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The use of hydrogen as a fuel is a promising way to reduce the emissions of civil aviation but it requires the development of wholly new injectors for the combustion chamber. Thanks to the increase in available computing power, the application of optimization techniques combined with CFD computations is now possible to develop these injectors. Among the optimization approaches, Bayesian optimization is particularly relevant when the objective functions and constraints of the optimization problem are expensive to evaluate which is the case in CFD-based optimization. Besides, the use of a multifidelity strategy allows to reduce the simulation cost of the Bayesian method. Therefore, this paper investigates the application of a multifidelity and multi-objective Bayesian approach to improve the performances of a laboratory swirl injector using hydrogen and operating in conditions close to industrial targets. This optimization study combines LES simulations as high-fidelity model with 2D RANS simulations as low-fidelity. • A multifidelity and multi-objective optimization is performed for an H 2 -powered laboratory injector. • The operating point is representative of the cruise conditions of a SMR aircraft. • Large-eddy simulations are integrated in the optimization approach. • This optimization study is compared to a monofidelity approach and to two single-objective approaches. [ABSTRACT FROM AUTHOR]

This paper proposes a novel methodology and its software implementation, called KADMOS (Knowledge- and graph-based Agile Design for Multidisciplinary Optimization System), to increase the agility of design teams in collaborative Multidisciplinary Design Analysis and Optimization (MDAO). Agility here refers to the ease and flexibility to assemble, adjust and reconfigure MDAO computational systems. This is a necessary feature to comply with the complex and iterative nature of the (aircraft) design process. KADMOS has been developed on the notion that a formal specification of an MDAO system is required before proceeding with integration of the executable workflow. A thorough formulation of the system becomes essential when such system is built on the many contributions of large, heterogeneous design teams. KADMOS can automate the generation of such formulations through a graph-based methodological approach. The graph syntax and manipulation algorithms form the core content of this paper. First, a simple MDAO benchmark problem is used to illustrate KADMOS's working principles. Second, a wing aerostructural design case is discussed to demonstrate KADMOS's capabilities to enable collaborative MDAO on large problems of industry-representative complexity. Next to its graph-theoretic foundation, KADMOS makes use of two data schemas: one containing the parametric representation of the product being designed and a second to store the achieved formulation of the MDAO system. The latter enables the interchangeable use of different process integration and design optimization platforms to automatically integrate the generated MDAO system formulation as an executable workflow. The proposed approach has been estimated to be capable of halving the time typically required to set up and iteratively reconfigure a complex MDAO system, while allowing discipline experts and system architects to maintain constant oversight and control of the overall system and its components by means of human-readable dynamic visualizations. [ABSTRACT FROM AUTHOR]

In order to realize accurate and secure homing control of the parafoil delivery systems under multiple constraints, an in-flight compound homing methodology is designed in this paper. It consists of the modeling of parafoil delivery systems, a trajectory optimization method based on quantum genetic algorithm, a control approach based on active disturbance rejection control. Firstly, the precise model of parafoil delivery system is established by the actual flight data. Then, in order to restrain the wind disturbance, a novel wind identification method is introduced into the trajectory optimization method. Combining the wind identification method and terrain-matching, the proposed trajectory optimization method highlights its improvement from the traditional homing approach. It can not only obtain an optimal trajectory in windy environment, but also maintain the essential advantage of the multiphase homing methodology – great realizability. Then, the ADRC controller is designed. It is applied to resist the varying wind and other disturbance. At last, the hardware-in-the-loop simulation results show that the proposed compound homing methodology can achieve excellent effects both in the trajectory optimization and trajectory tracking. In this paper, the proposed methodology is also compared with the trajectory optimization based on chaos particle swarm optimization method and the PID technology of the trajectory tracking. The results also present huge improvement and wide application prospect. [ABSTRACT FROM AUTHOR]

The paper presents a hybrid model method based on proper orthogonal decomposition (POD) and its application to aerodynamic design optimization of the last stage of a low-speed 4.5-stage compressor preliminary designed by an in-house design procedure. The stator blade of the last stage is redesigned to reduce the flow separation zones that appear in the suction-endwall corners. POD, recognized as a useful tool to construct the reduced order models, is employed to reconstruct the flow solutions with respect to a given aerodynamic shape in the system space. The flow solutions are described in a weighted summation form of the basis modes. In order to improve the description performance, the weights of basis modes are obtained by a nonlinear regression method in the present paper, rather than the widely used linear regression. The implementations of POD-based hybrid model are presented and the description performance is evaluated and validated firstly. Then the optimization is performed by using the hybrid model through modifying the spanwise stagger angle and the camber of stator blade, while maintaining the mass flow rate. The effects of stagger angle change and camber modification on the performance improvements are presented in detail, demonstrating that the POD-based hybrid model is an easy implemented but valuable and efficient method in aerodynamic design optimization. Finally, data mining technique is employed to discover the design knowledge of the optimization. POD, due to its ability on extracting the dominant characteristics, is used to illustrate the primary basis modes of design parameter changes, further demonstrating the effects of blade redesign on the performance variations. [ABSTRACT FROM AUTHOR]

