Neuromorphic Control of Robotic Systems with Numerical Simulations

In this paper, we considered potential benefits of the neuromorphic control technique for solving specific challenges in robotic control. Developing a neuromorphic control system for a robot involves simulating the architecture and dynamics of biological neurons to perform control tasks. This differs from typical control techniques and frequently employs spiking neural networks (SNNs). SNNs are more closely related to our brains than conventional neural networks, as they incorporate temporal dynamics. Biological neurons transmit information using spikes. Neurons do not fire in each cycle, but rather when the membrane potential reaches a predetermined threshold, as in a binary system. When a neuron fires, it transmits a signal to the synapse. The control strategy presented in this paper is based on the Leaky Integrated-and-Fire (LIF) and Generalized Integrate-and-Fire (GIF) neuron models. We designed neuromorphic control systems and utilized three robotic systems as examples. Numerical simulations were used to demonstrate the stability, robustness, and effectiveness of the neuromorphic robot control system design.