# Figure 3.2: Illustration of a state model

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Chapter | System Modeling |
---|---|

Figure number | 3.2 |

Figure title | Illustration of a state model |

GitHub URL | https://github.com/murrayrm/fbs2e-python/blob/main/figure-3.2-state model.py |

Requires | python-control |

**Figure 3.2**: Illustration of a state model. A state model gives the rate of change of the state as a function of the state. The plot on the left shows the evolution of the state as a function of time. The plot on the right, called a phase portrait, shows the evolution of the states relative to each other, with the velocity of the state denoted by arrows.

# figure-3.2-state_mode.py - illustration of a state model # RMM, 2 Jul 2021 # # Figure 3.2: Illustration of a state model. A state model gives the rate of # change of the state as a function of the state. The plot on the left shows # the evolution of the state as a function of time. The plot on the right, # called a phase portrait, shows the evolution of the states relative to # each other, with the velocity of the state denoted by arrows. # import numpy as np import scipy as sp import matplotlib.pyplot as plt import control as ct # # Spring mass system with nonlinear dampling # # This function gives the dynamics for a dampled oscillator with nonlinear # damping. The states of the system are # # x[0] position # x[1] velocity # # The nonlinear damping is implemented as a change in the linear damping # coefficient at a small velocity. This is intended to roughly correspond # to some sort of stiction (and give an interesting phase portrait). The # default parameters for the system are given by # # m = 1 mass, kg # k = 1 spring constant, N/m # b1 = 1 damping constant near origin, N-sec/m # b2 = 0.01 damping constant away from origin, N-sec/m # dth = 0.5 threshold for switching between damping # # This corresponds to a fairly lightly damped oscillator away from the origin. def nlspringmass(x, t, u=0, m=1, k=1, b1=2, b2=0.01, dth=0.2): # Compute the friction force if abs(x[1]) < dth: Fb = b1 * x[1]; elif x[1] < 0: Fb = -b1 * dth \ + b2 * (x[1] + dth); else: Fb = b1 * dth \ + b2 * (x[1] - dth); # Return the time derivative of the state return np.array([x[1], -k/m * x[0] - Fb/m]) # # (a) Simulation of the nonlinear spring mass system # plt.subplot(2, 2, 1) t = np.linspace(0, 16, 100) y = sp.integrate.odeint(nlspringmass, [2, 0], t) plt.plot(t, y[:, 0], '-', t, y[:, 1], '--') plt.xlabel('Time $t$ [s]') plt.ylabel('Position $q$ [m], velocity $\dot q$̇ [m/s]') plt.title('Time plot') plt.legend(['Position $q$', 'Velocity $v$']) # # (b) Generate a phase plot for the damped oscillator # plt.subplot(2, 2, 2) ct.phase_plot( nlspringmass, # dynamics (-1, 1, 8), (-1, 1, 8), # X, Y range, npoints scale=0.2, X0=[[-1, 0.4], [0.1, 1], [1, -0.4], [-0.1, -1]]) plt.xlabel('Position $q$ [m]') plt.ylabel('Velocity $\dot q$ [m/s]') plt.title('Phase portrait') plt.axis([-1, 1, -1, 1]) plt.tight_layout()