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]

The aim of the paper is to establish and demonstrate the significant capacity and performances of a well predictive numerical environment, developed for multidisciplinary design optimization (MDO) purposes, which presents one more contribution in cases of fluid–structure interaction (FSI) numerical modeling. Numerical modeling of fluid–structure interaction was conducted through closely coupled aerodynamic and structural computational domains, with very good overall computational reliability and accuracy. Various available experimental results, which were obtained mostly for calibration purposes, have been used for computational fluid dynamics (CFD) and computational structural mechanics (CSM) validation and verification, in order to assure that numerical optimization could be carried out with acceptable accuracy. The numerical optimization procedure was applied on the short range ballistic missile fin configuration, which was developed for scientific and internal experimental, testing and calibration purposes in Military Technical Institute (VTI) in Belgrade. The proposed monolithic, multimodular and on commercial code-based numerical environment, with adopted multipoint regimes and multicriteria settings was used for aerodynamic–structural optimization. The multidiscipline aerodynamic shape optimization, with respect to predefined objectives and constrains, was carried out in order to achieve global improvement of initial aerodynamic–structural responses of the mentioned configuration. The multidisciplinary feasible method proposed in this paper, is a single level method driven by an embedded surrogate-based evolutionary optimizer. The developed algorithm enabled an increased number of feasible optimal geometries of fin, while its special feature was the overall improvement of the missile initial geometry, with decreased costs of experimental and numerical resources. A special challenge of this research was to overcome scaling between available the wind tunnel missile model geometry, used for aerodynamic experiments, and the real fin geometry model, used for static structural experiments. [ABSTRACT FROM AUTHOR]

Actuator configuration and optimization are of great significance in active control of vibration and noise. This paper proposes an actuator position optimization method for active control system with uncertainties. The uncertain parameters were modeled by interval numbers. Based on interval analysis, the boundaries of eigenvalues of the controllability grammian are obtained and uncertainty propagation analysis method for actuator configuration is presented. Mathematical operation on the eigenvalues of controllability grammian is considered to be the optimization criterion in order to maximize the norm of controllability grammian. Both nominal value and radius of the performance index are considered in the optimization model proposed in this paper. The uncertain optimization model for actuator configuration is transformed into a deterministic optimization model based on weighted processing. Node index number on the finite element mesh is selected as design variable. Since the objective function is non-convexity, genetic algorithm is adopted to get the optimal solution. The feasibility of the robust optimization method is demonstrated by two examples. [ABSTRACT FROM AUTHOR]

In this paper, an optimized positive feedback based control approach is developed for unwanted resonant vibration attenuation of flexible lightweight aerospace structures. A modified form of the positive velocity feedback control (MPVF) is implemented, where the resonant compensator of each mode is enhanced with a parallel lossy integrator. For multi-mode control purpose and to control n -modes, n sets of parallel compensators are required. A focus of this paper is on development of effective gain optimization methods in order to improve the multi-mode suppression performance of the vibration controller. The optimization problem is addressed via the M -norm optimization method and the Linear Quadratic Regulator (LQR). A flexible clamped-free beam is considered as a sample aerospace structure, and the controller verification is performed experimentally. Results show that vibration velocity is well reduced, which is important in preventing fatigue failures in aerospace structures. The LQR-optimized MPVF provides a compensated result between vibration amplitude reduction and the consumed control power. The M -norm approach, on the other hand, aims to directly suppress each mode based on the assigned weighting coefficients. Since an M -norm-optimized MPVF controller focuses on modes with higher amplitudes of velocity, it provides a higher level of vibration suppression than LQR-optimized method. [ABSTRACT FROM AUTHOR]

