APPLICATION OF HYBRID GENETIC ALGORITHM IN AEROELASTIC MULTIDISCIPLINARY DESIGN OPTIMIZATION OF LARGE AIRCRAFT

The genetic/gradient-based hybrid algorithm is introduced and used in the design studies of aeroelastic optimization of large aircraft wings to attain skin distribution,stiffness distribution and design sensitivity.The program of genetic algorithm is developed by the authors while the gradient-based algorithm borrows from the modified method for feasible direction in MSC/NASTRAN software.In the hybrid algorithm,the genetic algorithm is used to perform global search to avoid to fall into local optima,and then the excellent individuals of every generation optimized by the genetic algorithm are further fine-tuned by the modified method for feasible direction to attain the local optima and hence to get global optima.Moreover,the application effects of hybrid genetic algorithm in aeroelastic multidisciplinary design optimization of large aircraft wing are discussed,which satisfy multiple constraints of strength,displacement,aileron efficiency,and flutter speed.The application results show that the genetic/gradient-based hybrid algorithm is available for aeroelastic optimization of large aircraft wings in initial design phase as well as detailed design phase,and the optimization results are very consistent.Therefore,the design modifications can be decreased using the genetic/gradient-based hybrid algorithm.     

Hybrid Multipopulation Cellular Genetic Algorithm and Its Performance

The selection pressure of genetic algorithm reveals the degree of balance between the global exploration and local optimization.A novel algorithm called the hybrid multi-population cellular genetic algorithm（HCGA）is proposed,which combines population segmentation with particle swarm optimization（PSO）.The control parameters are the number of individuals in the population and the number of subpopulations.By varying these control parameters,changes in selection pressure can be investigated.Population division is found to reduce the selection pressure.In particular,low selection pressure emerges in small and highly divided populations.Besides,slight or mild selection pressure reduces the convergence speed,and thus a new mutation operator accelerates the system.HPCGA is tested in the optimization of four typical functions and the results are compared with those of the conventional cellular genetic algorithm.HPCGA is found to significantly improve global convergence rate,convergence speed and stability.Population diversity is also investigated by HPCGA.Appropriate numbers of subpopulations not only achieve a better tradeoff between global exploration and local exploitation,but also greatly improve the optimization performance of HPCGA.It is concluded that HPCGA can elucidate the scientific basis for selecting the efficient numbers of subpopulations.     

Assembly Line Balancing Based on Double Chromosome Genetic Algorithm

Aiming at assembly line balancing problem, a double chromosome genetic algorithm （DCGA） is proposed to avoid trapping in local optimum, which is a disadvantage of standard genetic algorithm （SGA）. In this algorithm, there are two chromosomes of each individual, and the better one, regarded as dominant chromosome, determines the fitness. Dominant chromosome keeps excellent gene segments to speed up the convergence, and re cessive chromosome maintains population diversity to get better global search ability to avoid local optimal solu- tion. When the amounts of chromosomes are equal, the population size of DCGA is half that of SGA, which significantly reduces evolutionary time. Finally, the effectiveness is verified by experiments.     

Improved Algorithm of Variable Bandwidth Kernel Particle Filter

Aiming at the large cost of calculating variable bandwidth kernel particle filter and the high complexity of its algorithm, a self-adiusting kernel function particle filter is presented. Kernel density estimation is facilitated to iterate and obtain new particle set. And the standard deviation of particle is introduced in the kernel bandwidth. According to the characteristics of particle distribution, the bandwidth is dynamically adjusted, and the particle distribution can thus be more close to the posterior probability density model of the system. Meanwhile, the kernel density is used to estimate the weight of updating particle and the system state. The simulation results show the feasibility and effectiveness of the proposed algorithm.     

