Evolving Spiking Neural Networks to Mimic PID Control

Evolving Spiking Neural Networks to Mimic PID Control: Applied to Autonomous Blimps

Burgers, Tim (TU Delft Aerospace Engineering)

de Croon, G.C.H.E. (mentor)
Stroobants, S. (mentor)
de Wagter, C. (graduation committee)
Bombelli, A. (graduation committee)

Delft University of Technology

Aerospace Engineering

2023-10-06

In recent years, Artificial Neural Networks (ANN) have become a standard in robotic control. However, a significant drawback of large-scale ANNs is their increased power consumption. This becomes a critical concern when designing autonomous aerial vehicles, given the stringent constraints on power and weight. Especially in the case of blimps, known for their extended endurance, power-efficient control methods are essential. Spiking neural networks (SNN) can provide a solution, facilitating energy-efficient and asynchronous event-driven processing.
In this paper, we have evolved SNNs for accurate altitude control of a non-neutrally buoyant indoor blimp, relying solely on onboard sensing and processing power. The blimp's altitude tracking performance significantly improved compared to prior research, showing reduced oscillations and a minimal steady-state error. The parameters of the SNNs were optimized via an evolutionary algorithm, using a Proportional-Derivative-Integral (PID) controller as the target signal. We developed two complementary SNN controllers while examining various hidden layer structures. The first controller responds swiftly to control errors, mitigating overshooting and oscillations, while the second minimizes steady-state errors due to non-neutral buoyancy-induced drift. Despite the blimp's drivetrain limitations, our SNN controllers ensured stable altitude control, employing only 160 spiking neurons.

Neurorobotics
Machine Learning for Robot Control
Aerial Systems: Mechanics and Control
Spiking Neural Network (SNN)
Evolutionary Algorithm - EA
Blimp

http://resolver.tudelft.nl/uuid:1edec476-3b58-458d-a4a6-cbba30b783e6

Student theses

master thesis

© 2023 Tim Burgers