The maneuver characteristics of hypersonic glide vehicles are analyzed from the perspectives of maneuver modes and maneuverability in this paper. Longitudinal skip maneuver mode, equilibrium glide mode, lateral weaving maneuver mode and lateral turning maneuver mode are adopted to describe the maneuver process of hypersonic glide vehicles from the view of shapes of maneuver trajectories, and the corresponding control variables in each mode are deduced. Flight constraints including heating rate, aerodynamic press, load factor are converted to be constraints putting on angle of attack to validate whether the vehicle can realize the assigned maneuver. In addition, some performance indices to evaluate the maneuverability are analyzed in. The study in the paper can help to deepen the understanding of the maneuver characteristics of hypersonic glide vehicles and provide guide to trajectory planning and optimization. [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 the Mercury orbit insertion (MOI) in January 2022 of the ESA mission BepiColombo consisting of 15 manoeuvres is optimised. To maximise the mass in the target orbit, the gravity losses of the 15 burns are minimised by distributing the burns as evenly as possible. However, severe Sun aspect angle constraints make the optimisation problem difficult to solve. The NLP-solver SNOPT is used for the optimisation. The uncertainty in Mercury's gravity field is still high, such that its influence on the MOI was evaluated in several simulations of Mercury's gravity field. Since the target orbital parameters are still discussed their influence on the MOI Δ V -budget is investigated. The results indicate that the reference case was selected adequately. Since changes in arrival date, scientific orbit, instruments and strategies until BepiColombo's launch in 2015 are possible, this paper covers a wide range of possible future arrival options. [ABSTRACT FROM AUTHOR]

Black-box optimization methods like Bayesian optimization are often employed in cases where the underlying objective functions and their gradient are complex, expensive to evaluate, or unavailable in closed form, making it difficult or impossible to use traditional optimization techniques. Fixed-wing drone design problems often face this kind of situations. Moreover in the literature multi-fidelity strategies allow to consistently reduce the optimization cost for mono-objective problems. The purpose of this paper is to propose a multi-fidelity Bayesian optimization method that suits to multi-objective problem solving. In this approach, low-fidelity and high-fidelity objective functions are used to build co-Kriging surrogate models which are then optimized using a Bayesian framework. By combining multiple fidelity levels and objectives, this approach efficiently explores the solution space and identifies the set of Pareto-optimal solutions. First, four analytical problems were solved to assess the methodology. The approach was then used to solve a more realistic problem involving the design of a fixed-wing drone for a specific mission. Compared to the mono-fidelity strategy, the multi-fidelity one significantly improved optimization performance. On the drone test case, using a fixed budget, it allows to divide the inverted generational distance metric by 6.87 on average. [ABSTRACT FROM AUTHOR]

This paper aims to develop a computationally efficient sequential convex programming algorithm for a class of nonlinear systems. A Chebyshev collocation discretization (CCD) technique is proposed that transforms the continuous convex optimization problem into a finite-dimensional discrete optimization problem. The CCD technique uses Chebyshev polynomials to construct a closed-form approximate solution to the convex dynamic equation. Moreover, it establishes a linear mapping between the control inputs and the system states. This enables the constraints of the dynamics equations to be externalized from the optimization problem and computed using efficient linear equation solving algorithms. On the one hand, this strategy significantly reduces the number of constraint equations and optimization variables, improving the speed of the optimizer. On the other hand, it preserves the path constraints on the system states in the optimization problem. A numerical simulation example of the perching maneuver for a fixed-wing unmanned aerial vehicle is presented to validate the efficiency of the algorithm. The result shows significant improvements in both speed and accuracy compared to the Euler discretization, and greatly improves the solution speed while maintaining almost the same solution accuracy compared to the Gauss pseudospectral optimization method. [ABSTRACT FROM AUTHOR]

Abstract: This paper addresses the pretension optimization of a double-ring deployable cable net antenna. PSO (particle swarm optimization) is employed and combined with TS (Taboo search) to obtain the optimal pretension. In addition, the optimized pretension of all cable net segments is divided into different groups to satisfy engineering applications. Finally, to verify the optimal solutions with the algorithm investigated in this paper, a scaled model was manufactured and relevant precision tests were taken, which illustrate that the improved PSO makes sense for the pretension optimization of cable net antennas. [Copyright &y& Elsevier]

Abstract: Multiple antenna transmission methods are currently being developed all around the world for evolving 3G wireless standards. Space–Time Trellis Code (STTC) has been proven to use transmit diversity efficiently. It effectively exploits the effects of multipath fading to increase the information capacity of the multiple antenna transmission systems. STTC is a channel coding technique that maximises the ‘distance’ between different symbol matrices such that the probability of transmission errors are decreased when transmitting redundant symbol or in other words, to maximise the minimum determinant. Maximising the minimum determinant is equivalent to obtaining optimal generator matrix G. Instead of using state diagrams, optimal generator matrix G discussed in this paper is obtained using an improved algorithm which is based on Lisya tree structure. Optimal generator matrix G in this paper has a minimum determinant of 48 which is the highest coding gain obtained so far. [Copyright &y& Elsevier]

