From 8d5ea22f735e1c03a638b00815376a14c96e58c2 Mon Sep 17 00:00:00 2001 From: Robert Lefringhausen Date: Thu, 23 May 2024 14:49:53 +0200 Subject: [PATCH] Fixed invalid escape sequence in docstring. --- Julia/src/optimal_control_Ipopt.jl | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/Julia/src/optimal_control_Ipopt.jl b/Julia/src/optimal_control_Ipopt.jl index 2dd6624..1e971f5 100644 --- a/Julia/src/optimal_control_Ipopt.jl +++ b/Julia/src/optimal_control_Ipopt.jl @@ -23,8 +23,8 @@ h(&u_{0:H},x_{0:H}^{[k]},y_{0:H}^{[k]}) \\leq 0. - `R`: variance of zero-mean Gaussian measurement noise - only used if e_vec is not passed - `H`: horizon of the OCP - `J`: function with input arguments (``u_{1:H}``, ``x_{1:H}``, ``y_{1:H}``) (or ``u_{1:H}`` if `J_u` is set true) that returns the cost to be minimized -- `h_scenario`: function with input arguments (``u_{1:H}``, ``x_{1:H}``, ``y_{1:H}``) that returns the constraint vector belonging to a scenario; a feasible solution must satisfy ``h_{\mathrm{scenario}} \leq 0`` for all scenarios. -- `h_u`: function with input argument ``u_{1:H}`` that returns the constraint vector for the control inputs; a feasible solution satisfy yield ``h_u \leq 0`` +- `h_scenario`: function with input arguments (``u_{1:H}``, ``x_{1:H}``, ``y_{1:H}``) that returns the constraint vector belonging to a scenario; a feasible solution must satisfy ``h_{\\mathrm{scenario}} \\leq 0`` for all scenarios. +- `h_u`: function with input argument ``u_{1:H}`` that returns the constraint vector for the control inputs; a feasible solution satisfy yield ``h_u \\leq 0`` - `J_u`: set to true if cost depends only on inputs ``u_{1:H}` - this accelerates the optimization - `x_vec_0`: vector with K * n_x elements containing the initial state of all models - if not provided, the initial states are sampled based on the PGS samples - `v_vec`: array of dimension n_x x H x K that contains the process noise for all models and all timesteps - if not provided, the noise is sampled based on the PGS samples @@ -244,8 +244,8 @@ h(&u_{0:H},x_{0:H}^{[k]},y_{0:H}^{[k]}) \\leq 0. - `R`: variance of zero-mean Gaussian measurement noise - only used if e_vec is not passed - `H`: horizon of the OCP - `J`: function with input arguments (``u_{1:H}``, ``x_{1:H}``, ``y_{1:H}``) (or ``u_{1:H}`` if `J_u` is set true) that returns the cost to be minimized -- `h_scenario`: function with input arguments (``u_{1:H}``, ``x_{1:H}``, ``y_{1:H}``) that returns the constraint vector belonging to a scenario; a feasible solution must satisfy ``h_{\mathrm{scenario}} \leq 0`` for all scenarios. -- `h_u`: function with input argument ``u_{1:H}`` that returns the constraint vector for the control inputs; a feasible solution satisfy yield ``h_u \leq 0`` +- `h_scenario`: function with input arguments (``u_{1:H}``, ``x_{1:H}``, ``y_{1:H}``) that returns the constraint vector belonging to a scenario; a feasible solution must satisfy ``h_{\\mathrm{scenario}} \\leq 0`` for all scenarios. +- `h_u`: function with input argument ``u_{1:H}`` that returns the constraint vector for the control inputs; a feasible solution satisfy yield ``h_u \\leq 0`` - `β`: confidence parameter - `J_u`: set to true if cost depends only on inputs ``u_{1:H}` - this accelerates the optimization - `x_vec_0`: vector with K * n_x elements containing the initial state of all models - if not provided, the initial states are sampled based on the PGS samples