Research On Problems Of Aircraft Attitude And Navigation Based On Neural Networks

Traditional type of control needs mathematics model about controlled objects and designs controller based on performance indexes. But controlled objects are complex and uncertainty for nonlinear systems, thus traditional methods cannot solve the problems of modeling and control. Neural network imitates human brain systems using, storing and searching information on different degrees. It can approach arbitrary nonlinear function and has a good many merits of combining character, learning and organizing, tolerant fault and association of ideas. So it can solve many problems in nonlinear control fields. At present it obtains practicing application in many fields and branches, such as foundation knowledge, industrial produce, information technology and national defence military affairs etc.Neural network control receives great development in recently years, for its many good merits, it can be used to get modeling and be looked as controller or estimate device. For aircraft attitude and navigation system, the precision cannot be met by using traditional control. So it needs search an efficient method to solve the problem, and the neural network shows inhere superiority.The aircraft attitude control and GPS/INS navigation system are dealt with based on neural network in this thesis. First of all, the problem of predicting aircraft attitude is discussed by using BP algorithm. The predictive output is obtained. To expectation output locus, the optimal controlled sequence is presented by means of the nonlinear optimizer. The problem of attitude prediction is solved based on aircraft attitude equations. Then, the problem of aircraft attitude control is studied. Using BP algorithm,the aircraft angle speed in coordinate projection is adjusted. For it has strong robusness the inertia! gene is introduced to predict the output of attitude. Simulation shows the change of aircraft attitude. At last, the problems of GPS/INS integral navigation system are researched based on H∞ filter neural networks. The mapping relationship between input and output cannot be got if only using neural networks. So the H∞ filter is combined with the neural network to obtain the expectation output of the system, which much more improves the precision of navigation.BP algorithm of neural network is mostly used in the thesis. It can approach arbitrary nonlinear mapping if BP networks have enough latent layers and nodes. It adopts whole means of learning algorithm and has many joint powers, so it has good generalize ability and tolerant fault character. BP network obtains abroad applications in control fields.Simulation results are given at the end of each chapter to show the validity of each result.