Aug 7, 2021 · Jul 31, 2021 · Jul 31, 2021 · Jul 31, 2021 · Aug 1, 2021 · Aug 1, 2021
diff --git a/control/bdalg.py b/control/bdalg.py

    Parameters
    ----------
    sys1, sys2, ..., sysn: StateSpace orTransferfunction
    sys1, sys2, ..., sysn: StateSpace orTransferFunction
        LTI systems to combine



    Examples
    --------
    >>> sys1 = ss([[1., -2], [3., -4]], [[5.], [7]]", [[6., 8]], [[9.]])
    >>> sys1 = ss([[1., -2], [3., -4]], [[5.], [7]], [[6., 8]], [[9.]])
    >>> sys2 = ss([[-1.]], [[1.]], [[1.]], [[0.]])
    >>> sys = append(sys1, sys2)


    Parameters
    ----------
    sys : StateSpaceTransferfunction
    sys : StateSpaceor TransferFunction
        System to be connected
    Q : 2D array
        Interconnection matrix. First column gives the input to be connected.
diff --git a/control/iosys.py b/control/iosys.py
                 name=None, **kwargs):
        """Create an input/output system.

        The InputOutputSystemcontructor is used to create an input/output
        The InputOutputSystemconstructor is used to create an input/output
        object with the core information required for all input/output
        systems.  Instances of this class are normally created by one of the
        input/output subclasses: :class:`~control.LinearICSystem`,
 class LinearIOSystem(InputOutputSystem, StateSpace):
    """Input/output representation of a linear (state space) system.

    This class is used toimplementat a system that is a linear state
    This class is used toimplement a system that is a linear state
    space system (defined by the StateSpace system object).

    Parameters
              return_y=False, return_result=False, **kw):
    """Find the equilibrium point for an input/output system.

    Returns the value of anequlibrium point given the initial state and
    Returns the value of anequilibrium point given the initial state and
    either input value or desired output value for the equilibrium point.

    Parameters

    This function computes the linearization of an input/output system at a
    given state and input value and returns a :class:`~control.StateSpace`
    object.  Theeavaluation point need not be an equilibrium point.
    object.  Theevaluation point need not be an equilibrium point.

    Parameters
    ----------
    sys : InputOutputSystem
        The system to be linearized
    xeq : array
        The state at which the linearization will be evaluated (does not need
        to be anequlibrium state).
        to be anequilibrium state).
    ueq : array
        The input at which the linearization will be evaluated (does not need
        to correspond to an equlibrium state).
        ('sys', 'sig') are also recognized.

        Similarly, each output-spec should describe an output signal from one
        of thesusystems.  The lowest level representation is a tuple of the
        of thesubsystems.  The lowest level representation is a tuple of the
        form `(subsys_i, out_j, gain)`.  The input will be constructed by
        summing the listed outputs after multiplying by the gain term.  If the
        gain term is omitted, it is assumed to be 1.  If the system has a
diff --git a/control/optimal.py b/control/optimal.py
    """Description of a finite horizon, optimal control problem.

    The `OptimalControlProblem` class holds all of the information required to
    specifyand optimal control problem: the system dynamics, cost function,
    specifyan optimal control problem: the system dynamics, cost function,
    and constraints.  As much as possible, the information used to specify an
    optimal control problem matches the notation and terminology of the SciPy
    `optimize.minimize` module, with the hope that this makes it easier to
    The `_cost_function` method takes the information computes the cost of the
    trajectory generated by the proposed input.  It does this by calling a
    user-defined function for the integral_cost given the current states and
    inputs at each point along thetrajetory and then adding the value of a
    inputs at each point along thetrajectory and then adding the value of a
    user-defined terminal cost at the final pint in the trajectory.

    The `_constraint_function` method evaluates the constraint functions along
    the trajectory generated by the proposed input.  As in the case of the
    cost function, the constraints are evaluated at the state and input along
    each point on thetrjectory.  This information is compared against the
    each point on thetrajectory.  This information is compared against the
    constraint upper and lower bounds.  The constraint function is processed
    in the class initializer, so that it only needs to be computed once.

