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Continuous adaptive nonlinear model predictive control using spiking neural networks and real-time learning

This repository contains the source code and figures of our paper.

Environment Model

Paper Details

Title: Continuous adaptive nonlinear model predictive control using spiking neural networks and real-time learning

Abstract: Model predictive control (MPC) is a prominent control paradigm providing accurate state prediction and subsequent control actions for intricate dynamical systems with applications ranging from autonomous driving to star tracking. However, there is an apparent discrepancy between the model’s mathematical description and its behavior in real-world conditions, affecting its performance in real-time. In this work, we propose a novel neuromorphic (brain-inspired) spiking neural network for continuous adaptive non-linear MPC. Utilizing real-time learning, our design significantly reduces dynamic error and augments model accuracy, while simultaneously addressing unforeseen situations. We evaluated our framework using real-world scenarios in autonomous driving, implemented in a physics-driven simulation. We tested our design with various vehicles (from a Tesla Model 3 to an Ambulance) experiencing malfunctioning and swift steering scenarios. We demonstrate significant improvements in dynamic error rate compared with traditional MPC implementation with up to 89.15% median prediction error reduction with 5 spiking neurons and up to 96.08% with 5000 neurons. Our results may pave the way for novel applications in real-time control and stimulate further studies in the adaptive control realm with spiking neural networks.

Authors: Raz Halaly, Elishai Ezra Tsur

Keywords: autonomous driving, neuromorphic control, spiking neural networks (SNN), model predictive control (MPC), neural engineering framework (NEF), energy efficiency, motion planning, computational frameworks

Publication: IOPScience Neuromorphic Computing and Engineering (Link)

Table of Contents

  1. Installation
  2. Usage
  3. Data
  4. Citation
  5. License

Installation

Follow these steps to set up and run the project:

Prerequisites

Step 1: Install CARLA Simulator

Download and install CARLA Simulator 0.9.14 by following the instructions provided in their official documentation. If you are using Windows, make sure to install the Visual C++ Redistributable 17 (2022) as well.

Step 2: : Install Python dependencies

Navigate to the src directory of the cloned repository:

cd ./src

Install the required Python packages using pip:

pip install -r requirements.txt

NOTE: It is highly recommended to use virtual environment for installing the Python dependencies. For more information, see Python Virtual Environments Documentation.

Usage

  1. Start the CARLA Simulator by following the instructions provided in their official documentation.

  2. Navigate to the src directory of the cloned repository:

    cd ./src

  3. Run the run_experiment.py script:

    python run_experiment.py

    The script accepts several command line arguments:

    run_experiment.py [-h|--help] 
                  [--host HOST] 
                  [--port PORT] 
                  [--car CAR]
                  [--map MAP] 
                  [--waypoints WAYPOINTS]
                  [--predict-dt PREDICT_DT]
                  [--predict-horizon PREDICT_HORIZON]
                  [--synapse SYNAPSE] 
                  [--look-ahead LOOK_AHEAD]
                  [--road-degree ROAD_DEGREE]
                  [--learning-rate LEARNING_RATE]
                  [--predictor-neurons PREDICTOR_NEURONS]
                  [--steering-malfunction STEERING_MALFUNCTION]
                  [--simulation-time SIMULATION_TIME]
                  [--waypoints-resolution WAYPOINTS_RESOLUTION]
                  [--swift SWIFT]
                  [--vehicle-wheelbase]

    Where the arguments are:

    Argument Default Value Description
    -h, --help Show the help message and exit.
    --host HOST localhost IP address of the CARLA Simulator host.
    --port PORT 2000 TCP port of the CARLA Simulator host.
    --car CAR vehicle.tesla.model3 The vehicle model to use. See CARLA Catalogue for a list of available models.
    --map MAP Town04 The map to use.
    --waypoints WAYPOINTS 1,369,55,320,323,0 The waypoints to use as a list of spawn points ids.
    --predict-dt PREDICT_DT 0.1 The prediction time step.
    --predict-horizon PREDICT_HORIZON 5 The prediction horizon.
    --synapse SYNAPSE 0.05 The synapse time constant.
    --look-ahead LOOK_AHEAD 60 The look ahead distance in meters.
    --road-degree ROAD_DEGREE 3 The degree of polynomial to fit to road.
    --learning-rate LEARNING_RATE 1e-4 The learning rate.
    --predictor-neurons PREDICTOR_NEURONS 1000 The number of neurons in adaptive predictor.
    --steering-malfunction STEERING_MALFUNCTION 0.25 The steering malfunction.
    --simulation-time SIMULATION_TIME 60 The simulation time in seconds.
    --waypoints-resolution WAYPOINTS_RESOLUTION 1 The waypoints resolution in meters.
    --swift SWIFT 0 The swift malfunction direction every n seconds. 0 or negative to disable.
    --vehicle-wheelbase 2.3 The vehicle wheelbase in meters.

