Evolving Spiking Neural Networks to Mimic PID Control for Autonomous Blimps

TL;DR

This paper presents evolved spiking neural networks optimized via an evolutionary algorithm to control a blimp's altitude efficiently, achieving improved stability and reduced oscillations compared to previous methods, with minimal neuron count.

Contribution

It introduces a novel approach of evolving SNNs for altitude control of blimps, using a PID target, demonstrating enhanced performance with low neuron count.

Findings

Improved altitude tracking with reduced oscillations.

Stable control despite drivetrain limitations.

Effective SNN controllers with only 160 neurons.

Abstract

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.