The objective of an aircraft engine control system is to provide required thrust as well as protection against the physical and operational limits. Model predictive control (MPC) technique is an attractive approach that incorporates input/output constraints in its optimization process to fulfill the control requirements of an aircraft engine. However, due to heavy computational burden of MPC, the real-time implementation of this algorithm is challenging and selection of MPC design parameters is crucial. This paper presents the design and hardware implementation of MPC algorithm as well as its HIL testing for turbofan engine control. In addition, a feedback correction technique is employed to compensate the effect of plant-model mismatch. For this purpose, a thermodynamic nonlinear engine model is firstly developed. A multivariable model predictive controller is then designed based on a discrete-time linearized state-space model where the horizons are obtained through a genetic algorithm optimization procedure. Moreover, this controller is implemented on an appropriate hardware taking the real-time aspects into account. Finally, an HIL platform is developed for testing the turbofan engine electronic control unit (ECU). For this purpose, a multi-level throttle command is applied to the ECU and the performance of the engine is evaluated. The results indicate that the controller satisfies all the engine constraints, and confirm the successful software and hardware implementation of the control algorithm in real-time. [ABSTRACT FROM AUTHOR]

The polynomial chaos (PC) theory and direct collocation method have been integrated to solve a lot of robust trajectory optimization problems. However, the computational cost and memory consumption increase significantly with the increase of the dimension of uncertain factors, and the nonlinearity of dynamic equations. To address this issue, a novel robust trajectory optimization procedure combining PC with the convex optimization technique is proposed in this paper. With the proposed procedure, the trajectory optimization can be implemented with high accuracy and efficiency, by taking advantage of the high accuracy of PC in addressing UP for highly nonlinear dynamics and the high efficiency of convex optimization in solving optimal control. The proposed robust trajectory optimization procedure is applied to two examples and compared with the existing method employing PC and the pseudospectral method. The results show that the proposed procedure can obtain highly accurate results similar to the existing method. Moreover, with the increase of random dimension, the optimal trajectory can still be generated very efficiently without significant increase of computational cost. These results demonstrates the effectiveness of the proposed procedure. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents an optimal control strategy suitable for investigating the mission performance of a spacecraft equipped with a NASAʼs Evolutionary Xenon Thruster. Using an accurate mathematical model of the propulsion system, based on both experimental data and a semi-empirical schematization of the power generation system, the minimum-time rendezvous problem with various celestial bodies is investigated through an indirect approach. Unlike a conventional approach, in which the thruster power is varied with continuity within a given range, here the solar electric propulsion system is described using a finite number of operation points, characterized by different pairs of thruster input power and propellant mass flow rate. The model includes the possibility of obtaining coasting phases along the spacecraft trajectory. The optimal control strategy is found for a three-dimensional problem in which the actual shape and relative orientation of the initial and final orbits are taken into account. The paper presents the simulation results for a number of minimum-time trajectories towards inner planets (Mercury, Venus, and Mars) and three Near Earth Asteroids (NEAs) of particular interest for the scientific community: 1998 KY-26, 99942 Apophis, and 2007 VK-184. [Copyright &y& Elsevier]

Abstract: Based on the curvilinear grids, grid-optimized upwind dispersion-relation-preserving (GOUPDRP) finite difference schemes are studied in the paper. GOUPDRP finite difference schemes can automatically resolve waves with much shorter wavelengths and need not consider the filter or the explicit dissipation terms, and have much smaller dispersive and dissipative errors. Recently, GOUPDRP schemes on the non-uniform Cartesian meshes have been developed and tested by authors of this paper, others optimized upwind finite schemes just only on the uniform Cartesian grids also have been investigated; nevertheless, practical problems existing in aeroacoustics are unusually restricted to the uniform or non-uniform Cartesian grids, the corresponding computational grids could be curvilinear ones owing to the different complex geometries. In contrast to other optimized upwind schemes on the uniform Cartesian grids, GOUPDRP schemes can be easily extended to the curvilinear meshes. In order to determine the optimization coefficients of GOUPDRP schemes on the curvilinear grids, a general curvilinear transformation is introduced from physical domain to computational domain. Mathematic formulations related to GOUPDRP schemes on the curvilinear meshes are also derived in detail in the paper. To demonstrate the effectiveness of GOUPDRP schemes, benchmark problems are solved using the curvilinear grids; calculated solutions are compared with their analytical counterparts. Numerical results show that GOUPDRP schemes on the curvilinear grids are highly efficient and relatively potential to solve practical problems in aeroacoustics. [Copyright &y& Elsevier]

Abstract This paper aims at the trajectory optimization problem of an accompanying satellite in the space station under multi-obstacles by model predictive control (MPC) with successive linearization. This optimization problem is reformulated as a mixed integer second-order cone programming (MISOCP) problem by considering the obstacles avoidance, and various physical constraints. More specifically, a novel variation weight cost function is established with the distance information of the accompanying satellite and obstacles, such that a multi-objective optimization performance is achieved by incorporating fuel consumption and time expenditure further. Through the successive linearization of the MPC scheme, the satisfaction of the multiple constraints is established. These results lead to a feasible solution with harsh constraints as well as the fast convergence to the optimal solution. The obstacles avoidance constraints are first implemented through the appropriate selection of initial values, and then solved by successive approximation with the MPC framework for the convergence of solution. Numerical simulation has been conducted to demonstrate the effectiveness and applicability of the proposed method. [ABSTRACT FROM AUTHOR]

