# pvtol-nested.py - inner/outer design for vectored thrust aircraft
# RMM, 5 Sep 09
#
# This file works through a fairly complicated control design and
# analysis, corresponding to the planar vertical takeoff and landing
# (PVTOL) aircraft in Astrom and Mruray, Chapter 11.  It is intended
# to demonstrate the basic functionality of the python-control
# package.
#

import os
import matplotlib.pyplot as plt  # MATLAB plotting functions
import numpy as np
import math
import control as ct

# System parameters
m = 4           # mass of aircraft
J = 0.0475      # inertia around pitch axis
r = 0.25        # distance to center of force
g = 9.8         # gravitational constant
c = 0.05        # damping factor (estimated)

# Transfer functions for dynamics
Pi = ct.tf([r], [J, 0, 0])  # inner loop (roll)
Po = ct.tf([1], [m, c, 0])  # outer loop (position)

# Use state space versions
Pi = ct.tf2ss(Pi)
Po = ct.tf2ss(Po)

#
# Inner loop control design
#
# This is the controller for the pitch dynamics.  Goal is to have
# fast response for the pitch dynamics so that we can use this as a 
# control for the lateral dynamics
#

# Design a simple lead controller for the system
k, a, b = 200, 2, 50
Ci = k*ct.tf([1, a], [1, b])  # lead compensator

# Convert to statespace
Ci = ct.tf2ss(Ci)

# Compute the loop transfer function for the inner loop
Li = Pi*Ci


# Bode plot for the open loop process
plt.figure(1)
ct.bode(Pi)

# Bode plot for the loop transfer function, with margins
plt.figure(2)
ct.bode(Li)

# Compute out the gain and phase margins
#! Not implemented
# (gm, pm, wcg, wcp) = margin(Li);

# Compute the sensitivity and complementary sensitivity functions
Si = ct.feedback(1, Li)
Ti = Li*Si

# Check to make sure that the specification is met
plt.figure(3)
ct.gangof4(Pi, Ci)

# Compute out the actual transfer function from u1 to v1 (see L8.2 notes)
# Hi = Ci*(1-m*g*Pi)/(1+Ci*Pi);
Hi = ct.parallel(ct.feedback(Ci, Pi), -m*g*ct.feedback(Ci*Pi, 1))

plt.figure(4)
plt.clf()
ct.bode(Hi)

# Now design the lateral control system
a, b, K = 0.02, 5, 2
Co = -K*ct.tf([1, 0.3], [1, 10])  # another lead compensator

# Convert to statespace
Co = ct.tf2ss(Co)

# Compute the loop transfer function for the outer loop
Lo = -m*g*Po*Co

plt.figure(5)
ct.bode(Lo, display_margins=True)  # margin(Lo)

# Finally compute the real outer-loop loop gain + responses
L = Co*Hi*Po
S = ct.feedback(1, L)
T = ct.feedback(L, 1)

# Compute stability margins
#! Not yet implemented
# (gm, pm, wgc, wpc) = margin(L); 

plt.figure(6)
plt.clf()
out = ct.bode(L, np.logspace(-4, 3), initial_phase=-math.pi/2)
axs = ct.get_plot_axes(out)

# Add crossover line to magnitude plot
axs[0, 0].semilogx([1e-4, 1e3], 20*np.log10([1, 1]), 'k-')

#
# Nyquist plot for complete design
#
plt.figure(7)
ct.nyquist(L)

# set up the color
color = 'b'

# Add arrows to the plot
# H1 = L.evalfr(0.4); H2 = L.evalfr(0.41);
# arrow([real(H1), imag(H1)], [real(H2), imag(H2)], AM_normal_arrowsize, \
#  'EdgeColor', color, 'FaceColor', color);

# H1 = freqresp(L, 0.35); H2 = freqresp(L, 0.36);
# arrow([real(H2), -imag(H2)], [real(H1), -imag(H1)], AM_normal_arrowsize, \
#  'EdgeColor', color, 'FaceColor', color);

plt.figure(9)
Yvec, Tvec = ct.step_response(T, np.linspace(1, 20))
plt.plot(Tvec.T, Yvec.T)

Yvec, Tvec = ct.step_response(Co*S, np.linspace(1, 20))
plt.plot(Tvec.T, Yvec.T)

#TODO: PZmap for statespace systems has not yet been implemented.
# plt.figure(10)
# plt.clf()
# P, Z = pzmap(T, Plot=True)
# print("Closed loop poles and zeros: ", P, Z)
# plt.suptitle("This figure intentionally blank")

# Gang of Four
plt.figure(11)
plt.clf()
ct.gangof4(Hi*Po, Co, np.linspace(-2, 3))

if 'PYCONTROL_TEST_EXAMPLES' not in os.environ:
    plt.show()