A novel neural adaptive controller for robots

Existing industrial robotic manipulators have proven to be limited in many applications, e.g. both their payload capability and manipulation speeds are limited. This paper presents a novel neural adaptive controller-intelligent gain scheduling-(IGS) for robotic manipulators. It advances the idea of mapping the nonlinear relationship between robot working conditions (e.g. payload, speed, etc.) and its controller gains. This scheme is simple, inexpensive, and especially, attractive for its possible implementation in real-time. Simulation has shown promising results.