The aero-engine performance optimization control is to find the appropriate control variables to maximize or minimize the comprehensive index of one or more performance parameters while ensuring its safe operation. Traditional performance seeking control is mainly based on indirect control, with complex structure and large error. This paper develops an HNN (Hopfield Neural Network)-based generalized predictive control strategy for turbofan engine direct performance optimization. With a Hopfield network as the optimization algorithm and a CARIMA model based on generalized prediction criterion as the optimization model, an optimal controller is designed. Together with the identification module and the self-adaptive module, an integrated performance optimization control system is formed. Namely, the adaptive model calculates the unmeasurable performance parameters of the engine for the CARIMA model identification. Then HNN performs the iterative operation to obtain the optimal control variables for a given objective. The main contribution of this paper is to propose a direct performance control structure to avoid the nonlinear conversion of control reference; meanwhile, the linear CARIMA model is combined with HNN algorithm to build a neural network for solving constrained nonlinear programming problems, and mutation operation is introduced to improve its global search capability, which is evident in the minimum turbine temperature optimization mode. The method is evaluated on a turbofan model from the aspects of model verification, performance optimization control simulation at multiple operating points and under degeneration conditions. The results confirm the satisfactory performance of the proposed strategy at the selected points and conditions. And compared with the traditional algorithms, the computer calculation time is greatly shortened. [ABSTRACT FROM AUTHOR]

In the existing closed-loop cooperative guidance approaches for salvo attack of multiple missiles, the multiple constraints and time-variant velocity basically cannot be effectively considered. Therefore, two closed-loop cooperative guidance methods are developed in this paper, by employing the efficient convex optimization technique and receding horizon control (RHC) strategy. During each guidance cycle of RHC, the system coordination target is updated and then broadcasted to each missile as a constraint. Subsequently, the convex optimization technique is utilized to solve the multi-constraint optimal proportional guidance problem of each missile online to achieve the consensus on time-to-go among missiles. Simulation results show that for three cases with different conditions of velocity, the cooperative simultaneous attack under multiple constraints can be effectively carried out using each of the two proposed cooperative guidance laws, which verify their effectiveness and feasibility. [ABSTRACT FROM AUTHOR]

This paper explores the necessary and appropriate level of detail that is required to describe the structural geometry of aircraft wings accurately enough to predict the mass of the main load-carrying wing structure to an acceptable level of accuracy. Four different models of increasing structural fidelity are used to describe the wingbox structure of a realistic real-world aircraft wing. The wingbox of the NASA Common Research Model served as a test model for exploring and analyzing the trade-off between the granularity level of the wingbox geometry description under consideration and the computational resources necessary to achieve the required degree of accuracy. The mass of metallic and composite wingbox configurations was calculated via finite element analysis and design optimization techniques. The results provided an insight into the competence of certain wingbox models in predicting the mass of the metallic and composite primary wing structures to an acceptable level of accuracy, and in demonstrating the relative merits of the wingbox structural complexity and the computational time and input efforts for achieving the required level of accuracy. [ABSTRACT FROM AUTHOR]

A new indirect resolution method of an optimal control problem is proposed in this paper. And the optimization of the spacecraft low-thrust rendezvous with the fuel-minimum index to a safe region under the collision avoidance constraints is investigated. The objective is to minimize the fuel consumption in a power-limited low-thrust system, which leads to a bounded continuous control. The number of thrust arcs is unknown and the terminal positions in the rendezvous' safe region are unfixed for this optimization problem. The indirect resolution method of the optimal control employs deterministic interval analysis and gradient-based method to obtain the initial guess of the co-state variables. The interval analysis is used to sufficiently split, contract and clip the initial search space. And the gradient-based method is to determine the initial guess for each remained sub-space. Aiming at a low-thrust control system with the upper bound of acceleration of 5e −4 m/s 2 , numerical results are given to validate the proposed optimization method. [ABSTRACT FROM AUTHOR]

This paper describes a novel lateral guidance law for an unmanned aerial vehicle using nonlinear receding horizon optimization and shows its flight test results. The guidance law uses an extended Kalman filter which estimates steady wind velocities in order to follow a pre-specified reference path defined in a ground-fixed coordinate system. The guidance law can be applied to arbitrary reference path as long as the path is represented as a differentiable function of x and y in a ground-fixed coordinate system. A small-scale research vehicle developed by the Japan Aerospace Exploration Agency is used for flight tests, and the results demonstrate the high guidance performance of the proposed method. [ABSTRACT FROM AUTHOR]

