with purely imaginary poles
            tvec, yvec = control.matlab.step(self.sys)
            plt.plot(tvec.T, yvec)
        except:
            print "Error plotting step response"
        plt.suptitle('Step Response')

        self.canvas_pzmap.show()
        self.canvas_bode.show()
        self.canvas_step.show()

NUM34 = [0.891, 0.223]
H3 = [1., 0.7, 0.485, 0.22, 0.]

na3 = 2
nb31 = 1
nb32 = 2
nb33 = 1
nb34 = 2
th31 = 0
th32 = 1
th33 = 0
th34 = 2
nc3 = 3

# transfer function G, H
g_sample11 = cnt.tf(NUM11, DEN1, ts)
g_sample12 = cnt.tf(NUM12, DEN1, ts)
g_sample13 = cnt.tf(NUM13, DEN1, ts)
g_sample14 = cnt.tf(NUM14, DEN1, ts)

g_sample22 = cnt.tf(NUM22, DEN2, ts)
g_sample21 = cnt.tf(NUM21, DEN2, ts)
g_sample23 = cnt.tf(NUM23, DEN2, ts)
g_sample24 = cnt.tf(NUM24, DEN2, ts)

g_sample31 = cnt.tf(NUM31, DEN3, ts)
g_sample32 = cnt.tf(NUM32, DEN3, ts)
g_sample33 = cnt.tf(NUM33, DEN3, ts)
g_sample34 = cnt.tf(NUM34, DEN3, ts)

H_sample1 = cnt.tf(H1, DEN1, ts)
H_sample2 = cnt.tf(H2, DEN2, ts)