Real-time Predictive Control of 3D tower crane

In this paper a real-time Model Predictive Control (MPC) for a 3D tower crane, based on subsequent solving of three quadratic programs, is proposed. Three motions of the tower crane are considered as separate subsystems with couplings among them treated as a change in system parameters. Such linear parameter varying (LPV) system can be sampled and transformed into the corresponding polytopic model, by using a Tensor Product (TP) Model Transformation. Polytopic TP model of the tower crane is used to calculate the terminal set and the terminal cost via solving Linear Matrix Inequalities (LMI). In order to guarantee recursive feasibility, system states are kept in ellipsoidal approximation of worst case initial feasible set, while the asymptotic stability is ensured by using a dual-mode MPC strategy. The proposed approach is verified through simulation and experimental test on laboratory model of a 3D tower crane.