Unmanned Air Vehicles (UAVs), which have been popular in the military context, have recently attracted attention of many researchers because of their potential civilian applications. However, before UAVs can fly in civilian airspace, they need to be able to navigate safely to their goal while maintaining separation with other manned and unmanned aircraft during the transit. Algorithms for autonomous navigation of UAVs require access to accurate information about the state of the environment in order to perform well. However, this information is often uncertain and dynamically changing. In this paper, a Grey Wolf Optimization (GWO) based algorithm is proposed to find the optimal UAV trajectory in presence of moving obstacles, referred to as Intruder Aircraft (IAs), with unknown trajectories. The solution uses an efficient Bayesian formalism with a notion of cell weighting based on Distance Based Value Function (DBVF). The assumption is that the UAV is equipped with the Automatic Dependent Surveillance-Broadcast (ADS-B) and is provided with the position of IAs either via the ADS-B or ground-based radar. However, future trajectories of the IAs are unknown to the UAV. The proposed method is verified using simulations performed on multiple scenarios. The results demonstrate the effectiveness of the proposed method in solving the trajectory planning problem of the UAVs. [ABSTRACT FROM AUTHOR]

This paper presents the results from a study to design an adaptive shock control bump for a transonic aerofoil. An optimisation framework comprising aerodynamic and structural computational tools has been used to assess the performance of candidate adaptive bump geometries based on a novel surface-pressure-based performance metric. The geometry of the resultant design is a unique feature of its adaptivity; being strongly influenced by the (passive) aerodynamic pressure forces on the flexible surface as well as the (active) displacement constraints. This optimal geometry bifurcates the shock-wave and carefully manages the recovering post-shock flow to maximise pressure-smearing in the shock-region with only a small penalty in L / D for the aerofoil. Short adaptive bumps (with small imposed displacements) generally perform better than taller ones, and maintain their performance advantage for a wide range of bump positions, suggesting good robustness to variations in shock position, which are an inevitable feature of a real-world flight application. Such devices may offer advantages over conventional (fixed geometry) shock control bumps, where optimal performance is achieved with taller devices, at the expense of poor robustness to variations in shock position. [ABSTRACT FROM AUTHOR]

This paper introduces a novel data-driven risk metric and assessment method for UAVs operating in environments typically encountered in civilian applications. A truly “data-driven risk measure” is derived through a probabilistic formulation that not only accounts for the intrinsically stochastic nature of the considered environmental factors (such as weather and signal strength), but also incorporates extrinsic prediction uncertainties originating from the geographical sparsity of data collection sources. We present a data-driven modeling of the stochastic environmental factors using Gaussian process-based function approximations. Notably, the proposed mathematical definition of the risk metric is based on the probabilistic predictions of such a Gaussian process model and introduced through a path-integral formulation. The problem of minimizing operational risk for multiple UAVs in partially unknown environments is then defined in a multicriteria optimization framework to address the trade-off between the path-integral risk measure and classical path-efficiency (distance). We show that such approach can be embedded into current standard risk assessment methods which could be easily integrated into UAVs traffic management initiatives. We analyze the results through a number of simulations, including realistic scenarios. [ABSTRACT FROM AUTHOR]

The paper presents a numerical framework for the aerodynamic analysis of aircraft wings in transonic cruise and take-off/landing compatible with preliminary and conceptual design phase requirements based on the Non-Linear Vortex Lattice Method (NL-VLM). The purpose of this work is to demonstrate the applicability of the VLM-2.5D RANS approach for aircraft design optimization. The algorithm captures wing sweep effects, important in the transonic regime and near C L m a x conditions, by a stripwise viscous-inviscid coupling strategy with an infinite-swept wing (2.5D) Reynolds-Averaged Navier–Stokes (RANS) solver. Aerodynamic forces are evaluated through spanwise integration of the 2.5D RANS solutions and a trefftz-plane analysis of the VLM solver. The framework allows calculations of single and multi-element configurations without modifying the VLM mesh. A novel C L m a x criteria is proposed based on recently observed stall-cells patterns that captures C L m a x , α m a x and the spanwise location of the stall, which represent important design parameters. The applicability of the framework to aircraft design is demonstrated by embedding the analysis tools into a gradient-free Covariance Matrix Adaptation Evolution Strategy. After a verification phase, validation is performed on high-speed, high-lift and combined high-speed/high-lift optimizations cases. In particular, the capability of the numerical algorithms towards multi-topology optimization is demonstrated. [ABSTRACT FROM AUTHOR]

