Platoon Control System

A comprehensive framework for simulating and implementing Model Predictive Control (MPC) strategies for vehicle platooning, featuring ROS 2 integration and Python simulations.

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Repository Structure

platoon-control/
├── WK/                                         # ROS 2 simulation package
│
├── simulation.py                               # Python simulation for string stability without MPC
│
├── MPC/                                        # MPC implementation core
│   ├── MPCPkg/                                 # Compiled MATLAB MPC package
│   ├── TMPC.m                                  # Traditional MPC controller function
│   ├── LMPC.m                                  # Laguerre in MPC controller function
│   └── casadi-3.7.0/                           # Optimization library
│   └── sample.py                               # Python simulation for string stability using MPC
│
│── Dynamic Programming Decision Making.ipynb   # DP based decision making for merging and splitting
│             
└── README.md                                   # This document

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Key Features

• ROS 2 Integration: Full-scale Gazebo simulation with articulated vehicle models
• Predictive Control: MPC implementation using CasADi for real-time optimization
• Multi-Scale Testing: From 3-vehicle Python simulations to 10+ vehicle ROS scenarios

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Prerequisites

ROS 2 (Humble Hawksbill)

sudo apt install ros-humble-moveit ros-humble-depth-image-proc

Python

pip install numpy matplotlib casadi control

MATLAB (R2021a or later)

Required Toolboxes:

• Control System Toolbox
• MATLAB Compiler SDK

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Installation

Clone the Repository

git clone https://github.com/yourusername/platoon-control.git
cd platoon-control

ROS Setup

cd WK/
rosdep install --from-paths . --ignore-src -r -y
colcon build

Python Setup

cd ../simulation
pip install -r requirements.txt

MATLAB Setup

In MATLAB:

cd MPC/
addpath(genpath('casadi-3.7.0-windows64-matlab2018b')) % Update this path as per your OS and MATLAB version

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Usage

ROS Simulation (10+ Vehicles)

cd WK/
ros2 launch platoon_sim.launch.py vehicle_count:=10

Python Simulation (3 Vehicles)

cd simulation/
python platoon_sim.py --taus 0.51 0.6 0.55 --pos 200 195 175 --vel 22.2 20 25

MPC Controller

MATLAB

y = TMPC(4, [0.51, 0.6, 0.55, 0.49], [22.2, 20, 25, 21]);

Python

from platoon_mpc import TMPC
control_signals = TMPC(4, [0.51, 0.6, 0.55, 0.49], [22.2, 20, 25, 21])

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Configuration Guide

Parameter	File Location	Description
Vehicle Dynamics	MPC/TMPC.m (Lines 25–40)	τ values, state-space models
MPC Weights	MPC/TMPC.m (Lines 115–120)	Q/R matrices for cost function
Platoon Spacing	WK/config/platoon.yaml	Inter-vehicle distance policies

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Related Projects

• ROS 2 Robotic Manipulation Template
• CasADi Optimization Framework
• MATLAB-Python Integration Guide

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References

1. Liang, C.-Y., and Peng, H., “String Stability Analysis of Adaptive Cruise Controlled Vehicles,” Journal of Dynamic Systems, Measurement, and Control, vol. 122, no. 3, pp. 576–586, Sep. 2000. [Online].

2. Y. Zheng, S. E. Li, J. Wang, K. Li, and S. E. Shladover, “A Cooperative Driving Strategy for Merging at On-Ramps Based on Dynamic Programming,” IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 1, pp. 172–184, Jan. 2020. [Online].

3. J. Li, B. Yang, and F. Wang, “PeerVote: A Decentralized Voting Mechanism for P2P Collaboration Systems,” in Proc. 10th IEEE Int. Conf. on Computer Communications and Networks (ICCCN), Scottsdale, AZ, USA, 2001, pp. 277–282. doi:10.1109/ICCCN.2001.956282

4. L. Guo, P. Ge, D. Sun, and Y. Qiao, “Adaptive Cruise Control Based on Model Predictive Control with Constraints Softening,” Applied Sciences, vol. 10, no. 5, pp. 1–16, Feb. 2020. doi:10.3390/app10051635

5. Elsevier, “Laguerre Function – An Overview,” ScienceDirect Topics, [Online]. Available: https://www.sciencedirect.com/topics/mathematics/laguerre-function