Intelligent PID Approach For The High-precision Temperature Control Of Star Clock

Star clock in the field of basic research a wide range of applications, such as changes in the Earth's rotation dynamics, narrow, experimental verification of general relativity and research, the cycle of pulsar research and geodetic satellite dynamics and so on. Moreover, the star clock is also the building of national defense and the economy also has important applications such as aerospace, radar, sonar technology, satellite launching and monitoring, measurement and control aircraft and guidance, navigation and communications, geological mapping,power transmission, communication, synchronization network building and so on. In these applications, the satellite clock to ensure long-term stability is very important. Studies have shown that Star clock resonant cavity temperature fluctuations in the satellite clock frequency of long-term stability of the main factors. Therefore, raising the temperature of the resonator satellite clock stability is necessary. This thermal model based on star clock designed a variety of intelligent PID control method, developed in the MATLAB platform, a variety of control methods for control systems.In this paper, their work units:1. With a large number of refereinces, review the temperature-controlled satellite clock research background and the current control area is of more sophisticated control methods;2. Clock thermal systems based on star streamlined model is designed to split the integral PID Star resonant cavity temperature to keep the system clock to find intelligent PID control method to adapt to their own parameters and external disturbance changes.3. In separate integral PID control system based on the design of RBF neural network model based on approximation of the PID control system;4. Fuzzy RBF neural network-tuning PID is a dynamic adjustment of PID parameters, to a large extent be able to do high-precision control. As the neural network learning ability and adaptive ability, making the face of parameter variations and external disturbances has a strong adaptability membership degree as well as the dynamic adjustment of weights makes the model adaptable. RBF neural network model will be based on approximation of the PID star clock thermostat to keep the system resonant cavity further to fuzzy RBF neural network-tuning PID control system and simulation control performance of the system ;5. Specific study of the integral discrete PID, based on RBF neural network model approximation of the PID and the RBF neural network based on fuzzy-tuning PID temperature control program in the satellite clock model in real applications, the actual satellite clock model of the algorithm on a simulation, and were compared to arrive at intelligent control methods overshoot is relatively small, the precision is higher. This fully verified intelligent PID control method will be the next phase of the high-precision control of the important direction.6. Thermal systems for the satellite clock parameters of complex and changing conditions, to study an improved robust projection identification algorithm and simulation verify the effectiveness of the algorithm; Finally, the simulation results of various control methods, a comparative analysis; for the two models respectively model transformation, parametric linear processing, and simulation. This is due to model complex, large measurement noise, while the linear model was similar to the model itself, rather than the original model was derived directly, thus there is a certain margin of error identification. Finally the parameter identification to a certain range, can provide the necessary guidance system design information;In the satellite clock temperature control system, temperature system for the satellite clock non-linear model, we designed a discrete PID control points, based on RBF neural network model approximation of PID control and fuzzy RBF neural network-tuning PID control, and projection algorithm based on robust Star clock model-line parameter identification of several other more mature control methods, and several groups of control methods of these were done under different sets of simulation, simulation results comparing these groups, you can draw the following conclusions:1. Integral discrete PID control parameters in the system remain unchanged, there is no delay, non-linear links, as well as outside the case of small random disturbances can reflect strong adaptability and reliability, and simple structure and low cost. In general the accuracy of less demanding situations, integral discrete PID control is widely used, and the results would be more satisfactory; but with time-varying nonlinear system, PID control of discrete points only to a certain extent reflect the out of control, effects, too, when the time-varying and non-linear, integral PID control separate expression of the more obvious limitations.2. Based on RBF Neural Network Approximation of PID control can be used for precise mathematical model of the system are not readily available, or the system parameters change occasions. Control of the premise that the RBF neural network approximation of the model with sufficient precision, if the approximation results are not satisfactory, the control will be in a disordered state, without achieving the desired control effect. To achieve this premise, the RBF neural network is particularly critical to set the initial parameters, which have to rely on expert experience to be adjusted. Achieved this premise, the simulation results from the point of view the system control quality very good, the overshoot is small (compared with PID control of discrete points to be much smaller),Control parameters to changing circumstances, the control accuracy is still high.3. Fuzzy RBF neural network-tuning PID control can also be used for precise mathematical model of the system are not readily available, or the system parameters change occasions. PID control parameters output by the RBF neural network, in which the RBF neural network Jacobian information is obtained approximate solution, the system of quality control is better than the PID number of discrete points, but compared with RBF neural network model based on the approximation of the PID control is also some lack of .4. Clock model parameters based on star identification algorithm is mainly directed against the system parameters are unknown or time-varying case, the system model solving research methods. Exact Solution of the system model in control system is a very important aspect of the design of high-quality control system is a necessary precondition. The project uses robust projection algorithm for identification star clock model, simulation results from the analysis, identify good results, the identification algorithm is effective.