OPTIMIZED STRAPDOWN CONING CORRECTION ALGORITHM

Traditional coning algorithms are based on the first-order coning correction reference model. Usually they reduce the algorithm error of coning axis （z） by increasing the sample numbers in one iteration interval. But the increase of sample numbers requires the faster output rates of sensors. Therefore, the algorithms are often lim- ited in practical use. Moreover, the noncommutivity error of rotation usually exists on all three axes and the in- crease of sample numbers has little positive effect on reducing the algorithm errors of orthogonal axes （x, y）. Considering the errors of orthogonal axes cannot be neglected in the high-precision applications, a coning algorithm with an additional second-order coning correction term is developed to further improve the performance of coning algorithm. Compared with the traditional algorithms, the new second-order coning algorithm can effectively reduce the algorithm error without increasing the sample numbers. Theoretical analyses validate that in a coning environ- ment with low frequency, the new algorithm has the better performance than the traditional time-series and fre- quency-series coning algorithms, while in a maneuver environment the new algorithm has the same order accuracy as the traditional time-series and frequency-series algorithms. Finally, the practical feasibility of the new coning al- gorithm is demonstrated by digital simulations and practical turntable tests     

NONLINEAR FILTER METHOD OF GPS DYNAMIC POSITIONING BASED ON BANCROFT ALGORITHM

Because of the ignored items after linearization, the extended Kalman filter (EKF) becomes a form of suboptimal gradient descent algorithm. The emanative tendency exists in GPS solution when the filter equations are ill-posed. The deviation in the estimation cannot be avoided. Furthermore, the true solution may be lost in pseudorange positioning because the linearized pseudorange equations are partial solutions. To solve the above problems in GPS dynamic positioning by using EKF, a closed-form Kalman filter method called the two-stage algorithm is presented for the nonlinear algebraic solution of GPS dynamic positioning based on the global nonlinear least squares closed algorithm--Bancroft numerical algorithm of American. The method separates the spatial parts from temporal parts during processing the GPS filter problems, and solves the nonlinear GPS dynamic positioning, thus getting stable and reliable dynamic positioning solutions.     

FAST FEATURE RANKING AND ITS APPLICATION TO FACE RECOGNITION

A fast feature ranking algorithm for classification in the presence of high dimensionahty and small sample size is proposed. The basic idea is that the important features force the data points of the same class to maintain their intrinsic neighbor relations, whereas neighboring points of different classes are no longer to stick to one an- other. Applying this assumption, an optimization problem weighting each feature is derived. The algorithm does not involve the dense matrix eigen-decomposition which can be computationally expensive in time. Extensive exper- iments are conducted to validate the significance of selected features using the Yale, Extended YaleB and PIE data- sets. The thorough evaluation shows that, using one-nearest neighbor classifier, the recognition rates using 100-- 500 leading features selected by the algorithm distinctively outperform those with features selected by the baseline feature selection algorithms, while using support vector machine features selected by the algorithm show less prominent improvement. Moreover, the experiments demonstrate that the proposed algorithm is particularly effi- cient for multi-class face recognition problem.     

Adaptive Angular Velocity Tracking Control of Spacecraft with Dynamic Uncertainties

The tracking of orientation and angular velocity is a primary attitude control task for an on-orbit space- craft. The problem for a rigid spacecraft tracking a desired angular velocity profile is addressed using an adaptive feedback control. An angular velocity feedback tracking algorithm is firstly developed based on the precisely known attitude dynamics of the spacecraft, and the global tracking of the control algorithm is proved based on the Lya- punov analysis. An adaptation mechanism is then designed to deal with the dynamic uncertainties of the spacecraft. Such an adaptation mechanism enables the controller to track any desired angular velocity trajectories even in the presence of uncertain inertia parameters, although it does not guarantee the inertia tensor being precisely identified. To verify the effectiveness of the proposed adaptive control policy, computer simulations on dynamic equations of a spacecraft are conducted and their results are discussed.     

Vehicle State and Parameter Estimation Based on Dual Unscented Particle Filter Algorithm

Acquisition of real-time and accurate vehicle state and parameter information is critical to the research of vehicle dynamic control system. By studying the defects of the former Kalman filter based estimation method, a new estimating method is proposed. First the nonlinear vehicle dynamics system, containing inaccurate model pa rameters and constant noise, is established. Then a dual unscented particle filter （DUPF） algorithm is proposed. In the algorithm two unscented particle filters run in parallel, states estimation and parameters estimation update each other. The results of simulation and vehicle ground testing indicate that the DUPF algorithm has higher state estimation accuracy than unscented Kalman filter （UKF） and dual extended Kalman filter （DEKF）, and it also has good capability to revise model parameters.     

