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This paper considers the cooperative attack with multiple missiles on a stationary target and a two-step procedure is proposed. First the states of all missiles are guided to achieve minimum time consensus, and then, by using the proportional navigation guidance law with identical navigation gain each missile arrives at the target simultaneously or sequentially. Among which, the strategy to realize state consensus in minimum time is the focus of the issue. In the paper the coordination algorithms are developed based on bang–bang control strategy due to its simply structure, subsequently, it is proved that the time optimal consensus problem can be transformed into a parameter optimization problem. Besides, the proposed schemes are able to significantly reduce the number of parameters to be optimized and finally solve the problem with little computation cost. Different from the impact time control methods, the strategy for state consensus does not demand the information of time-to-go. Meanwhile only the initial values of missiles' states are required for the coordination algorithms which lead to less communication burden. Two different examples are provided to demonstrate the validity and efficiency of our proposed strategy. [ABSTRACT FROM AUTHOR]

In this paper, the hybrid model of Edge Potential Function (EPF) and Simulated Annealing Pigeon-inspired Optimization (SAPIO) algorithm is proposed to accomplish the target detection task for Unmanned Aerial Vehicles (UAVs) at low altitude. EPF can be calculated from the edge map of the original image and provide a kind of attractive pattern for the given target, which is conventionally exploited by the optimization algorithms. Pigeon-inspired Optimization (PIO) is a novel bio-inspired computation algorithm, which was inspired from the homing characteristics of pigeons. In this paper, the simulated annealing mechanism is adopted in our SAPIO algorithm for maximizing the value of EPF. A series of comparative experiments with standard Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Artificial Bee Colony Optimization (ABC) and PIO algorithms demonstrate the robustness and effectiveness of our SAPIO algorithm. Meanwhile, the proposed approach can guarantee accurate target matching. [ABSTRACT FROM AUTHOR]

In this paper, we conduct the design of the automatic carrier landing system (ACLS) for aircraft, which can integrates the aircraft flight control system, throttle control system, inertial navigation sensors, and shipboard AN/SPN-42 tracking radar and computer system to achieve fully automatic approach control to the carrier deck in all weather conditions. In order to obtain the better dynamic response of the longitude command, a novel control parameter design method is presented for the automatic carrier landing system. To overcome difficulties in the manual parameter adjustment task, the Lévy flight based pigeon-inspired optimization (PIO) algorithm is utilized by converting the parameter design problem to an optimization problem. To optimize control parameters in the H-Dot autopilot and the approach power compensation system, a weighted linear cost function in the time domain is adopted. Series of experiments are conducted to demonstrate the feasibility and effectiveness of our method. Comparative results indicate that our method is much better than other methods. With the help of the optimization algorithm, ACLS can have better performance in dynamic responses. [ABSTRACT FROM AUTHOR]

The six-degree-of-freedom (6-DoF) powered landing guidance problem usually is difficult to solve for several reasons. First, this problem coupled with low-frequency translational and high-frequency rotational equations of motion is naturally large-scale. Second, the constraints enforced into the problem are highly coupled, nonlinear and non-convex, which makes the problem quite sensitive to the initial value guesses. Third, the initial position, velocity and attitude of the landing vehicle is quite difficult to forecast due to the complex endo-atmospheric environment, thus the traditional guidance methods are difficult to obtain a predetermined trajectory. In this paper, for the first time we proposed a nonlinear homotopy interior-point optimization algorithm (NHOPT) for solving the 6-DoF powered landing guidance problem. Different from general external homotopy strategies, we embed the concept of homotopy methods into the barrier line-search interior-point method. Using vertical landing without position and velocity offset, it is easy to provide initial guesses, which we define as the initial homotopy subproblem. Then a class of related homotopy subproblems is solved along a homotopy path using the algorithm until the original problem is converged. Numerical results demonstrate that the algorithm has high performance in terms of convergence and computational efficiency. [ABSTRACT FROM AUTHOR]

Designing robust air traffic network is an ongoing research effort that seeks to improve the network robustness when one or more air routes are added to the existing network. We demonstrate that Laplacian energy is a fair and promising measure of network robustness based on a case study of a real air traffic network and extensive numerical experiments. Therefore, this paper aims at maximizing the Laplacian energy to enhance network robustness. The corresponding weighted Laplacian energy maximization problem is formulated as flight route addition problem to facilitate practical operations. Three methods are proposed to solve the flight route addition problem, including tabu search, greedy algorithm and integer program. Their trade-off between optimality performance and computational efficiency is demonstrated through the numerical results on a scale-free network. A case study on a real air traffic network is also included for further investigation. [ABSTRACT FROM AUTHOR]