In this paper, a novel stochastic gradient particle swarm optimization (SGPSO) algorithm is proposed, which combines the high-efficiency of gradient search with the randomness of particle swarm search. By adjusting the stochastic gradient obtained by the best historical positions of adjacent two generations, the proposed algorithm can effectively overcome the problems of premature convergence and poor accuracy of standard particle swarm optimization (PSO). Due to the capabilities of rapidity, optimality and adaptability, the proposed algorithm is applied as a global optimization approach to rapidly generate feasible and smooth entry trajectories for hypersonic glide vehicles with highly constraints. Under the constraints of Earth's rotation and oblateness, the entry trajectory planning model is established. By parameterizing the control variables including angle of attack (AOA) and bank angle, the entry trajectory optimization problem is then converted to a multi-parameter optimization problem, which can be solved by the proposed SGPSO algorithm. Considering Common Aero Vehicle (CAV) model, the simulations show that the proposed algorithm has better performance on optimization speed, stability and solution optimality than those of the classical methods, and it can realize the rapid optimization of entry trajectory for hypersonic glide vehicles. [ABSTRACT FROM AUTHOR]

This paper proposes an intelligent self-organized algorithm (ISOA) to solve a cooperative search-attack mission planning problem for multiple unmanned aerial vehicles (multi-UAV). This algorithm adopts the distributed control architecture which divides the global optimization problem into several local optimization problems. Each UAV is able to solve its own local optimization problem, and then make the optimal decision for the multi-UAV system through the information exchange among UAVs. The search-attack mission planning process is divided into two phases, the one is waypoints generation under constraints of UAV's maneuverability, collision avoidance and maximum range, the other is path generation which takes account of the threat avoidance. In the first phase, an improved distributed ant colony optimization (ACO) algorithm is presented to carry out the mission planning and generate waypoints. Considering the range constraint of UAV, a new state transition rule is designed to guide UAV back to its initial point within the maximum flight range. In the second phase, Dubins curve is employed to smoothly connect the waypoints generated by the ACO. As for the unexpected threats during the flight, an online threat avoidance method is proposed to replan the paths. Finally, simulations are carried out to analyze the convergence performance, external responsiveness and internal scalability of the proposed ISOA for the multi-UAV search-attack mission planning problem. [ABSTRACT FROM AUTHOR]

With growing interest in low-cost, high performance and short time-to-flight satellites, the idea of using multi-purpose satellite platforms has drawn many attentions. However, under certain conditions in which the required capability of a mission is slightly more than the capabilities of a specified variant of a multi-purpose platform, it must jump to the next higher capability variant. Correspondingly, lots of unnecessary performance or capability might be imposed. In this paper, the main goal is to minimize the wasted performance in the design of an Earth Observation (EO) satellite based on a multi-purpose platform. To this end, a dynamic simulation-based model has been developed with the capability of simulating the satellite performance characteristics, EO mission requirements and supportable performance by the platform under various circumstances and throughout the satellite lifetime. To minimize the wasted performance we face with an optimization problem containing a complicated non-linear, non-convex and multi-modal design space which is enveloped by constraints of the mission requirements and supportable performance of the platform. A novel criterion as an objective function has been introduced aiming at the incompatibility reduction between the capabilities of a certain multi-purpose platform variant and the EO mission requirements. This objective function considers all the performance variations during the entire mission lifetime rather than solely the worst case. Through this, the mission and orbital characteristics have been determined by which there would be no need to jump to the higher performance variant in order to satisfy the EO mission requirements. [ABSTRACT FROM AUTHOR]

The classical unscented Kalman filter (UKF) requires prior knowledge on statistical characteristics of system noises for state estimation of a nonlinear dynamic system. If the statistical characteristics of system noises are unknown or inaccurate, the UKF solution will be deteriorated or even divergent. This paper presents a novel adaptive UKF by combining the maximum likelihood principle (MLP) and moving horizon estimation (MHE) to overcome this limitation. This method constructs an optimization based estimation of system noise statistics according to MLP. Subsequently, it further establishes a moving horizon strategy to improve the computational efficiency of the MLP based optimization estimation. Based on above, a new expectation maximization technique is developed to iteratively compute the MLP and MHE based noise statistic estimation by replacing complex smoothed estimates with filtering estimates for further improvement of the computational efficiency. The proposed method can achieve the online estimation of system noise statistic and enhance the robustness of the classical UKF. The efficacy of the proposed adaptive UKF is demonstrated through simulations and practical experiments in the INS/GPS integrated navigation. [ABSTRACT FROM AUTHOR]

In order to explore the drag reduction performance, an experimental and computational research effort for aero engine blade with micro-texture is undertaken. An optimized drag reduction model on blade with micro-textures is established for this effort in this paper. First, a new method is proposed to determine the placement position of micro-texture on blade surface through the analysis of flow domain around blade surface and geometric model of micro-textures on blade surface is established based on the proposed method. Then the section shape of micro texture with the better drag reduction performance is determined by FEA (Finite Element Analysis) simulation for the established geometric model of micro-textures on blade surface. Finally, an optimized method for drag reduction performance of micro-texturing size is proposed and an optimized drag reduction model of textured blade is established. The simulation experiments for drag reduction performance of un-textured blade and textured blade with optimized micro-texture is conducted. The comparison results show that the established drag reduction model of textured blade has better drag reduction performance than that of un-textured blade and verify that the proposed methods to determine the placement position of micro-texture on blade surface and to optimize drag reduction performance of textured blade are satisfied. [ABSTRACT FROM AUTHOR]

