This paper validates the combination of nonlinear model predictive control and moving horizon estimation to optimally control an overhead crane. Real-time implementation of this combined optimal control and estimation approach with execution times far below the sampling time was realized through the use of automatic code generation. Besides experiments that reflect good point-to-point performan...

This paper presents an experimental validation of a recently proposed robust normoptimal iterative learning control (ILC). The robust ILC input is computed by minimizing the worst-case value of a performance index under model uncertainty, yielding a convex optimization problem. The proposed robust ILC design is experimentally validated on a lab scale overhead crane system, showing the advantage...

A control strategy is developed for a 3-dimensional overhead crane. Using the differential flatness of the crane and a parameterization method, we first calculate the optimal trajectory of the payload that results in minimum transfer time when there exist obstacles in the direct moving path. A learning algorithm is then used to generate the desired feed forward input signal that can drive the s...

An overhead crane transports cargoes of various weights and volumes depending on the operation case. Friction factors characterized by damped coefficients are changeable in terms of the operating environment. In this study, an adaptive version of the sliding mode control of a crane system is developed in the case of no priori knowledge of the payload mass and damped elements. Using two inputs, ...