The time delay of the pulsar integrated pulse profile relative to standard pulse profile is one of the important observations in X-ray pulsar-based navigation system, which accuracy directly affects the accuracy of the pulsar-based navigation system. In order to improve the measuring accuracy of the time delay of the pulsar integrated pulse profile and reduce computation complexity, a novel optimization algorithm based on sparse representation is proposed in this paper. This method firstly constructs a waveform-matched dictionary in accordance with standard pulse profile. On the basis of the dictionary, first order sparse coefficient vector that contains the phase information of time delay is used to represent measurement pulse profile. Then, the time delay for integrated pulse profile is calculated with the sparse coefficient by the matching pursuit algorithm. The proposed algorithm is validated with the real data observed by Rossi X-ray Timing Explorer. Comparing with the state-of-the-art fast Fourier transform algorithm, experiments show that the proposed algorithm can significantly reduce the impact of signal-to-noise ratio of the observed pulse profile on the measuring accuracy of the time delay. Even in a short observation time, the proposed algorithm can still maintain the high measuring accuracy. In addition, the measuring accuracy of the proposed algorithm can be further improved when the redundant dictionary is constructed with more sampling phase bins of the standard pulse profile. [ABSTRACT FROM AUTHOR]

In this paper a successful topology optimization of a centrifugally loaded aero-engine part is presented. For the topology optimization, which is a nonlinear problem due to several contact regions, a self-developed topology optimization algorithm in combination with a commercial FE-solver is used. The goal of the optimization was the reduction of stresses in the structure. [ABSTRACT FROM AUTHOR]

Trajectory optimization is essentially an optimal control problem (OCP) with highly nonlinear dynamic properties and complex constraints, and a critical part of spacecraft design. In this paper, a hybrid algorithm is proposed for automatic reentry trajectory optimization of reusable launch vehicle (RLV) without providing user-specified initial guesses and a priori knowledge about the optimal trajectory. The method combines the strong robustness and global optimization properties of hyper-heuristic whale optimization algorithm (HHWOA) with the efficient and accurate features of the Gauss pseudospectral method (GPM). HHWOA works as the first-stage optimizer aims to obtain an approximate solution to provide a high-quality initial guess for the GPM, while the GPM works as the second-stage optimizer aims to accelerate the search of the optimum neighborhood to obtain an accurate optimal solution. Additionally, to enhance the progress during the evolutionary process, HHWOA is equipped with opposition-based learning, differential evolution operators, chaotic map sequences and smoothing technique strategies. The utilization of such strategies can potentially smooth the flight trajectory and improve the global convergence of the algorithm, while the three OCPs have shown their superiority in HHWOA. In order to evaluate the performance of the hybrid algorithm, complex constrained RLV maximum cross-range reentry problems with three different path constraint scenarios are investigated. Furthermore, more discussion and experiments are likewise conducted to investigate the impact of the parameters on the performance of the algorithm. The results show that the proposed hybrid algorithm can be very effective in addressing RLV reentry trajectory optimization problems. [ABSTRACT FROM AUTHOR]

The Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm is one of the most successful continuous black-box optimization algorithms widely used in many fields. However, the algorithm would be computationally unaffordable due to the sharp increase in dimension and complexity of problems. To improve the computational efficiency and global optimizing ability, a competitive variable-fidelity surrogate-assisted CMA-ES (CVFS-CMA-ES) algorithm using data mining techniques is proposed in this paper. In the first data mining of CVFS-CMA-ES, the establishment method of the competitive variable-fidelity model (VFM) based on the fuzzy clustering algorithm is developed, aiming at making full use of high-fidelity information and focusing more on potential areas of design space. In the second data mining of CVFS-CMA-ES, the Lower Confidence Bound (LCB) method is modified by introducing the step size of CMA-ES to control the uncertain term adaptively, and the sample points are screened in each generation of CMA-ES based on the modified LCB method, which can make a balance between exploration and exploitation in the optimization process. The performance of CVFS-CMA-ES is compared with four known black-box optimization algorithms. Firstly, ten numerical examples of 10-dimensional and 30-dimensional benchmark functions are carried out, respectively. Moreover, a 20-dimensional engineering example of the aerospace variable-stiffness composite shell under combined loadings is studied in detail. Results of benchmark functions and engineering examples verify the high efficiency, robustness and high global optimizing ability of the proposed CVFS-CMA-ES in comparison to other algorithms. [ABSTRACT FROM AUTHOR]

