Research On Mechanical Performance And Coordinated Control Of Cable Parallel Manipulator For Multiple Coorperative Cranes

As the engineering tasks become more arduous and complicated, a single crane can hardly handle the tasks under special working conditions. Meanwhile, the rapid developments of single crane in configuration, material and control strategy make it possible to lift the load with coorperative multiple cranes, which has been more and more applied in engineering practice. However, the coupling between multiple cranes increases the compliexity and dangerous, which tend to cause the extreme serious accidents. Thus, it becomes more and more important to ensure the coordinated control during the coorpertive hoisting process. This thesis was supported by Chinese National Natural Science Foundation, entitled by “Research on dynamic performance analysis and coordinated control technology of large heavy-duty special cable parallel manipulators for multiple cranes(Project Number: 51275515)”. Research on mechanical performance and coordinated control of the CPMMC are conducted, which is aimed to provide the theory basis and technology solution for the safe and efficient operation of the CPMMC. The main research works can be described as follows:Firstly, the cable parallel robot theory is applied for the analysis of cable parallel manipulator for multiple coorperative cranes. Based on the engineering requirements and the theory of cable parallel manipulator on degree of freedom, two configurations of the CPMMC are designed and the corresponding hoisting schemes are proposed, which are 3R3T-CPMMC and 3T-CPMMC, respectively. Based on the kinematic analysis, the D’alembert principle and Langrange equation are adopted to solve the dynamics problems of the mentioned two configurations. The dynamics characteristic of the whole system is obtained through numerical simulation, including the changing regularity of the motion and the driving load of hoisting subsystem, the luffing subsystem and the slewing subsystem. The results indicate that the force fluctuation on the steel cable of 3R3T-CPMMC is significantly larger than the 3T-CPMMC due to the under-constrained characteristic of the 3R3T-CPMMC. In addition, the inclination of the steel cables is increased with the speed of the load in the 3R3T-CPMMC, causing larger lateral forces on the cranes, which reduces the safety and efficiency of the system during the lifting process. Through the numerical simulation analysis of two configurations, the 3T-CPMMC is adopted for further research.Secondly, the important performance indicators of the 3T-CPMMC are analysed, including the workspace, dexterity indicator, error modeling and sensitivity analysis. The workspace generating algorithm is proposed, and the influences of the key structure parameters to the workspace volume are exposed. On the basis of kinematic analysis, the error model of the 3T-CPMMC is established and the local sensitivity analysis is conducted in order to reveal the impact of various error sources on position error of load. Based on the physical meaning of the condition number of Jacobian matrix, the dexterity performance indicator is investigated. Combining the sensitivity and dexterity analysis in different sections of hight, the working area is obtained with large span, high precision of the load position and better dexterity according to the results of numerial simulation, which is of great significance for structural parameter optimization and coordinated control in engineering application of the CPMMC.Lastly, considering the repetitive characters of the tasks of the CPMMC, the robust iteration learning controller for the CPMMC is designed based on the dynamic model. The random disturbances and linearized residual are taken into consideration. Compared with the numerical simulation of traditional PD controller, the robust iterative learning controller for the CPMMC is proved to be of better trajectory tracking performance and lower power consumption. The experimental prototype of the CPMMC is established, and the control and measure system are developed as well. The results of the trajectory tracking experiment validate the effectiveness of the robust iterative learning controller for the CPMMC and lay the foundation for industrial application experiments in the further research.