Dynamic Analysis And Control Of The Cable-cabin Structure For Large Antenna System

The cooperative variation of lengths of six long cables pulls the feed cabin to track some radio source with six-DOF. Similar to a parallel robot, the cable-cabin flexible structure for large antenna system, i.e. the next generation large spherical radio telescope(LT ), is viewed as a cable-supported parallel robot(CPR). Conventional robots with serial or parallel structures are impractical for some applications since the workspace requirements are higher than what the conventional robots can provide. CPR uses cables instead of links to manipulate objects. The cables are so light that actuators of CPR have only to drive their loads. Furthermore, CPR gives a wide range of motion, because drums of the mechanism can wind long cables. For these reasons, cable driven mechanisms have received attention and have been recently studied since 1980s.Dynamic behavior and control method of CPR for LT5m model are studied. Based on the kinematics analysis the dynamic model of CPR is established using Lagrange's Equations. Through the model the inverse dynamic problem of CPR is solved. Simulations on the system of design of the LT5m model are made based on the established dynamic equation. Results of simulation justify the dynamic modeling for control and a foundation is made for CPR control used for LT5m.A decentralized design method is proposed. Considering the inherent characteristics of strong nonlinearity, large delay, MIMO and multivariable coupled flexible structure, a novel control method combining bi-fuzzy control and disturbance observer is developed to realize the trajectory tracking of the feed. Through observation of a variety of disturbances by using disturbance observer, the disturbances acting on the flexible structural system can be compensated; as a result, the effect of disturbance is suppressed. Combining a practical project, an algorithm of bi-fuzzy controller with self-tuning factor and proportional-integral-tuning unit, which optimizes the control rule by adjusting factors, is introduced to approximate optimal control. Simulation results have shown that the tracking accuracy is much better and the control system has strong robustness.