neuromancer.psl.nonautonomous module

Non-autonomous dynamic systems. Reference for Chaotic nonlinear ODEs

class neuromancer.psl.nonautonomous.Actuator(exclude_norms=['Time'], backend='numpy', requires_grad=False, seed: int | Generator = 59, set_stats=True)[source]

Bases: ODE_NonAutonomous

These are the actuator dynamics from the IVER systems. Since the equations are linear they are a good sanity check for your modeling implementations.

equations(t, x, u)[source]

  • States (8): [delta_u, delta_q, delta_r]

  • Inputs (3): [delta_uc, delta_qc, delta_rc] (thrust speed/deflections, normalized)

get_U(nsim, signal=None, **signal_kwargs)[source]

For sampling a sequence of control actions :param nsim: length of sequence :return: Matrix nsim X nU

property params
class neuromancer.psl.nonautonomous.CSTR(exclude_norms=['Time'], backend='numpy', requires_grad=False, seed: int | Generator = 59, set_stats=True)[source]

Bases: ODE_NonAutonomous

Continuous Stirred Tank Reactor model Original code obtained from APMonitor

equations(t, x, u)[source]
Inputs (1):

  • Temperature of cooling jacket (K)

Disturbances (2):

  • Tf = Feed Temperature (K)

  • Caf = Feed Concentration (mol/m^3)

States (2):

  • Concentration of A in CSTR (mol/m^3)

  • Temperature in CSTR (K)

get_U(nsim, signal=None, **signal_kwargs)[source]

For sampling a sequence of control actions :param nsim: length of sequence :return: Matrix nsim X nU

get_x0(rand=False)[source]

Randomly sample an initial condition

Parameters:

box – Dictionary with keys ‘min’ and ‘max’ and values np.arrays with shape=(nx,)

property params
class neuromancer.psl.nonautonomous.DuffingControl(exclude_norms=['Time'], backend='numpy', requires_grad=False, seed: int | Generator = 59, set_stats=True)[source]

Bases: ODE_NonAutonomous

Duffing equation with driving force as a function of control inputs not time Source

equations(t, x, u)[source]
get_U(nsim, signal=None, **signal_kwargs)[source]

For sampling a sequence of control actions :param nsim: length of sequence :return: Matrix nsim X nU

property params
class neuromancer.psl.nonautonomous.HindmarshRose(exclude_norms=['Time'], backend='numpy', requires_grad=False, seed: int | Generator = 59, set_stats=True)[source]

Bases: ODE_NonAutonomous

Hindmarsh–Rose model of neuronal activity

equations(t, x, u)[source]
get_U(nsim, signal=None, **signal_kwargs)[source]

For sampling a sequence of control actions :param nsim: length of sequence :return: Matrix nsim X nU

property params
class neuromancer.psl.nonautonomous.InvPendulum(exclude_norms=['Time'], backend='numpy', requires_grad=False, seed: int | Generator = 59, set_stats=True)[source]

Bases: ODE_NonAutonomous

Inverted Pendulum dynamics * states: \(x = [ heta \dot{ heta}]\);

  • :math:` heta` is angle from upright equilibrium

  • input: u = input torque

equations(t, x, u)[source]
get_U(nsim, signal=None, **signal_kwargs)[source]

For sampling a sequence of control actions :param nsim: length of sequence :return: Matrix nsim X nU

property params
class neuromancer.psl.nonautonomous.IverSimple(exclude_norms=['Time'], backend='numpy', requires_grad=False, seed: int | Generator = 59, set_stats=True)[source]

Bases: ODE_NonAutonomous

Dynamic model of Unmanned Underwater Vehicle (modified from Stankiewicz et al) – Excludes rolling, sway, currents, Includes: hydrostate/dynamic terms, control surface deflections/propeller thrust, and actuator dynamics with non-kinematic output

equations(t, x, u)[source]

  • States (8): [theta, psi, uu, q, r, delta_u, delta_q, delta_r]

  • Inputs (3): [delta_uc, delta_qc, delta_rc] (thrust speed/deflections, normalized)

get_U(nsim, signal=None, **signal_kwargs)[source]

For sampling a sequence of control actions :param nsim: length of sequence :return: Matrix nsim X nU

property params
class neuromancer.psl.nonautonomous.LorenzControl(exclude_norms=['Time'], backend='numpy', requires_grad=False, seed: int | Generator = 59, set_stats=True)[source]

