Research On Control And Error Compensation Methods For Rocket Nozzle Motion Simulation Device

Semi-physical simulation plays an important role in the defense industry,while the rocket-nozzle motion simulation device belongs to a category of semi-physical simulation technology.Because of the special purpose of the rocket-nozzle motion simulation device,there is a stringent requirement in terms of its control accuracy.Therefore,in order to improve the control accuracy of rocket-nozzle motion simulation device,it is of great significance for this paper to study the control and error compensation methods of rocket-nozzle motion simulation device.In order to improve the control accuracy of rocket-nozzle motion simulation device,this paper mainly studies the control and static position error measurement and compensation methods of the system.The following is the specific content of this paper.Firstly,this paper introduces the research status of servo control methods,static position error measurement methods and static position error compensation methods in detail.In terms of control method,the Active Disturbance Rejection Controller(ADRC)is adopted for the rocket-nozzle motion simulation device in this paper,and this method not only doesn't depend on the exact mathematical model of the system,but also has strong anti-interference ability.However,there are many parameters need to be tuned in ADCR,so a method to tune the parameters of ADRC based on the improved Chaotic Particle Swarm Optimization(CPSO)algorithm is proposed in this paper.The MATLAB simulation results show that the proposed method can tune the parameters of the ADRC and achieve good control effect.Secondly,although the ADRC can achieve a good control effect in the rocket-nozzle motion simulation device,there is still a static position error.In order to meet its stringent requirements for control accuracy,this paper also needs to measure the static position error of the rocket-nozzle motion simulation device and compensate it.The static position error of the rocket-nozzle motion simulation device is measured by binocular vision measurement system,and then the sum of error is compensated by RBF neural network.An RBF neural network training algorithm based on subtractive clustering & gradient descent method is proposed in this paper.RBF neural network is trained by a part of static position error data,and then the training result is verified by the remaining error data.In the end,it can be found that the error compensation method can improve the control accuracy of the rocket-nozzle motion simulation device.Finally,the PC control software of rocket-nozzle motion simulation device is designed by Visual C++ 6.0 in this paper.The software can control the rocketnozzle motion simulation device to achieve some trajectory operation,such as manual operation,sine and cosine trajectory operation,and circular trajectory operation.Through the experimental results we can see that the control accuracy of rocket-nozzle motion simulation device meets the requirements.