    The script is configured to run the experiment Experiment II from the paper. To run Experiment I use:

    python run_experiment.py --steering-malfunction 0.0 --car <vehicle-blueprint>

    The arguments for Experiment III are:

    python run_experiment.py --map Town06 --waypoints 263,7,55,314 --simulation-time 40 --swift 10 --car <vehicle-blueprint>

The vehicle wheelbase can be set using the --vehicle-wheelbase argument. We used the following values for the experiments:

Vehicle Wheelbase
Ford Ambulance 4.55
Mini Cooper S 2.467
Tesla Cybertruck 3.807
Mitsubishi Fusorosa 3.995
Tesla Model 3 2.875
Ford Mustang 2.7432
Volkswagen T2 (2021) 2.4

Data

The recorded data from the experiments can be found in the this OneDrive folder. The data is organized in the following structure:

├───Experiment I
│   ├───...
├───Experiment II
│   ├───...
├───Experiment III
│   ├───...
├───waypoints_town04.csv
└───waypoints_town06.csv

Each folder contains the logs from the experiment in CSV format. The CSV files names are in the following format:

state_log_<prediction-horizon>_<prediction-time-step>_<car-model>_[norm|steermal=<value>]_[bicycle|adaptive_<neurons>_<synapse>_<learning_rate>]

Each CSV file contains the following columns:

Column Description
px The x position of the vehicle in meters.
py The y position of the vehicle in meters.
yaw_cos The cosine of the yaw angle of the vehicle.
yaw_sin The sine of the yaw angle of the vehicle.
vx The x velocity of the vehicle in meters per second.
vy The y velocity of the vehicle in meters per second.
ax (Adaptive only) The x acceleration of the vehicle in meters per second squared.
ay (Adaptive only) The y acceleration of the vehicle in meters per second squared.
vr (Adaptive only) The yaw rate of the vehicle in radians per second.
dx_err The prediction error in the x position of the vehicle in meters.
dy_err The prediction error in the y position of the vehicle in meters.
dyaw_err The prediction error in the yaw angle of the vehicle.
dvx_err The prediction error in the x velocity of the vehicle in meters per second.
dvy_err The prediction error in the y velocity of the vehicle in meters per second.
dax_err (Adaptive only) The prediction error in the x acceleration of the vehicle in meters per second squared.
day_err (Adaptive only) The prediction error in the y acceleration of the vehicle in meters per second squared.
dvr_err (Adaptive only) The prediction error in the yaw rate of the vehicle in radians per second.
predict_cost The prediction cost.

The waypoints_town04.csv and waypoints_town06.csv files contain the waypoints used in the experiments.

Citation

If you use our work in your research, please cite it using the following BibTeX entry:

@article{10.1088/2634-4386/ad4209,
	author={Halaly, Raz and Ezra Tsur, Elishai},
	title={Continuous adaptive nonlinear model predictive control using spiking neural networks and real-time learning},
	journal={Neuromorphic Computing and Engineering },
	url={http://iopscience.iop.org/article/10.1088/2634-4386/ad4209},
	year={2024},
	abstract={Model Predictive Control (MPC) is a prominent control paradigm providing accurate state prediction and subsequent control actions for intricate dynamical systems with applications ranging from autonomous driving to star tracking. However, there is an apparent discrepancy between the model’s mathematical description and its behavior in real-world conditions, affecting its performance in real-time. In this work, we propose a novel neuromorphic spiking neural network for continuous adaptive non-linear MPC. By using real-time learning, our design significantly reduces dynamic error and augments model accuracy, while simultaneously addressing unforeseen situations. We evaluated our framework using real-world scenarios in autonomous driving, implemented in a physics-driven simulation. We tested our design with various vehicles (from a Tesla Model 3 to an Ambulance) experiencing malfunctioning and swift steering scenarios. We demonstrate significant improvements in dynamic error rate compared with traditional MPC implementation with up to 89.87% median prediction error reduction with 5 spiking neurons and up to 96.95% with 5000 neurons. Our results may pave the way for novel applications in real-time control and stimulate further studies in the adaptive control realm with spiking neural networks.}
}

License

MIT License