Bases: ODE_NonAutonomous

equations(t, x, u)[source]
get_U(nsim, signal=None, **signal_kwargs)[source]

For sampling a sequence of control actions :param nsim: length of sequence :return: Matrix nsim X nU

property params
u_fun(t)[source]
class neuromancer.psl.nonautonomous.SEIR_population(exclude_norms=['Time'], backend='numpy', requires_grad=False, seed: int | Generator = 59, set_stats=True)[source]

Bases: ODE_NonAutonomous

Susceptible, Exposed, Infected, and Recovered (SEIR) population population model. Used to model COVID-19 spread. Source of the model

states:

  • Susceptible (s): population fraction that is susceptible to the virus

  • Exposed (e): population fraction is infected with the virus but does not transmit to others

  • Infectious (i): population fraction that is infected and can infect others

  • Recovered (r): population fraction recovered from infection and is immune from further infection

equations(t, x, u)[source]

  • Inputs (1): social distancing (u=0 (none), u=1 (total isolation))

  • States (4):

    • Susceptible (s): population fraction that is susceptible to the virus

    • Exposed (e): population fraction is infected with the virus but does not transmit to others

    • Infectious (i): population fraction that is infected and can infect others

    • Recovered (r): population fraction recovered from infection and is immune from further infection

get_U(nsim, signal=None, **signal_kwargs)[source]

For sampling a sequence of control actions :param nsim: length of sequence :return: Matrix nsim X nU

property params
class neuromancer.psl.nonautonomous.SwingEquation(exclude_norms=['Time'], backend='numpy', requires_grad=False, seed: int | Generator = 59, set_stats=True)[source]

Bases: ODE_NonAutonomous

Power Grid Swing Equation. The second-order swing equation is converted to two first-order ODEs

equations(t, x, u)[source]
get_U(nsim, signal=None, **signal_kwargs)[source]

For sampling a sequence of control actions :param nsim: length of sequence :return: Matrix nsim X nU

property params
class neuromancer.psl.nonautonomous.Tank(exclude_norms=['Time'], backend='numpy', requires_grad=False, seed: int | Generator = 59, set_stats=True)[source]

Bases: ODE_NonAutonomous

Single Tank model Original code obtained from APMonitor

equations(t, x, u)[source]

  • States (1): level in the tanks

  • Inputs u(1): c - valve coefficient (kg/s / %open)

  • Inputs u(2): valve in % [0-100]

get_U(nsim, signal=None, **signal_kwargs)[source]

For sampling a sequence of control actions :param nsim: length of sequence :return: Matrix nsim X nU

get_x0()[source]

Randomly sample an initial condition

Parameters:

box – Dictionary with keys ‘min’ and ‘max’ and values np.arrays with shape=(nx,)

property params
class neuromancer.psl.nonautonomous.ThomasAttractorControl(exclude_norms=['Time'], backend='numpy', requires_grad=False, seed: int | Generator = 59, set_stats=True)[source]

Bases: ODE_NonAutonomous

Thomas’ cyclically symmetric attractor control input: dissipativity parameter b Source

equations(t, x, u)[source]
get_U(nsim, signal=None, **signal_kwargs)[source]

For sampling a sequence of control actions :param nsim: length of sequence :return: Matrix nsim X nU

property params
class neuromancer.psl.nonautonomous.TwoTank(exclude_norms=['Time'], backend='numpy', requires_grad=False, seed: int | Generator = 59, set_stats=True)[source]

Bases: ODE_NonAutonomous

Two Tank model. Original code obtained from APMonitor

equations(t, x, u)[source]
get_U(nsim, signal=None, **signal_kwargs)[source]

For sampling a sequence of control actions :param nsim: length of sequence :return: Matrix nsim X nU

get_x0()[source]

Randomly sample an initial condition

Parameters:

box – Dictionary with keys ‘min’ and ‘max’ and values np.arrays with shape=(nx,)

property params
property umax

Note that although the theoretical upper bound is 1.0, this results in numerical instability in the integration.

property umin
class neuromancer.psl.nonautonomous.VanDerPolControl(exclude_norms=['Time'], backend='numpy', requires_grad=False, seed: int | Generator = 59, set_stats=True)[source]

Bases: ODE_NonAutonomous

Van der Pol oscillator

equations(t, x, u)[source]
get_U(nsim, signal=None, **signal_kwargs)[source]

For sampling a sequence of control actions :param nsim: length of sequence :return: Matrix nsim X nU

property params