Project Structure

Project Structure#

This section outlines the internal code structure of the project.

Direct Data-Driven MPC Controller#

The project is structured as a Python package, encapsulating the core logic of the Data-Driven MPC controllers within the following modules:

direct_data_driven_mpc.lti_data_driven_mpc_controller: Implements a Data-Driven MPC controller for Linear Time-Invariant (LTI) systems in the LTIDataDrivenMPCController class. This implementation is based on the Nominal and Robust Data-Driven MPC schemes described in [1].
direct_data_driven_mpc.nonlinear_data_driven_mpc_controller: Implements a Data-Driven MPC controller for nonlinear systems in the NonlinearDataDrivenMPCController class. This implementation is based on the Nonlinear Data-Driven MPC scheme described in [2].

The utility module direct_data_driven_mpc.utilities.hankel_matrix is used for constructing Hankel matrices and evaluating whether data sequences are persistently exciting of a given order.

Model Simulation#

The following utility modules have been implemented to simulate LTI and nonlinear systems:

direct_data_driven_mpc.utilities.models.lti_model: Implements the LTIModel and LTISystemModel classes for simulating LTI systems.
direct_data_driven_mpc.utilities.models.nonlinear_model: Implements the NonlinearSystem class for simulating nonlinear systems.

Controller Creation#

To modularize the creation of Data-Driven MPC controllers, the following utility modules are provided:

direct_data_driven_mpc.utilities.controller.controller_creation: Provides functions for creating Data-Driven MPC controller instances from specified configuration parameters for both LTI and nonlinear controllers.
direct_data_driven_mpc.utilities.controller.controller_params: Provides functions for loading Data-Driven MPC controller parameters from YAML configuration files for both LTI and nonlinear controllers.
direct_data_driven_mpc.utilities.controller.initial_data_generation: Provides functions for generating input-output data from LTI and nonlinear systems, which can be used as initial trajectories in Data-Driven MPC controller creation.

Data-Driven Controller Simulation#

The direct_data_driven_mpc.utilities.controller.data_driven_mpc_sim module implements the main control loops for both Data-Driven MPC controllers, following Algorithms 1 and 2 of [1] for LTI systems and Algorithm 1 of [2] for nonlinear systems.

Visualization (Static and Animated Plots)#

Custom functions are provided in direct_data_driven_mpc/utilities/visualization/ to display input-output data in static and animated plots. These functions use Matplotlib for visualization and FFmpeg for saving animations in various formats (e.g., GIF, MP4).

Examples#

The examples directory contains scripts that demonstrate the operation of the Data-Driven MPC controller and reproduce the results presented in the referenced papers.

examples/lti_control/lti_dd_mpc_example.py: Demonstrates the setup, simulation, and data visualization of a Data-Driven MPC controller applied to an LTI system.
examples/lti_control/robust_lti_dd_mpc_reproduction.py: Implements a reproduction of the example presented in [1], showing various Robust Data-Driven MPC schemes applied to an LTI system.
examples/nonlinear_control/nonlinear_dd_mpc_example.py: Demonstrates the setup, simulation, and data visualization of a Data-Driven MPC controller applied to a nonlinear system while closely following the example presented in [2].

Configuration Files#

The system and controller parameters used in the example scripts are defined in YAML configuration files in examples/config/. These parameters are based on the examples from Section V of [1] for LTI systems, and from Section V of [2] for nonlinear systems.

Data-Driven MPC controllers:
- examples/config/controllers/lti_dd_mpc_example_params.yaml: Defines parameters for a Data-Driven MPC controller designed for LTI systems.
- examples/config/controllers/nonlinear_dd_mpc_example_params.yaml: Defines parameters for a Data-Driven MPC controller designed for nonlinear systems.
Models:
- examples/config/models/four_tank_system_params.yaml: Defines system model parameters for a linearized version of a four-tank system.
- examples/config/models/nonlinear_cstr_system_params.yaml: Defines system model parameters for a nonlinear continuous stirred tank reactor (CSTR) system.
Plots:
- examples/config/plots/plot_params.yaml: Defines Matplotlib properties of lines, legends, and figures for input-output plots.

A YAML loading function is provided in direct_data_driven_mpc.utilities.yaml_config_loading.

References#

See the full citations of the reference papers in the Citation section.