Sailboat navigation control system based on spiking neural networks

Nelson Santiago Giraldo, Sebastián Isaza, Ricardo Andrés Velásquez

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


In this paper, we presented the development of a navigation control system for a sailboat based on spiking neural networks (SNN). Our inspiration for this choice of network lies in their potential to achieve fast and low-energy computing on specialized hardware. To train our system, we use the modulated spike time-dependent plasticity reinforcement learning rule and a simulation environment based on the BindsNET library and USVSim simulator. Our objective was to develop a spiking neural network-based control systems that can learn policies allowing sailboats to navigate between two points by following a straight line or performing tacking and gybing strategies, depending on the sailing scenario conditions. We presented the mathematical definition of the problem, the operation scheme of the simulation environment, the spiking neural network controllers, and the control strategy used. As a result, we obtained 425 SNN-based controllers that completed the proposed navigation task, indicating that the simulation environment and the implemented control strategy work effectively. Finally, we compare the behavior of our best controller with other algorithms and present some possible strategies to improve its performance.

Translated title of the contributionSistema de control de navegación de veleros basado en redes neuronales spiking
Original languageEnglish (US)
Pages (from-to)489–504
Number of pages16
JournalControl Theory and Technology
StateE-pub ahead of print - Aug 29 2023

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Signal Processing
  • Information Systems
  • Modeling and Simulation
  • Aerospace Engineering
  • Control and Optimization
  • Electrical and Electronic Engineering


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