# set_meas¶

Class method.

do_mpc.model.Model.set_meas(self, meas_name, expr, meas_noise=True)

Introduce new measurable output to the model class.

$y = h(x(t),u(t),z(t),p(t),p_{\text{tv}}(t)) + v(t)$

or in case of discrete dynamics:

$y_k = h(x_k,u_k,z_k,p_k,p_{\text{tv},k}) + v_k$

By default, the model assumes state-feedback (all states are measured outputs). Expressions must be formulated with respect to _x, _u, _z, _tvp, _p.

Be default, it is assumed that the measurements experience additive noise $$v_k$$. This can be deactivated for individual measured variables by changing the boolean variable meas_noise to False. Note that measurement noise is only meaningful for state-estimation and will not affect the controller. Furthermore, it can be set with each do_mpc.simulator.Simulator call to obtain imperfect outputs.

Note

For moving horizon estimation it is suggested to declare all inputs (_u) and e.g. a subset of states (_x) as measurable output. Some other MHE formulations treat inputs separately.

Note

It is often suggested to deactivate measurement noise for “measured” inputs (_u). These can typically seen as certain variables.

Example:

# Introduce states:
x_meas = model.set_variable('_x', 'x', 3) # 3 measured states (vector)
x_est = model.set_variable('_x', 'x', 3) # 3 estimated states (vector)
# and inputs:
u = model.set_variable('_u', 'u', 2) # 2 inputs (vector)

# define measurements:
model.set_meas('x_meas', x_meas)
model.set_meas('u', u)

Parameters: expr_name (string) – Arbitrary name for the given expression. Names are used for key word indexing. expr (CasADi SX or MX) – CasADi SX or MX function depending on _x, _u, _z, _tvp, _p. meas_noise (bool) – Set if the measurement equation is disturbed by additive noise. assertion – expr_name must be str assertion – expr must be a casadi SX or MX type assertion – Cannot call after setup(). Returns the newly created measurement expression. casadi.SX

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