Research On System Modeling And Control Of Image Motion Compensation Device

The image motion compensation device has become an indispensable device for testing inertial devices due to its advantages such as high stability,high flexibility,and simple structure.Not only in aerospace,military,but also in civil industry and other fields are also widely used,its accuracy also has a direct impact on the test results of inertial components.So far,the research on the image motion compensation device mainly involves its software and hardware design,dynamic analysis,decoupling of the image motion compensation device,etc.However,the research on the system control technology of the image motion compensation device is not perfect.However,if you want to obtain The best control performance and the realization of precise control are particularly important.This paper takes the image motion compensation device as the research object,and the main work is as follows:(1)System modeling of image motion compensation deviceDescribe the mechanical structure of the self-designed image motion compensation device according to the technical indicators.According to the motion characteristics of the three shaft frames of the image motion compensation device under three degrees of freedom,the frame coordinate system of each shaft frame is defined,and the transformation relationship between the rotation angles of the three shaft frames fixedly connected to each other is derived,which is the system solution Couple to pave the way.Finally,the motor model is determined according to the overall size and driving mode of the image motion compensation device,and the system is modeled to lay the foundation for system identification.(2)Decoupling analysis based on the kinematic model of each axle frame of the image motion compensation deviceAccording to the conversion relationship between the gravitational moments between two adjacent axle supports,the kinematic equations of the three axle supports of the image motion compensation device are obtained,and the coupling of the three axle supports of the device is analyzed on this basis.According to the Lie derivative operator,the decoupling control law is obtained,and the Simulink module in Matlab is used to build the decoupling controller and simulate it,which lays the foundation for the design and analysis of the identification of the nonlinear system of the image motion compensation device and the control algorithm.(3)An improved cuckoo search algorithm is proposed to identify the nonlinear Wiener system of the image motion compensation deviceAn improved dynamic adaptive probability of cuckoo search algorithm(APCS)is proposed,which dynamically adjusts the value of the discovery probability according to the cuckoo flight mechanism to realize the parameter identification of the nonlinear Wiener system model.At the same time,select two standard test functions to compare the performance of traditional Cuckoo Search algorithm(CS),Adaptive Step size Cuckoo Search algorithm(ASCS)and APCS algorithm,and collect them through system identification.The obtained experimental data verifies the global search capability of APCS.(4)Design a sliding mode variable structure controller for the identified image motion compensation device systemAiming at the nonlinearity and uncertainty of the image motion compensation device system,a sliding mode variable structure control algorithm based on the improved reaching law is designed,that is,the exponential term is added on the basis of the double power reaching law.The sliding mode control method takes s=1 as the critical value,and divides the system to the sliding mode surface into two stages,which speeds up the response speed of the system,reduces the system chattering,and reduces the steady-state error.Then design the controller according to the proposed control strategy,and analyze its convergence with the help of Lyapunov Stability.Finally,according to the designed controller,the Simulink module in Matlab is used to build the control system,and the system stability is analyzed through the step input signal and the system response curve obtained under the sinusoidal input signal to verify its rationality and effectiveness.