    #
    # Initially guesses from the user are passed as input vectors as a
    # function of time, but internally we store the guess in terms of the
    # basis coefficients.  We do this by solving a least squaresprobelm to
    # basis coefficients.  We do this by solving a least squaresproblem to
    # find coefficients that match the input functions at the time points (as
    # much as possible, if the problem is under-determined).
    #
        Function that returns the terminal cost given the current state
        and input.  Called as terminal_cost(x, u).

 terminal_constraint : list of tuples, optional
 terminal_constraints : list of tuples, optional
        List of constraints that should hold at the end of the trajectory.
        Same format as `constraints`.

    res : OptimalControlResult
        Bundle object with the results of the optimal control problem.

    res.success: bool
    res.success: bool
        Boolean flag indicating whether the optimization was successful.

    res.time : array
        Function that returns the terminal cost given the current state
        and input.  Called as terminal_cost(x, u).

 terminal_constraint : list of tuples, optional
 terminal_constraints : list of tuples, optional
        List of constraints that should hold at the end of the trajectory.
        Same format as `constraints`.

    Returns
    -------
    ctrl : InputOutputSystem
        An I/O system taking thecurrrent state of the model system and
        An I/O system taking thecurrent state of the model system and
        returning the current input to be applied that minimizes the cost
        function while satisfying the constraints.

    R : 2D array_like
        Weighting matrix for input cost.  Dimensions must match system input.
    x0 : 1D array
 Nomimal value of the system state (for which cost should be zero).
 Nominal value of the system state (for which cost should be zero).
    u0 : 1D array
 Nomimal value of the system input (for which cost should be zero).
 Nominal value of the system input (for which cost should be zero).

    Returns
    -------
 # As in the cost function evaluation, the main "trick" in creating a constrain
 # on the state or input is to properly evaluate the constraint on the stacked
 # state and input vector at the current time point.  The constraint itself
 # will be called at eachpoing along the trajectory (or the endpoint) via the
 # will be called at eachpoint along the trajectory (or the endpoint) via the
 # constrain_function() method.
 #
 # Note that these functions to not actually evaluate the constraint, they
 def output_poly_constraint(sys, A, b):
    """Create output constraint from polytope

    Creates a linear constraint on the systemouput of the form A y <= b that
    Creates a linear constraint on the systemoutput of the form A y <= b that
    can be used as an optimal control constraint (trajectory or terminal).

    Parameters
diff --git a/control/statefbk.py b/control/statefbk.py

    .. math:: x_e = A x_e + B u + L(y - C x_e - D u)

    produces a state estimatethatx_e that minimizes the expected squared
 errorusing the sensor measurements y. The noise cross-correlation `NN`
 isset to zero when omitted.
    produces a state estimate x_e that minimizes the expected squared error
    using the sensor measurements y. The noise cross-correlation `NN` is
    set to zero when omitted.

    Parameters
    ----------
    if type not in ['c', 'o', 'cf', 'of']:
        raise ValueError("That type is not supported!")

    # TODO: Check forcontinous or discrete, only continuous supported for now
    # TODO: Check forcontinuous or discrete, only continuous supported for now
        # if isCont():
        #    dico = 'C'
        # elif isDisc():
Original file line number	Diff line number	Diff line change
Expand Up		@@ -263,7 +263,7 @@ def append(*sys):

		Parameters
		----------
		sys1, sys2, ..., sysn: StateSpace orTransferfunction
		sys1, sys2, ..., sysn: StateSpace orTransferFunction
		LTI systems to combine


Expand All		@@ -275,7 +275,7 @@ def append(*sys):

		Examples
		--------
		>>> sys1 = ss([[1., -2], [3., -4]], [[5.], [7]]", [[6., 8]], [[9.]])
		>>> sys1 = ss([[1., -2], [3., -4]], [[5.], [7]], [[6., 8]], [[9.]])
		>>> sys2 = ss([[-1.]], [[1.]], [[1.]], [[0.]])
		>>> sys = append(sys1, sys2)

Expand All		@@ -299,7 +299,7 @@ def connect(sys, Q, inputv, outputv):

		Parameters
		----------
		sys : StateSpaceTransferfunction
		sys : StateSpaceor TransferFunction
		System to be connected
		Q : 2D array
		Interconnection matrix. First column gives the input to be connected.
Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -131,7 +131,7 @@ def __init__(self, inputs=None, outputs=None, states=None, params={},
		name=None, **kwargs):
		"""Create an input/output system.

