Research Of Three-axis Turntable’s Control System

With the development of the aerospace and aviation industry recent years, the role of inertial system becomes more and more important, and the precision of inertia components also must be improved. As the test equipment of inertial components, the three-axis turntable must be improved in precision. C onsidering the coupling effect existing in turntable system, the dynamic model of three-axis- turntable will be derived, and some decoupling algorithms will be presented. After the turntable system is decoupled, controlling algorithms and related performance will be researched in next chapters.Firstly, the mathematical model of three-axis turntable is constructed. The model of brushless electric motor is established and simplified motor model is given. Based on behaviors of turntable, the dynamic models of three axis are derived and then integrated model is derived by the interaction of forces. From the simulation results based on three-axis- turntable, the accuracy of model and the existence of coupling are proved. The princ ipal diagonal and non principal diagonal are deduced by static identification algorithm, and inertia matrix is given. After that, nonlinear feedback decoupling algorithm and the decoupling algorithm based on Lee derivative are introduced. The result of the nonlinear feedback decoupling is proved by simulation.Secondly, the time- vary sliding mode control algorithm is researched. Analysis of interference factors of turntable system is introduced. Based on that, a time- vary sliding mode control algorithm is designed, and the convergence of the algorithm is proved. Then the performance of time- vary sliding model and traditional slid ing model is compared. In order to further compare the tracking results of time- vary sliding model and traditional sliding model, the simu lation of sine signal is presented.Finally, in order to optimize the former control algorithm, an iterative learning control algorithm based on time- vary sliding mode is designed. During the init ial period, the iterative control algorithm is selected to decrease error and accelerate velocity of convergence. When the error is limited to a minimum, sliding mode is activated and iterative learning control algorithm is stopped Although the iterative control has good effect in tracking, it has some obvious defects such as slowly learning rate and too many iteration times. And then the neural network algorithm is introduced. N eural network has advantage in refining the gain of iterative control and combing together the learning online, controlling online and refining the performance of system.