.. automodule:: control.iosys
   :no-members:
   :no-inherited-members:

An input/output system is defined as a dynamical system that has a system state as well as inputs and outputs (either inputs or states can be empty). The dynamics of the system can be in continuous or discrete time. To simulate an input/output system, use the :func:`~control.input_output_response` function:

t, y = input_output_response(io_sys, T, U, X0, params)

An input/output system can be linearized around an equilibrium point to obtain a :class:`~control.StateSpace` linear system. Use the :func:`~control.find_eqpt` function to obtain an equilibrium point and the :func:`~control.linearize` function to linearize about that equilibrium point:

xeq, ueq = find_eqpt(io_sys, X0, U0)
ss_sys = linearize(io_sys, xeq, ueq)

Input/output systems can be created from state space LTI systems by using the :class:`~control.LinearIOSystem` class`:

io_sys = LinearIOSystem(ss_sys)

Nonlinear input/output systems can be created using the :class:`~control.NonlinearIOSystem` class, which requires the definition of an update function (for the right hand side of the differential or different equation) and and output function (computes the outputs from the state):

io_sys = NonlinearIOSystem(updfcn, outfcn, inputs=M, outputs=P, states=N)

More complex input/output systems can be constructed by using the :class:`~control.InterconnectedSystem` class, which allows a collection of input/output subsystems to be combined with internal connections between the subsystems and a set of overall system inputs and outputs that link to the subsystems:

steering = ct.InterconnectedSystem(
    (plant, controller), name='system',
    connections=(('controller.e', '-plant.y')),
    inplist=('controller.e'), inputs='r',
    outlist=('plant.y'), outputs='y')

Interconnected systems can also be created using block diagram manipulations such as the :func:`~control.series`, :func:`~control.parallel`, and :func:`~control.feedback` functions. The :class:`~control.InputOutputSystem` class also supports various algebraic operations such as * (series interconnection) and + (parallel interconnection).

To illustrate the use of the input/output systems module, we create a model for a predator/prey system, following the notation and parameter values in FBS2e.

We begin by defining the dynamics of the system

import control
import numpy as np
import matplotlib.pyplot as plt

def predprey_rhs(t, x, u, params):
    # Parameter setup
    a = params.get('a', 3.2)
    b = params.get('b', 0.6)
    c = params.get('c', 50.)
    d = params.get('d', 0.56)
    k = params.get('k', 125)
    r = params.get('r', 1.6)

    # Map the states into local variable names
    H = x[0]
    L = x[1]

    # Compute the control action (only allow addition of food)
    u_0 = u if u > 0 else 0

    # Compute the discrete updates
    dH = (r + u_0) * H * (1 - H/k) - (a * H * L)/(c + H)
    dL = b * (a * H *  L)/(c + H) - d * L

    return [dH, dL]

We now create an input/output system using these dynamics:

io_predprey = control.NonlinearIOSystem(
    predprey_rhs, None, inputs=('u'), outputs=('H', 'L'),
    states=('H', 'L'), name='predprey')

Note that since we have not specified an output function, the entire state will be used as the output of the system.

The io_predprey system can now be simulated to obtain the open loop dynamics of the system:

X0 = [25, 20]                 # Initial H, L
T = np.linspace(0, 70, 500)   # Simulation 70 years of time

# Simulate the system
t, y = control.input_output_response(io_predprey, T, 0, X0)

# Plot the response
plt.figure(1)
plt.plot(t, y[0])
plt.plot(t, y[1])
plt.legend(['Hare', 'Lynx'])
plt.show(block=False)

We can also create a feedback controller to stabilize a desired population of the system. We begin by finding the (unstable) equilibrium point for the system and computing the linearization about that point.

eqpt = control.find_eqpt(io_predprey, X0, 0)
xeq = eqpt[0]                         # choose the nonzero equilibrium point
lin_predprey = control.linearize(io_predprey, xeq, 0)

We next compute a controller that stabilizes the equilibrium point using eigenvalue placement and computing the feedforward gain using the number of lynxes as the desired output (following FBS2e, Example 7.5):

K = control.place(lin_predprey.A, lin_predprey.B, [-0.1, -0.2])
A, B = lin_predprey.A, lin_predprey.B
C = np.array([[0, 1]])                # regulated output = number of lynxes
kf = -1/(C @ np.linalg.inv(A - B @ K) @ B)

To construct the control law, we build a simple input/output system that applies a corrective input based on deviations from the equilibrium point. This system has no dynamics, since it is a static (affine) map, and can constructed using the ~control.ios.NonlinearIOSystem class:

io_controller = control.NonlinearIOSystem(
  None,
  lambda t, x, u, params: -K @ (u[1:] - xeq) + kf * (u[0] - xeq[1]),
  inputs=('Ld', 'u1', 'u2'), outputs=1, name='control')

The input to the controller is u, consisting of the vector of hare and lynx populations followed by the desired lynx population.

To connect the controller to the predatory-prey model, we create an InterconnectedSystem:

io_closed = control.InterconnectedSystem(
  (io_predprey, io_controller),       # systems
  connections=(
    ('predprey.u', 'control.y[0]'),
    ('control.u1',  'predprey.H'),
    ('control.u2',  'predprey.L')
  ),
  inplist=('control.Ld'),
  outlist=('predprey.H', 'predprey.L', 'control.y[0]')
)

Finally, we simulate the closed loop system:

# Simulate the system
t, y = control.input_output_response(io_closed, T, 30, [15, 20])

# Plot the response
plt.figure(2)
plt.subplot(2, 1, 1)
plt.plot(t, y[0])
plt.plot(t, y[1])
plt.legend(['Hare', 'Lynx'])
plt.subplot(2, 1, 2)
plt.plot(t, y[2])
plt.legend(['input'])
plt.show(block=False)

.. autosummary::

   InputOutputSystem
   InterconnectedSystem
   LinearIOSystem
   NonlinearIOSystem

.. autosummary::

   find_eqpt
   linearize
   input_output_response
   ss2io
   tf2io