Partial state feedback sliding mode control for double-pendulum overhead cranes with unknown disturbances

In this paper, a novel sliding mode controller which requires partial state feedback is proposed for double-pendulum overhead cranes subject to unknown payload parameters and unknown external disturbances. Firstly, it is theoretically proved that the hook and payload tend to their respective equilibrium points concurrently. Secondly, a decoupling transformation is performed on the original nonlinear dynamics of double-pendulum overhead cranes. The novel sliding mode controller that does not require the prior information and motion signals of the payload is designed based on the decoupled nonlinear dynamics. Then, the asymptotic stability of the equilibrium point of double-pendulum overhead cranes is proved by rigorous analysis. Finally, several simulations are conducted to validate the effectiveness and robustness of the proposed controller.

Risk Analysis and Reliability Assessment of Overhead Cranes Using Fault Tree Analysis Integrated with Markov Chain and Fuzzy Bayesian Networks

Establishing an adequate level of reliability in the overhead crane operations is an important and vital principle to avoid undesirable consequences. To do this, it is appropriate to have a comprehensive approach for risk and reliability assessment of the most probable failure scenarios during overhead crane operations. In this study, fault tree analysis (FTA) in combination with fuzzy set theory, Bayesian network (BN), and Markov chain was used to evaluate the probability of top event and reliability of overhead cranes. A total of 47 basic events were identified for ladle fall in overhead cranes. The results showed that the probability of the ladle fall in the FT approach is equal to 0.0523035 and in the BN approach in the prior event is equal to 0.0273394 which is less than the FT method. Based on the values predicted by Markov chain, the reliability of the system decreases over time by 67.9% after 60 months. This study showed that the plan for ladle fall prevention should consider all influencing parameters identified by proper risk assessment methodologies.

Genetic Programming Based Identification of an Overhead Crane

Overhead cranes carry out an important function in the transportation of loads in industry. The ability to transport a payload quickly and accurately without excessive oscillations could reduce the chance of accidents as well as increase productivity. Accurate modelling of the crane system dynamics reduces the plant-model mismatch which could improve the performance of model-based controllers. In this work the simulation model to be identified is developed using the Euler-Lagrange method with friction. A 5-step ahead predictor, as well as a 10-step ahead predictor, are obtained using multi-gene genetic programming (MGGP) using input-output data. The weights of the genes are obtained by using least squares. The results of 15 different genetic programming runs are plotted on a complexity-mean square error graph with the Pareto optimal solutions shown.