Abstract: Inertial navigation system (INS) necessitates an alignment stage to determine the initial attitude at the very start. A novel alignment approach is devised by way of an optimization method, in contrast to the existing alignment methods, e.g., gyrocompassing and filtering techniques. This paper shows that the INS attitude alignment can be equivalently transformed into a “continuous” attitude determination problem using infinite vector observations. It reveals an interesting link between these two individual problems that has been studied in parallel for several decades. The INS alignment is heuristically established as an optimization problem of finding the minimum eigenvector. Sensitivity analysis with respect to sensor biases is made and explicit error equations are obtained for a special stationary case. Simulation studies and experiment tests favorably demonstrate its rapidness, accuracy and robustness. The proposed approach is inherently able to cope with any large angular motions, as well as high-frequency translational motions. By inspecting the constant initial Euler angles, it could alternatively be used to detect the existence of significant sensor biases. [ABSTRACT FROM AUTHOR]

Abstract: Path planning of Uninhabited Combat Air Vehicle (UCAV) is a rather complicated global optimum problem which is about seeking a superior flight route considering the different kinds of constrains under complex combat field environment. Artificial Bee Colony (ABC) algorithm is a new optimization method motivated by the intelligent behavior of honey bees. In this paper, we propose an improved ABC optimization algorithm based on chaos theory for solving the UCAV path planning in various combat field environments, and the implementation procedure of our proposed chaotic ABC approach is also described in detail. Series of experimental comparison results are presented to show the feasibility, effectiveness and robustness of our proposed method. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a strength Pareto evolutionary algorithm (SPEA) based approach is proposed for designing an integrated fuzzy guidance law which consists of three fuzzy controllers. Each of these controllers is activated in a region of the interception. The distribution of the membership functions and the rules are obtained by solving a nonlinear constrained multi-objective optimization problem where final time, energy consumption, and miss distance are treated as competing objectives. Tabu search is proposed to build a library of initial feasible solutions for the multi-objective optimization algorithm. In addition, a hierarchical clustering technique is implemented to provide the decision maker with a representative and manageable Pareto-optimal set without destroying the characteristics of the trade-off front. Moreover, a fuzzy-based mechanism is employed to extract the best compromise solution over the trade-off curve. The simulation results show that the proposed design technique was able to generate a missile guidance law with satisfactory performance with the existence of noisy measurements. [Copyright &y& Elsevier]

Abstract: Redundant thrusters are generally used for a reliable attitude control system. Also, redundant thrusters yield a better performance if they are used appropriately. In this paper, the authors propose an efficient redundancy management algorithm to reduce the fuel consumption. The algorithm is based on a linear programming problem which is a constrained optimization problem. For the algorithm, a cost function is defined as a quantity related to the fuel consumption for a maneuver. The independent variables are the thrusters'' on-times which are control input variables of a satellite dynamic model. The advantage of the proposed method is verified by numerical examples. The examples show that the proposed method consumes less fuel than an existing method for a given maneuvering command. A sub-optimal algorithm is also discussed for an onboard computation. The proposed algorithm is applied to two maneuvers: move-to-rest and rest-to-rest. This is verified by a numerical simulation. [Copyright &y& Elsevier]

For the optimization and algorithm design of high precision strapdown inertial navigation system (SINS), a systematic and fine attitude error analysis method is needed. To this end, a closed-loop method and an energy-level method are proposed to analyze the non-initial attitude estimation error of SINS. The closed-loop method is constructed from the basic angular velocity error equation in which angular velocity error is combined with angular velocity and rotation estimate to form a closed loop. This makes it the first time to realize the complete (theoretically unbiased) analysis of SINS rotation estimation error. The energy-level method is constructed from the energy level of angular motion which is expressed as the series expansion of rotation estimate under the defined motion such as coning and maneuvering motion. This enables us to finely analyze the error of any existing multi-sample coning algorithm based on Taylor-series method. The theoretical basis, establishment process and instructions of two methods are given in order in the paper. The detailed process of proof and reasoning of the two methods are given in Appendixes A, B, C and D. Several cases of attitude error analysis based on the proposed methods and simulation examples verify the correctness of the analysis results from the proposed method and the applicability of the proposed method, which provides a systematic analysis tool for the design of SINS and attitude algorithm. [ABSTRACT FROM AUTHOR]