Spiking Neural Network implementation of LQR control on underactuated system

Abstract

Adaptability, learning capabilities, and space-energy efficient hardware are required in robotic architectures, which must deal with changing dynamic environments. Nowadays, learning algorithms are implemented in Von Neumann Architectures, which separate storage from processing units, making them not appropriate for artificial neural networks (ANN), resulting in inefficient implementations. This writing presents a neural architecture proposal designed to implement a control loop in a mobile wheeled under-actuated inverted pendulum system, using spiking neural networks, linear quadratic regulator control technique, and a neural framework that allows us to define the neuron ensembles specification to represent specific control signals. The intention is to study how typical control theory algorithms can be translated into neural structures, aiming for neuromorphic implementation.