		The InputOutputSystemcontructor is used to create an input/output
		The InputOutputSystemconstructor is used to create an input/output
		object with the core information required for all input/output
		systems. Instances of this class are normally created by one of the
		input/output subclasses: :class:`~control.LinearICSystem`,
Expand DownExpand Up		@@ -661,7 +661,7 @@ def copy(self, newname=None):
		class LinearIOSystem(InputOutputSystem, StateSpace):
		"""Input/output representation of a linear (state space) system.

		This class is used toimplementat a system that is a linear state
		This class is used toimplement a system that is a linear state
		space system (defined by the StateSpace system object).

		Parameters
Expand DownExpand Up		@@ -1675,7 +1675,7 @@ def find_eqpt(sys, x0, u0=[], y0=None, t=0, params={},
		return_y=False, return_result=False, **kw):
		"""Find the equilibrium point for an input/output system.

		Returns the value of anequlibrium point given the initial state and
		Returns the value of anequilibrium point given the initial state and
		either input value or desired output value for the equilibrium point.

		Parameters
Expand DownExpand Up		@@ -1926,15 +1926,15 @@ def linearize(sys, xeq, ueq=[], t=0, params={}, **kw):

		This function computes the linearization of an input/output system at a
		given state and input value and returns a :class:`~control.StateSpace`
		object. Theeavaluation point need not be an equilibrium point.
		object. Theevaluation point need not be an equilibrium point.

		Parameters
		----------
		sys : InputOutputSystem
		The system to be linearized
		xeq : array
		The state at which the linearization will be evaluated (does not need
		to be anequlibrium state).
		to be anequilibrium state).
		ueq : array
		The input at which the linearization will be evaluated (does not need
		to correspond to an equlibrium state).
Expand DownExpand Up		@@ -2055,7 +2055,7 @@ def interconnect(syslist, connections=None, inplist=[], outlist=[],
		('sys', 'sig') are also recognized.

		Similarly, each output-spec should describe an output signal from one
		of thesusystems. The lowest level representation is a tuple of the
		of thesubsystems. The lowest level representation is a tuple of the
		form `(subsys_i, out_j, gain)`. The input will be constructed by
		summing the listed outputs after multiplying by the gain term. If the
		gain term is omitted, it is assumed to be 1. If the system has a
Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -25,7 +25,7 @@ class OptimalControlProblem():
		"""Description of a finite horizon, optimal control problem.

		The `OptimalControlProblem` class holds all of the information required to
		specifyand optimal control problem: the system dynamics, cost function,
		specifyan optimal control problem: the system dynamics, cost function,
		and constraints. As much as possible, the information used to specify an
		optimal control problem matches the notation and terminology of the SciPy
		`optimize.minimize` module, with the hope that this makes it easier to
Expand DownExpand Up		@@ -94,13 +94,13 @@ class OptimalControlProblem():
		The `_cost_function` method takes the information computes the cost of the
		trajectory generated by the proposed input. It does this by calling a
		user-defined function for the integral_cost given the current states and
		inputs at each point along thetrajetory and then adding the value of a
		inputs at each point along thetrajectory and then adding the value of a
		user-defined terminal cost at the final pint in the trajectory.

		The `_constraint_function` method evaluates the constraint functions along
		the trajectory generated by the proposed input. As in the case of the
		cost function, the constraints are evaluated at the state and input along
		each point on thetrjectory. This information is compared against the
		each point on thetrajectory. This information is compared against the
		constraint upper and lower bounds. The constraint function is processed
		in the class initializer, so that it only needs to be computed once.

