Model predictive control python toolbox - do-mpc
do-mpc is a comprehensive open-source Python toolbox for robust model predictive control (MPC) and moving horizon estimation (MHE).
Getting started: MPC — do-mpc 4.6.5 documentation
With the configured and setup model we can now create the optimizer for model predictive control (MPC). We start by creating the object (with the model as the only input)
Model predictive control python toolbox - do-mpc
do-mpc is a comprehensive open-source Python toolbox for robust model predictive control (MPC) and moving horizon estimation (MHE).
Basics of model predictive control — do-mpc 4.6.5 documentation
Model predictive control (MPC) is a control scheme where a model is used for predicting the future behavior of the system over finite time window, the horizon. Based on these predictions and the current measured/estimated state of the system, the optimal control inputs with respect to a defined control objective and subject to system ...
Installation — do-mpc 4.6.5 documentation
do-mpc is a python 3.x package. Follow this guide to install do-mpc. If you are new to Python, please read this article about Python environments. We recommend using a new Python environment for every project and to manage it with miniconda. Requirements# do-mpc requires the following Python packages and their dependencies: numpy. CasADi ...
Continuous stirred tank reactor (CSTR) — do-mpc 4.6.5 …
Model# In the following we will present the configuration, setup and connection between these blocks, starting with the model. The considered model of the CSTR is continuous and has 4 states and 2 control inputs. The model is initiated by:
Basics of moving horizon estimation — do-mpc 4.6.5 documentation
In many ways it can be seen as the counterpart to model predictive control (MPC), which we are describing in our MPC article. In comparison to more traditional state-estimation methods, e.g. the extended Kalman filter (EKF) , MHE will often outperform the former in …
do_mpc — do-mpc 4.6.5 documentation
The core modules are used to create the do-mpc control loop (click on elements to open documentation page):
Orthogonal collocation on finite elements - do-mpc
A dynamic system model is at the core of all model predictive control (MPC) and moving horizon estimation (MHE) formulations. This model allows to predict and optimize the future behavior of the system (MPC) or establishes the relationship between past …
MPC — do-mpc 4.6.5 documentation
For general information on model predictive control, please read our background article. The MPC controller extends the do_mpc.optimizer.Optimizer base class (which is also used for the do_mpc.estimator.MHE estimator).