Use of artificial intelligence in design of input shaper for motion control of mechanical systems

The cranes are usually controlled by a variety of actions including handling, lifting, carrying, then lowering and gripping the load. An intelligent control system could provide the operator with important advantages when performing such operations. In this study, it is shown how to perform an effective transport operation under variable conditions with hybrid control methods on a simple pendulum. Zero vibration input shaping method is implemented to a simple pendulum experiment set with different heights and variable weights. Single- Hidden-Layer Neural Network (NN), General Regression NN (GRNN), and Radial Basis Function Network (RBFN) are used in a serial configuration as an intelligent control method. Performance comparisons of these smart control methods are also made for different weight and height values other than the training data. Also, in order to operate such systems safely and economically, an effective automation system is needed too. In this study the results of automation system and mechanical damping methods have been presented. The mechanical damping method is necessary not only used for positioning a load with minimum oscillation but also for balancing it against wind forces. The experimental performances of these methods for different conditions have been compared with each other. In addition, current state-of-the-art industrial applications of automation and mechanical damping methods are also examined.