Study On The Neuro-Fuzzy Control Based On Bettered Genetic Algorithm For The Attitude Of Flexible Satellite

The satellites more and more play a major role in national economy and military affairs. A lot of business can not get away man-made satellites, for instance, correspondence, navigation, weather, and so on. With the development of the aerospace technology, the man-made satellites construction is increasingly complexity, so the flexibility problem of satellites impacting on attitude of satellite increasingly stands out, and the requirements for the flexible satellite attitude control are getting more and more complex. The flexibility structure of the satellite result in a few accidents of the control performance descend or astaticism, which indicates both classical control and modern control havn't completely solved these issues, but intelligent control basically independent of model of controlled object. The study on this line not only has certain help for design of vehicle control systems of current type satellites, but also momentous sense on control system design of large-scale space structures such as manned vehicles and space stations.In order to study new methods used in satellite attitude control, this thesis presents the identification and controller of T-S type fuzzy neural network based on genetic algorithm. Then this thesis makes simulation study on the attitude stabilization control of flexible satellite and analyses control outcome.Firstly, the models of the three-axis stabilized attitude control systems of a rigid satellite and a flexible satellite with a pair of solar arrays are set up. The model is composed of kinematics equation and dynamics equation. The model of the attitude control of the three-axis satellite is decoupled under assumer and predigestion, that is to say, the model is used in simulation.Secondly, the simple genetic arithmetic (SGA) is improved. Genetic algorithms are search algorithms based on the mechanics of natural selection and natural genetics. In general, a solution of an objective function is represented as a chromosome in a string structure, and each element represents a parameter in the solution. Through a search process such as selectness, crossover and mutation operators, the choice individuals in the string structure increase with series, and finally the chromosomes will approach the global optimum. The solutions are commonly encoded as binary strings, algorism strings and Gray Code, but in dimensions and high precision continuum problem, the second is better than the first, so in this thesis, genetic algorithms uses algorism coding. Genetic operations of simple genetic algorithms (SGA) easily decrease diversity of genetic population, reduce competition of individuals and result in premature, so genetic algorithms can't find global optimum. Aimed at these problems, the bettered genetic algorithms (BGA) is put forward. It changes the roulette wheel selection into the selection based on the fitness of individuals in population in order to preserve the individuals with the high fitness. The selected individuals will be put into the second process. Used changing P_c and P_m to adapt the changing situation. It can increase diversity of the population and avoid premature. In the end, BGA is tested by De Jong function. The result shows the feasibility and effectiveness of BGA in overcoming premature and improving convergence speed and accuracy.Thirdly, There were three input/ three output Neuro-Fuzzy network for identification. And there were three Fuzzy congregation of gauess function. Used BGA to optimize the typical value, variance and weighting of Neuro-Fuzzy network. The Neuro-fuzzy network combined the structural knowlage of Fuzzy reasoning and learning capability of neural nerwork. It combined the two field perfectly. It can solve the problems without the precise model, only use the user and the perfeson's experience. But because of the experience and the parameter of network were limited, it is hard to make the network model. So we used the beterred genetic algorithms to optimize the structure of neural network. We verify the feasibility and validity by using BGA to optimize the Neuro-fuzzy network to simulate the identification of a three-axis-stabilized Flexible satellite.Therefore, this paper presents the T-S type Neuro-Fuzzy control system of a three-axis-stabilized Flexible satellite. From the identification to get the change rate of angle, It will help us to amend the learning rate and parameter of the controller. It was made five subfunction for the three axes angle and it's rate of the Flexible satellite. And the subfunction is gauss function. The control moment of the flywheel is between [-0.2,0.2]. I got the center of the clustering with fuzzy C-mean clustering, and got the typical value of the Neuro-fuzzy controller. By the back structure, we can got the weighting of the Neuro-fuzzy contoller. After the clustering, a few parameter will be optimized, It will be faster. And the adaptation function is J(ITAE). It will accurate to use the parameter K to adjust the learning rate. This paper simulate the three axis angle of the flexible satellite, when the inertia matrixes rise from 70% to 100%. The result showd that the perturbation and the vibration of subassembly can be keep down, and the control is precise, after the Neuro-fuzzy to be optimized.At last, we sum the achievement up and pointed out the deficiency in this paper.