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A novel method, modified particle swarm optimization algorithm, is proposed to compute flight envelope in this paper. According to the principle of thrust method, the problem of solving flight envelope is converted into searching the optimal solution by the particle swarm optimization algorithm (PSO). Combined with adaptive inertia weight factor, the standard PSO algorithm is modified. Applying the modified PSO algorithm, the flight envelope is solved. Numerical simulations results illustrate that the search algorithm is feasible and efficient for solving flight envelop.

In order to investigate and exploit advanced flight management system with complete functions, provide applicable and reliable performance data for optimal trajectory search, data structure requirements of performance database of flight management system are presented, including the structure of aerodynamic data ,engine performance data and operation data. The whole authentic performance data is acquired by integrating the performance data of flight simulator in the second phase of 985 projects and the flight manual. Based on the former performance data, combining the thrust method, particle swarm optimization algorithm and the energy-state method, the performance data is transferred into equal energy-height curves which can be more applicable for optimal trajectory search. Based on the processed performance data, the performance model is built on the basis of Microsoft Visual C++ and MATLAB/SIMULINK. The running results of the flight management system program indicate the method used to exploit performance database of flight simulator prototype system is effective and feasible.

Points cloud surface reconstruction method in reverse engineering was proposed, Radial basis function neural network (RBFNN) was designed, and simulated annealing arithmetic was adopted to adjust the network weights. MATLAB program was compiled, experiments on points cloud data have been done employing the program. All experiments have shown that the arithmetic can efficiently approach the surface with 10-4 mm error precision, also the learning speed is quick and reconstruction surface is smooth. Trainings have been done with other networks in comparison. The sum squared error is 4.6802×10-8mm using the algorithmic proposed in this paper, the one is 1.1014×10-6mm under the same parameters employing RBFNN only. Back-propagation learning algorithm network does not converge until 3500 iterative procedure, and exactness design RBFNN is time-consuming. The arithmetic can approach nonlinear function by arbitrary precision, and also keep the network from getting into partial minimum.

This paper presents the theoretical development of a revised optimal control pilot model (ROCM) based the modified optimal control model (MOCM) of human operator developed by Davidson and Schmidt. An algorithm designed for easy implantation in current computer simulation and simulator fidelity analysis is presented. Example results based upon the analysis of a tracking task using a basic dynamics system is compared with the results of the ROCM and the results of the MOCM. The pilot frequency responses and root mean square obtained with the ROM are shown to more favorably to the measured experimental results than the MOCM in the pilot's frequency range of interest. The ROCM is not only consonant with the appropriate physical essence of the pilot, but also possess' the advantages of the MOCM.