yaocptool

(YAOCPTool) Yet Another Optimal Control Tool

How to install

The package is not yet listed in PyPI (pip install), however it can be installed directly from GitHub using the following command:

For MASTER branch:

pip install https://github.com/marcoaaguiar/yaocptool/archive/master.zip

For DEV branch:

pip install https://github.com/marcoaaguiar/yaocptool/archive/dev.zip

If you are not installing using pip install, but cloning the repository you will need to install the following packages

pip install casadi numpy scipy sobol_seq matplotlib networkx

Documentation

The documentation can be accessed here! For PDF version click here!

How to use

The objective of this tool is to make easier to use the state-of-the-art CasADi, at the same time allowing for researchers to propose new methods.

Trust me, it is easier!

Creating a model

from yaocptool.modelling import SystemModel

model = SystemModel(name="simple_model")
x = model.create_state("x") # vector of state variables
u = model.create_control("u") # vector of control variables

# Include the dynamic equation
ode = [-x + u]
model.include_system_equations(ode=ode)

# Print model information
print(model)

Creating a Optimal Control Problem (OCP)

from yaocptool.modelling import OptimalControlProblem

problem = OptimalControlProblem(model, x_0=[1], t_f=10, obj={"Q": 1, "R": 1})

Creating a Solver for the OCP

from yaocptool.methods import DirectMethod
# Initialize a DirectMethod to solve the OCP using collocation
solution_method = DirectMethod(problem, finite_elements=20, discretization_scheme='collocation')

# Solve the problem and get the result
result = solution_method.solve()

# Make one plot with the element x[0] (the first state) and one plot with the control u[0]
result.plot([{'x':[0]}, {'u':[0]}])