Optimal Control Strategy for Buck Converter Under Successive Load Current Change

A new control algorithm is presented for digitally controlled dc-dc converters to achieve a fast response under a successive load-change. Under the steady-state condition, the tight voltage regulation is processed by the conventional digital PID compensator. If the load disturbance is significant, the controller switches to an optimal control scheme. With the integration of the capacitor current, the proposed algorithm predicts the optimal switch over time based on the charge balance control, and the minimal voltage derivation and recovery time are thus achieved when the load current has a successive load-change. The method for calculating the optimal switch over time is described, and the implementation of the proposed algorithm with a digital controller is treated in detail. Furthermore, the simulation and experiment results are provided to validate the effectiveness of the approaches.     

NEW ALGORITHM FOR FAST INTEGER AMBIGUITY RESOLUTION

Fast integer ambiguity resolution is referred as a key part in precision relative positioning of the GPS carrier phase. A new algorithm for fast integer ambiguity resolution based on LAMBDA and FASF methods is proposed. This algorithm integrates the LAMBDA method and the FASF method, thus improving the efficiency of the ambiguity resolution. Firstly, the ambiguity search space transformation in the LAMBDA method is used,and then the FASF method is used to search ambiguities. Experiments in the relative positioning of about 1 km static baseline demonstrate that the error is less than 1 cm.     

基于基因算法与博弈论的气动高升力优化

A multi-objective evolutionary optimization method (combining genetic algorithms(GAs)and game theory(GT))is presented for high lift multi-airfoil systems in aerospace engineering.Due to large dimension global op-timization problems and the increasing importance of low cost distributed parallel environments,it is a natural idea to replace a globar optimization by decentralized local sub-optimizations using GT which introduces the notion of games associated to an optimization problem.The GT/GAs combined optimization method is used for recon-struction and optimization problems by high lift multi-air-foil desing.Numerical results are favorably compared with single global GAs.The method shows teh promising robustness and efficient parallel properties of coupled GAs with different game scenarios for future advanced multi-disciplinary aerospace techmologies.     

Weak GPS L1 Signal Acquisition Based on BPDC

It is difficult to achieve accurate acquisition of weak global positioning system（GPS） signals with traditional methods. A weak signal acquisition strategy based on block processing and differentially coherent （BPDC） is put forward after analyzing the advantages and disadvantages of coherent and non-coherent integration algorithms. Code phase parallel search of the pre-coherent integration is conducted by using fast Fourier transform（FFT）, and the results are then differential coherent processed and block processed. BPDC method reduces computation cost compared with coherent and non-coherent（CNC） algorithm. The performance of the two algorithms is also compared based on simulated signals. The result shows that the noise suppression effect of BPDC algorithms is superior to that of traditional CNC algorithm, and the superiority of BPDC is more apparent with the reduction of carrier to noise ratio （CNR）. In the case that the pre-coherent integration length is 4 ms and CNR is reduced to 28 dB-Hz, CNC algorithm cannot yet acquire signal correctly while BPDC has well acquisition performance. Therefore, for weak GPS signal acquisition, BPDC algorithm can acquire the signal with lower CNR and has better acquisition property.     

RESEARCH ON SCHEME OF HIGH-SPEED ROTOR／WING TRANSITION HELICOPTER RD15

To analyze the existing schemes of high-speed rotorcrafts and some new technologies, a new conceptual sketch of the high-speed rotor/wing transition helicopter RD15 is proposed. The overall layout of the RD15 is given out and the transition process from the helicopter mode to the airplane mode is designed. The lift system consists of a circular disk-wing with four retractable blades. The technology of individual blade control is adopted for flight control in hover and low speed flight. The tail is a vectored thrust duct propeller. It can provide the anti-torque in hover, and offer the multi-directional controls and propulsion drive for the airplane mode flight. The aerodynamic characteristics and key technologies in the transition process for this layout, including the nose up angle of disk-wing, the length of the blade, rotation speed, pitch angle and other parameters, are theoretically ana lyzed and experimentally tested. Calculation and experiments show that the shift process of the lift, the power and controls are smooth, and the designed scheme is feasible.     

