Real-time time-optimal control for a nonlinear container crane using a neural network

This paper considers time-optimal control for a container crane based on a Model Predictive Control approach. The model we use is nonlinear and it is planar, i.e. we only consider the swing (not the skew) and we take constraints on the input signal into consideration. Since the time required for the optimization makes time-optimal not suitable for fast systems and/or complex systems, such as the crane system we consider, we propose an off-line computation of the control law by using a neural network. After the neural network has been trained off-line, it can then be used in an on-line mode as a feedback control strategy.