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In recent years, Artificial Neural Networks (ANN) have become a standard inrobotic control. However, a significant drawback of large-scale ANNs is theirincreased power consumption. This becomes a critical concern when designingautonomous aerial vehicles, given the stringent constraints on power andweight. 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 asynchronousevent-driven processing. In this paper, we have evolved SNNs for accuratealtitude control of a non-neutrally buoyant indoor blimp, relying solely ononboard sensing and processing power. The blimp's altitude tracking performancesignificantly improved compared to prior research, showing reduced oscillationsand a minimal steady-state error. The parameters of the SNNs were optimized viaan evolutionary algorithm, using a Proportional-Derivative-Integral (PID)controller as the target signal. We developed two complementary SNN controllerswhile examining various hidden layer structures. The first controller respondsswiftly to control errors, mitigating overshooting and oscillations, while thesecond minimizes steady-state errors due to non-neutral buoyancy-induced drift.Despite the blimp's drivetrain limitations, our SNN controllers ensured stablealtitude control, employing only 160 spiking neurons.

Evolving Spiking Neural Networks to Mimic PID Control for Autonomous Blimps