set_default_objective

Class method.

do_mpc.estimator.MHE.set_default_objective(self, P_x, P_v=None, P_p=None, P_w=None)

Configure the suggested default MHE formulation.

Use this method to pass tuning matrices for the MHE optimization problem:

\[\begin{split}\underset{ \begin{array}{c} \mathbf{x}_{0:N+1}, \mathbf{u}_{0:N}, p,\\ \mathbf{w}_{0:N}, \mathbf{v}_{0:N} \end{array} }{\mathrm{min}} &m(x_0,\tilde{x}_0, p,\tilde{p}) +\sum_{k=0}^{N-1} l(v_k, w_k, p, p_{\text{tv},k}),\\ &\left.\begin{aligned} \mathrm{s.t.}\quad x_{k+1} &= f(x_k,u_k,z_k,p,p_{\text{tv},k})+ w_k,\\ y_k &= h(x_k,u_k,z_k,p,p_{\text{tv},k}) + v_k, \\ &g(x_k,u_k,z_k,p_k,p_{\text{tv},k}) \leq 0 \end{aligned}\right\} k=0,\dots, N\end{split}\]

where we introduce the bold letter notation, e.g. \(\mathbf{x}_{0:N+1}=[x_0, x_1, \dots, x_{N+1}]^T\) to represent sequences and where \(\|x\|_P^2=x^T P x\) denotes the \(P\) weighted squared norm.

Pass the weighting matrices \(P_x\), \(P_p\) and \(P_v\) and \(P_w\). The matrices must be of appropriate dimension and array-like.

Note

It is possible to pass parameters or time-varying parameters defined in the do_mpc.model.Model as weighting. You’ll probably choose time-varying parameters (_tvp) for P_v and P_w and parameters (_p) for P_x and P_p. Use set_p_fun() and set_tvp_fun() to configure how these values are determined at each time step.

General remarks:

• In the case that no parameters are estimated, the weighting matrix \(P_p\) is not required.
• In the case that the do_mpc.model.Model is configured without process-noise (see do_mpc.model.Model.set_rhs()) the parameter P_w is not required.
• In the case that the do_mpc.model.Model is configured without measurement-noise (see do_mpc.model.Model.set_meas()) the parameter P_v is not required.

The respective terms are not present in the MHE formulation in that case.

Note

Use set_objective() as a low-level alternative for this method, if you want to use a custom objective function.

Parameters:

• P_x (numpy.ndarray, casadi.SX, casadi.DM) – Tuning matrix \(P_x\) of dimension \(n \times n\) \((x \in \mathbb{R}^{n})\)
• P_v (numpy.ndarray, casadi.SX, casadi.DM) – Tuning matrix \(P_v\) of dimension \(m \times m\) \((v \in \mathbb{R}^{m})\)
• P_p (numpy.ndarray, casadi.SX, casadi.DM) – Tuning matrix \(P_p\) of dimension \(l \times l\) \((p_{\text{est}} \in \mathbb{R}^{l})\))
• P_w (numpy.ndarray, casadi.SX, casadi.DM) – Tuning matrix \(P_w\) of dimension \(k \times k\) \((w \in \mathbb{R}^{k})\)

This page is auto-generated. Page source is not available on Github.