Welcome to the MPC-ModelPredictiveControl repository! This repository contains an example implementation of a Model Predicted Control (MPC) algorithm designed to showcase how MPC can be used for controlling dynamic systems. MPC is a process control method that uses a model to predict future outputs and applies an optimization algorithm at each step to find the optimal control action.
- Detailed MPC Implementation: Includes a step-by-step implementation of the MPC algorithm, demonstrating its predictive and optimization capabilities.
- Customizable Parameters: Allows users to easily adjust the model parameters, control horizon, and other MPC settings.
- Visualization Tools: Features scripts for visualizing the system's behavior and the control actions taken by the MPC.
- Simulation Examples: Comes with simulation examples to showcase the algorithm's application in different scenarios.
Before you begin, ensure you have Python 3.6 or newer installed on your system. This project also requires the following Python libraries: numpy, matplotlib, scipy, and pandas.
To get started with the MPC-ModelPredictiveControl project, follow these steps:
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Clone the repository to your local machine:
git clone https://github.com/yourusername/MPC-ModelPredictiveControl.git
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Navigate to the project directory:
cd MPC-ModelPredictiveControl -
Create a new conda environment and install the required dependencies:
conda env create -f environment.yml
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Activate the conda environment
conda activate mpc
To run the simulation examples, execute the following command:
python <simulation_name>.py or <simulation_name>.ipynbThis will launch a simulation that demonstrates the MPC algorithm controlling a specified system. Results, including plots of the system's response and control inputs, will be displayed upon completion.
The core of this project is the MPC algorithm, which operates by solving an optimization problem at each time step to determine the optimal control actions. The algorithm considers the current state of the system, predicts future states based on a given model, and computes the control actions that minimize a cost function subject to constraints.
Contributions to the MPC-ModelPredictiveControl project are welcome! If you have suggestions for improvements or encounter any issues, please feel free to open an issue or submit a pull request.
This project is licensed under the MIT License - see the LICENSE.md file for details.
This README provides a solid foundation for your repository, guiding users through installation, usage, and contribution. Feel free to adjust the content to fit the specifics of your project better.