STRAIGHTFORWARD MULTI-SCALE BOUNDARY ELEMENT METHOD FOR GLOBAL/LOCAL MECHANICAL ANALYSIS OF ELASTIC HETEROGENEOUS MATERIAL

A straightforward multi-scale boundary element method is proposed for global and local mechanical analysis of heterogeneous material.The method is more accurate and convenient than finite element based multi-scale method.The formulations of this method are derived by combining the homogenization approach and the fundamental equations of boundary element method.The solution gives the convenient formulations to compute global elastic constants and the local stress field.Finally,two numerical examples of porous material are presented to prove the accuracy and the efficiency of the proposed method.The results show that the method does not require the iteration to obtain the solution of the displacement in micro level.     

Bottom-Up Saliency Estimation Based on Redundancy Reduction and Global Contrast

A new algorithm for bottom-up saliency estimation is proposed. Based on the sparse coding model, a power spectral filter is proposed to eliminate the second-order residual correlation, which suppresses the global repeated items effectively. In addition, aiming at modeling the mechanism of the human retina prior response to high-contrast stimuli, the effect of color context is considered. Experiments on the three publicly available databases and some psychophysical images show that the proposed model is comparable with the state-of-the-art saliency models, which not only highlights the salient objects in a complex environment but also pops up them uniformly.     

The 3rd Asia-Pacfic Conference on Control and Measurement(APCCM’98)Will Be Held

The 3rd Asia-Pacfic Conference on Control and Measurement Sponsored by IEEE Shanghai Subsec-tion,Jiangsu Society of Aeronautics and Astronautics,Nanjing University of Aeronautics and Astronau-tics and the City University of Hong Kong will be held on August31 to September4,1998 in Dun-huang,China.APCCM Conference is the best chance for scholars,experts and engineers on control andmeasurement fields from the world to propose their academic insights,exchange their academic achieve-ments and enhance international cooperations.The purpose of of the APCCM’98 is to bring togetherscholars and experts engaging in control and measurement research to highlight new issues and contribu-tions of information technology for advances in industrial engineering.The topics of this conference arepresented as follows:control engineering,ideniification,modeling and system theory,specification andimplementation languages,controller synthesis,computer-aided design and simulation,algorithm anddeductive verification,fiber optica     

WIFI AIDED INTEGRITY IMPROVEMENT IN MEMS INS/GNSS INTEGRATION

The reliability of global navigation satellite system （GNSS） positioning degrades when satellite signals are interfered. Such degradation is hard to be deteced by a micro-electro mechanical system （MEMS） based inertial system（INS）/GNSS, integrating navigation system with a conventional Kalman filtering, which results in poten- tial integrity problem of the system. Hence, an algorithm combining wireless fidelity （WiFi） signal with a federa- ted Kalman filter （FKF） is proposed to identify the system integrity in dense urban navigation. The criterion of the system integrity detection is created followed by the derivation of the integrity coefficient. The field test shows that integrity changes can be captured by applying WiFi, and the maximum positioning error is reduced by 67~ without compensation of inertial sensors in integrity deterioration.     

LONG-TERM RIGOROUS NUMERICAL INTEGRATION OF NAVIER-STOKES EQUATION BY NEWTON-GMRES ITERATION

The recent result of an orbit continuation algorithm has provided a rigorous method for long-term numer- ical integration of an orbit on the unstable manifold of a periodic solution. This algorithm is matrix-free and em- ploys a combination of the Newton-Raphson method and the Krylov subspace method. Moreover, the algorithm adopts a multiple shooting method to address the problem of orbital instability due to long-term numerical integra- tion. The algorithm is described through computing the extension of unstable manifold of a recomputed Nagata~s lower-branch steady solution of plane Couette flow, which is an example of an exact coherent state that has recently been studied in subcritical transition to turbulence.     

Fast Numerical Solution to Forward Kinematics of General Stewart Mechanism Using Quaternion

The forward kinematics of the general Stewart mechanism is studied and a fast numerical method is presented.Quaternion is utilized to model the forward kinematics and the equations are merely a system of quadratic ones.The numerical method is a nice simplification of the Newton-Raphson method when applied to this system.A simulation of the movement control of the Stewart mechanism is accomplished,confirming the effectiveness of the proposed algorithm in real-time conditions.