control.optimal.create_mpc_iosystem

control.optimal.create_mpc_iosystem(sys, timepts, integral_cost=None, trajectory_constraints=None, terminal_cost=None, terminal_constraints=None, log=False, **kwargs)

Create a model predictive I/O control system.

This function creates an input/output system that implements a model predictive control for a system given the time points, cost function and constraints that define the finite-horizon optimization that should be carried out at each state.

Parameters

sysInputOutputSystem

I/O system for which the optimal input will be computed.

timepts1D array_like

List of times at which the optimal input should be computed.

integral_cost (or cost)callable

Function that returns the integral cost given the current state and input. Called as integral_cost(x, u).

trajectory_constraints (or constraints)list of tuples, optional

List of constraints that should hold at each point in the time vector. See solve_optimal_trajectory for more details.

terminal_costcallable, optional

Function that returns the terminal cost given the final state and input. Called as terminal_cost(x, u).

terminal_constraintslist of tuples, optional

List of constraints that should hold at the end of the trajectory. Same format as constraints.

**kwargs

Additional parameters, passed to scipy.optimize.minimize and NonlinearIOSystem.

Returns

ctrlInputOutputSystem

An I/O system taking the current state of the model system and returning the current input to be applied that minimizes the cost function while satisfying the constraints.

Other Parameters

inputs, outputs, statesint or list of str, optional

Set the names of the inputs, outputs, and states, as described in InputOutputSystem.

logbool, optional

If True, turn on logging messages (using Python logging module). Use logging.basicConfig to enable logging output (e.g., to a file).

namestring, optional

System name (used for specifying signals). If unspecified, a generic name ‘sys[id]’ is generated with a unique integer id.

Notes

Additional keyword parameters can be used to fine-tune the behavior of the underlying optimization and integration functions. See OptimalControlProblem for more information.