58 lines
1.4 KiB
Markdown
58 lines
1.4 KiB
Markdown
# mpc_python
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Python implementation of a mpc controller for path tracking using **[CVXPY](https://www.cvxpy.org/)**.
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## About
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The MPC is a model predictive path following controller which does follow a predefined reference by solving an optimization problem. The resulting optimization problem is shown in the following equation:
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The terns of the cost function are the sum of the **cross-track error**, **heading error**, **velocity error** and **actuaction effort**.
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Where R,P,K,Q are the cost matrices used to tune the response.
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The vehicle model is described by the bicycle kinematics model using the state space matrices A and B:
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The state variables **(x)** of the model are:
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* **x** coordinate of the robot
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* **y** coordinate of the robot
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* **v** velocuty of the robot
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* **theta** heading of the robot
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The inputs **(u)** of the model are:
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* **a** linear acceleration of the robot
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* **delta** steering angle of the robot
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## Demo
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The MPC implementation is tested using **[bullet](https://pybullet.org/wordpress/)** physics simulator. Racing car model is from: *https://github.com/erwincoumans/pybullet_robots*.
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Results:
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To run the pybullet demo:
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```bash
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python3 mpc_demo/mpc_demo_pybullet.py
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```
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To run the simulation-less demo:
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```bash
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python3 mpc_demo/mpc_demo_pybullet.py
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```
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## Requirements
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```bash
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pip3 install --user --requirement requirements.txt
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```
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