The use of Neural Network for Nonlinear Predictive Control design for and Overhead Crane

Abstract

The importance of nonlinear model predictive control (NMPC) implementations for industrial processes rises with the increasing of computational power in all hardware units used for regulation and control in practice. However, it assumes a sufficiently accurate model. In case of more complex systems, there might be problem to perform analytical identification. Instead of this, numerical approaches may be deployed with benefit. This paper deals with the design of NMPC for a nonlinear model of an overhead crane using a neural network and compares the solution with the one achieved with the use analytical model of the system. All steps of NMPC design and verification of functionality are performed in Matlab. The paper finally suggests possibility to extend the presented approach for hosting the NMPC algorithm on some real-time embedded target.