Study On Some Key Problems Of Three-axis ATP Moving Platform

The signal of photoelectric ATP (Acquisition,Tracking and Pointing) system is carried by light-wave, whose frequency is higher than microwave. Therefore, the tactical performance of ATP system was constitutionally improved. The applications in arms control, celestial observation, shooting range determine, aeronautics and astronautics, laser communication and etc. were constantly extended. The performance demand of ATP system is dissimilar in different fields, but the trend of development is higher precision, faster speed and more extensive applicability.Because of the continuously demand for higher performance of photoelectric ATP system, especially for the maneuverability, keeping away from atmosphere and light transfers and etc. the photoelectric ATP system based on the earth can not meet the demand. The development of photoelectric ATP system setup on moving platform becomes the significant question for discussion recently.Comparing with the ATP system based on the earth, the ATP system setup on moving platform has more questions to deal with. Arm to achieve higher precision, faster speed and more extensive applicability, besides the high performance photoelectric transducer, the servo system providing with fast speed, flexibility and high precision is also the key question. The configuration of executive machine, the backlash and friction consisting in gearing are all the primary factors limiting to improve the precision of servo system. Otherwise, along with the development of modern war, aeronautics and astronautics, detection technology and intelligent control, the moving bed of unpiloted, avoiding collision intelligently and self-acting path planning becomes one of the dominant fields of ATP system.Base on the above perception, the tracking and pointing characteristic of three-axis ATP system, the dynamic modeling of three-axis structure, the three-axis optimization of tracking and pointing, backlash and friction compensation and path planning and etc. were investigated as follows.Base on the ATP system setup on moving platform designed all by us, the multifarious characteristic of tracking and pointing strategy was introduced. The effects of deflection distance on tracking capability were analyzed, and the methods deal with the sightless area and error caused by deflection distance were introduced.The dynamic model of three-axis ATP system was established base on Lagrange-Maxwell functions and the quaternary method for describing the rigid body rotation. The movement of three-axis ATP system was analyzed based on the dynamic model. The validity of this dynamic model was validated by simulation and experiment results.The idea of nonlinear decreasing inertia weight strategy was introduced in modified Meta particle swarm optimization (M2PSO), and an improved M2PSO was presented. The performance of M2PSO and improved M2PSO was tested by Sphere and Rosnbrock functions. The test results suggested that the convergent speed of improved M2PSO is higher than that of M2PSO.Based on the movement analysis of three-axis ATP system, the solution of angle increment was transformed to a question of optimization. If the current angles and the detect errors are known, the angle increment of three-axis can be solved by improved M2PSO.A method of RBF neural network sliding mode variable structure compound control was presented. The design process of compound controller was introduced in detail. The stability of compound controller was analyzed, and the stable condition of compound controller was put forward. The compound control can reduce the buffeting of sliding mode variable structure control by adopting the learning function online of RBF neural network, which can adjust the switch gain of sliding mode variable structure control.The dynamic model of three-axis drive system including backlash and friction was established based on the backlash hysteresis model and friction aggregation model. The RBF neural network sliding mode variable structure compound control was adopted as the backlash and friction compensate control method. The effectiveness of PD control, sliding mode variable structure control and RBF neural network sliding mode variable structure control were simulated and compared. Results suggest that RBF neural network sliding mode variable structure control can achieve higher precision than PD control and sliding mode variable structure control.The kinematic and dynamic model of the moving platform was established, and a grid method for uncertain environments based on sensors was presented. Because the classical potential field method has the problem of local minimum, a modified potential field method base on PSO was put forward. The program flow of improved method was presented in detail. Simulation results suggest that the modified potential field method can avoid the local minimum of classical potential field method.