  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.
