Search

In order to solve the difficulties in flight control law design of a helicopter caused by its strong coupling characteristics, an improved linear quadratic regulator (LQR) design method based on particle swarm optimization algorithm is proposed. Based on the linearized state space model of a helicopter, a basic algorithm to solve the full state feedback matrix in helicopter flight control law is established based on LQR design method. A cost function for particle swarm optimization, which maximizes the influence factor of main state feedback coefficients and takes the stability of the system as constraint, is designed. The multiparameter synchronous optimization of weighting matrix Q is implemented, and the optimized main state feedback coefficients are used as control law design results of a helicopter. The flight control law of a UH-60A helicopter at hover state is designed and verified. The results show that the control law designed by using this method has good performance and the flying quality of the helicopter is increased considerably. Meanwhile, the difficulty in determining weighting matrix during LQR design procedure is solved systematically based on the optimization method developed in this paper, and the efficiency in control law design is increased. [ABSTRACT FROM AUTHOR]

Picking the first arrivals is an important step in the near-surface modeling method. The precise picking of the first arrivals can affect many aspects of the work，such as static correction and velocity modeling，etc. However，the first arrivals are affected by strong background noise and complex near-surface structure， which lead to the reducing accuracy. This paper proposes the first-arrival automatic picking algorithm based on two-stage optimization（FPTO）. First-arrival automatic picking algorithm based on two-stage optimization divides the picking question into two sub-questions. According to these two sub-questions，two optimization targets are generated respectively. Next，two optimization functions are generated by these two optimization targets. First-arrival automatic picking algorithm based on two-stage optimization takes two steps：the first step is to use the vertical sliding window to find out the possible range of the first arrivals；the second step improves the energy ratio algorithm and picks up the first arrivals in the range found in the first step. Compared with three algorithms（Backpropagation neural networks method， BNN；Direct correlation method，DC；Modified Coppens's method，MCM） on the two data sets， experimental results show that the accuracy and the stability of the FPTO algorithm are greatly improved. [ABSTRACT FROM AUTHOR]

To improve the quality and efficiency in satellite design, the satellite multidisciplinary design optimization is presented with the object of minimizing total mass considering orbit, payload, power supply and structure. The paper combs the coupling among the four subsystems and establishes the detailed and practical disciplinary analysis model. The optimization strategy is proposed based on support vector regresstion (SVR) method. The strategy is applied to muhidisciplnary design optimization (MDO) of the marine satellite. The studied satellite takes Ocean 1 satellite (HY-1) as a reference, and the total mass of the satellite is reduced by 14. 1%0 after optimization compared with the initial value. The optimization result proves the rationality of the satellite's MDO model and the effectiveness of the optimization strategy. It is of vital importance to the future work of the application of SVR metamodel in satellite MDO design. [ABSTRACT FROM AUTHOR]