commonpower.control.controllers.OptimalController

class OptimalController(name: str, solver: ~pyomo.opt.base.solvers.OptSolver = <pyomo.solvers.plugins.solvers.gurobi_direct.GurobiDirect object>, control_input_trajectory_length: int = 1, cost_fcn: ~commonpower.modeling.robust_cost.BaseRobustCost = <commonpower.modeling.robust_cost.NominalCost object>)

Bases: BaseController

Optimal controller that solves a constrained optimization problem to find the control inputs which minimize the cost function of all its controlled entities while satisfying their constraints.

Parameters:

• name (str) – name of the controller

• solver (OptSolver, optional) – solver for optimization problem

• control_input_trajectory_length (int, optional) – number of time steps the controller computes control inputs for

• cost_fcn (BaseRobustCost, optional) – Robust cost function. Defaults to NominalCost.

Returns:

OptimalController

Methods

act_array_to_dict	Converts numpy array of actions to dictionary.
add_entity	Add a controllable entity to the controller.
add_system	When adding a system to a controller, the system tree is searched recursively and all controllable entities that do not yet have a controller are added to 'nodes'.
clip_to_bounds	Clips the control inputs to their bounds to avoid numerical errors.
compute_control_input	Main functionality of the controller: computes the control inputs which minimize the objective function of the controlled entities while satisfying their constraints.
detach	Remove controller from all controlled entities
empty_copy	Create a fresh copy of the controller without any history.
filter_history_for_time_period	Filters all element histories for a given time period
flatten_obs	Converts observation dictionary to a numpy array.
get_cost	Compute control cost for one time step
get_id	Get ID of controller.
get_input_space	Derives action space of the controller from the list of its controlled entities.
get_nodes	Get controlled nodes.
get_objective_fcn	Constructs the objective function for the optimal control problem.
get_top_level_nodes	Retrieve the controlled entities at the highest level in the tree.
initialize	Initial set-up of controller.
reset_history	Delete history

Attributes

obs_mask

compute_control_input(obs: OrderedDict | None = None, input_callback: Callable | None = None) → Tuple[OrderedDict, float]

Main functionality of the controller: computes the control inputs which minimize the objective function of the controlled entities while satisfying their constraints.

Parameters:

• obs (OrderedDict) – not needed her

• input_callback (Callable) – not needed here

Returns:

Tuple –

tuple containing:

• action (OrderedDict)

• safety penalty (float) (not needed hear, only for RL controllers).

empty_copy()

Create a fresh copy of the controller without any history.

Returns:

OptimalController – cloned controller

get_objective_fcn() → Callable

Constructs the objective function for the optimal control problem.

Returns:

Callable – Pyomo expression of the objective function.

reset_history() → None

Delete history

Returns:

None