Application of neuro-genetic techniques in solving Industrial Crane kinematic control problem

This work presents a solution to solve industrial cranes kinematic control problem also called automatic travel control (ATC) [10]. Aspects such as optimal trajectory reference calculation considering: process cycle time and distance travelled minimization, improvements in mechanical transmission systems useful life, prohibited areas and obstacles in the crane workspace, etc., and load position control with close tracking of trajectory reference avoiding excessive load swinging angles too, are analyzed and tackled applying intelligent control techniques based on genetic algorithms and neural networks. The use of numerical and Hardware in the Loop (HiL) simulations, together with rapid prototyping advanced tools make quick changes and fast iterations between conceptual, preliminary, detailed, prototyping and validation design stages possible, allowing to reduce embedded control system development time and also increasing industrial crane overall quality.