Spiking neural networks stabilize periodic motion in underactuated robots
Spiking Neuromorphic Control for Periodic Orbits Stabilization in Underactuated Robots: A Greedy Lyapunov-Based Approach
Robotics
Summary
Controlling robots with fewer motors than joints is tricky, especially when trying to keep them moving in a repeating pattern. The researchers study how to use spiking neurons—tiny circuits that mimic brain signals—to guide these robots smoothly along desired paths. By translating the robot’s complex movement into simpler steps, they design a controller that learns moment by moment how to keep the robot steady on its repeating track. They test this approach on a robot called the butterfly robot and show it can reliably follow its path.
What this means in practice
- •For robot control engineers: Use event-driven spiking neural controllers to maintain stable periodic motions in robots with limited motors.
- •For embedded systems developers: Implement efficient spiking neural network based controllers for real-time control of dynamic underactuated robotic systems.
Authors
Maksim Surov, Oleg Sumenkov
Abstract
This work investigates the use of event-based spiking controllers, implemented as spiking neural networks, for trajectory tracking in underactuated robots. Specifically, we address the problem of stabilizing a prescribed periodic orbit. It is well known that, by applying the method of transverse linearization to the robot dynamics in a neighborhood of a periodic trajectory, the dynamics can be approximately described by a linear time-varying system with periodic coefficients (a periodic LTV system). The problem of orbital stabilization can therefore be reduced to stabilizing the trivial solution of this LTV system. We show that a previously proposed Lyapunov-based greedy design, originally developed for LTI systems, can also be extended to the corresponding linearized transverse dynamics, which take the form of a periodic LTV system. This enables the construction of a feedback controller for orbital stabilization of the robot. The effectiveness of the proposed method is demonstrated on the trajectory stabilization problem for the butterfly robot.