Expand DownExpand Up		@@ -567,7 +567,7 @@ def _process_initial_guess(self, initial_guess):
		#
		# Initially guesses from the user are passed as input vectors as a
		# function of time, but internally we store the guess in terms of the
		# basis coefficients. We do this by solving a least squaresprobelm to
		# basis coefficients. We do this by solving a least squaresproblem to
		# find coefficients that match the input functions at the time points (as
		# much as possible, if the problem is under-determined).
		#
Expand DownExpand Up		@@ -880,7 +880,7 @@ def solve_ocp(
		Function that returns the terminal cost given the current state
		and input. Called as terminal_cost(x, u).

		terminal_constraint : list of tuples, optional
		terminal_constraints : list of tuples, optional
		List of constraints that should hold at the end of the trajectory.
		Same format as `constraints`.

Expand DownExpand Up		@@ -914,7 +914,7 @@ def solve_ocp(
		res : OptimalControlResult
		Bundle object with the results of the optimal control problem.

		res.success: bool
		res.success: bool
		Boolean flag indicating whether the optimization was successful.

		res.time : array
Expand DownExpand Up		@@ -982,7 +982,7 @@ def create_mpc_iosystem(
		Function that returns the terminal cost given the current state
		and input. Called as terminal_cost(x, u).

		terminal_constraint : list of tuples, optional
		terminal_constraints : list of tuples, optional
		List of constraints that should hold at the end of the trajectory.
		Same format as `constraints`.

Expand All		@@ -992,7 +992,7 @@ def create_mpc_iosystem(
		Returns
		-------
		ctrl : InputOutputSystem
		An I/O system taking thecurrrent state of the model system and
		An I/O system taking thecurrent state of the model system and
		returning the current input to be applied that minimizes the cost
		function while satisfying the constraints.

Expand DownExpand Up		@@ -1039,9 +1039,9 @@ def quadratic_cost(sys, Q, R, x0=0, u0=0):
		R : 2D array_like
		Weighting matrix for input cost. Dimensions must match system input.
		x0 : 1D array
		Nomimal value of the system state (for which cost should be zero).
		Nominal value of the system state (for which cost should be zero).
		u0 : 1D array
		Nomimal value of the system input (for which cost should be zero).
		Nominal value of the system input (for which cost should be zero).

		Returns
		-------
Expand DownExpand Up		@@ -1082,7 +1082,7 @@ def quadratic_cost(sys, Q, R, x0=0, u0=0):
		# As in the cost function evaluation, the main "trick" in creating a constrain
		# on the state or input is to properly evaluate the constraint on the stacked
		# state and input vector at the current time point. The constraint itself
		# will be called at eachpoing along the trajectory (or the endpoint) via the
		# will be called at eachpoint along the trajectory (or the endpoint) via the
		# constrain_function() method.
		#
		# Note that these functions to not actually evaluate the constraint, they
Expand DownExpand Up		@@ -1250,7 +1250,7 @@ def input_range_constraint(sys, lb, ub):
		def output_poly_constraint(sys, A, b):
		"""Create output constraint from polytope

		Creates a linear constraint on the systemouput of the form A y <= b that
		Creates a linear constraint on the systemoutput of the form A y <= b that
		can be used as an optimal control constraint (trajectory or terminal).

		Parameters
Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -277,9 +277,9 @@ def lqe(A, G, C, QN, RN, NN=None):

		.. math:: x_e = A x_e + B u + L(y - C x_e - D u)

		produces a state estimatethatx_e that minimizes the expected squared
		errorusing the sensor measurements y. The noise cross-correlation `NN`
		isset to zero when omitted.
		produces a state estimate x_e that minimizes the expected squared error
		using the sensor measurements y. The noise cross-correlation `NN` is
		set to zero when omitted.

		Parameters
		----------
Expand DownExpand Up		@@ -617,7 +617,7 @@ def gram(sys, type):
		if type not in ['c', 'o', 'cf', 'of']:
		raise ValueError("That type is not supported!")

		# TODO: Check forcontinous or discrete, only continuous supported for now
		# TODO: Check forcontinuous or discrete, only continuous supported for now
		# if isCont():
		# dico = 'C'
		# elif isDisc():
Expand Down