This paper presents a unified framework for addressing the stability analysis of the hovering condition for a multirotor vehicle. By linearization about a trim condition, a decoupled model made of four dynamic modes is derived. A set of stability derivatives is obtained and the effect on natural dynamic stability of design parameters such as arm dihedral and rotor tilt angles is analyzed. A numerical test case is finally discussed in support of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper investigates an optimal small satellite orbit design problem subject to the atmospheric drag uncertainty, and the small satellite is assumed to have no orbital control systems due to volume and mass constraints. The optimization objective of maximum target observation time is considered, which is constrained by the priority and minimum observation duration of each target site. Given the adverse impacts of atmospheric drag uncertainty on the designed orbit, the optimal solution is expected to maximize observation duration for given target sites while being less sensitive to substantial variations of atmospheric coefficients involved in atmospheric drag modeling. As a result, a robust orbit design approach is proposed by combining the robust optimization model with multi-objective optimization algorithm. Finally, the Monte Carlo simulation results are provided to demonstrate that the robust orbit solutions are more reliable compared to the nominal orbit solution designed from the traditional single-objective stochastic optimization algorithm. [ABSTRACT FROM AUTHOR]

Reducing the burden of the remaining actuators through decreasing the performance gracefully is an important field in active fault tolerant control. According to the literature, two important points have been identified in the works considering graceful performance degradation: 1) using single-objective optimization, 2) assuming an engineering insight into the performance of the faulty system. This paper has two contributions: First, it is shown that in some cases, single-objective optimization may not be able to provide a satisfactory solution for the problem. Second, a new systematic and general method is proposed to remove the need for the engineering insight. The proposed method is based on multi-objective optimization. Attitude tracking of a faulty spacecraft is considered as a case study. Simulation results show the advantages of the proposed methodology. [ABSTRACT FROM AUTHOR]

This paper proposes a fast real-time estimation algorithm which is inspired by the philosophy of the generalized model predictive static programming and solves the three-dimensional wind estimation problem in an optimal control manner. The wind estimation problem is transformed into a static optimization problem featuring a highly efficient solving framework. The proposed algorithm estimates the three-dimensional wind components without considering any hypothesis on the structure of the wind, and without relying on airspeed measurements either. The measurement noise and the model uncertainties are applied. Simulations demonstrate the rapidity and accuracy of the proposed method. [ABSTRACT FROM AUTHOR]

Ground Vibration Test (GVT) is a crucial step in the design process of a new aircraft. The test involves the analysis of different load configurations that helps to understand the real structural behaviour, to certificate some extreme load cases and also to validate and improve the Finite Element (FE) model used in the design. The validation of the FE model is done by making a comparison between Frequency Response Functions (FRF) from the GVT and the FE model at some response points. Ideally, numerical and experimental results should be in agreement. This is certainly true in the case of single components but in the case of assemblies it is common that numerical and experimental results show discrepancies, especially in the case of dynamic analysis. These differences increase as the number of components in the assembly grows, because connections are usually not represented sufficiently well in the FE model. The components of these assembled structures are usually connected at a small number of points, and they can be seen as local sources of stiffness and damping. In the FE model these joints can be represented as lumped spring/damper elements in a simplified way. In this paper a formulation of the dynamic equilibrium equation employing a decoupled approach for the joint mechanical properties of an assembly is presented. Later, an optimization process based on this formulation that involves the joint properties as design variables has been developed in order to identify the joint parameters that improve the adjustment between numerical and experimental FRF in the case of harmonic excitations. The approach has been applied to a FE model that represents the connection between a segment of fuselage and the tail cone and to a real aircraft system where experimental data is available. The results achieved with this procedure are very efficient and promising. [ABSTRACT FROM AUTHOR]

In the present paper a multidisciplinary optimization procedure for the self-generation of re-entry vehicle shapes has been developed. The procedure relies on a novel parametric model of blended wing-body shapes which is used to create a re-entry configuration around a fixed volume. The flexibility of the model allows us to create lifting body or winged re-entry vehicle from an optimization procedure as monolithic bodies. Multidisciplinary analysis is performed with engineering methods valid in conceptual design. Results of shape optimization for a minimum mass configuration, performed for a Low Earth Orbit Re-entry mission, confirmed the suitability of the procedure by indicating a decrease of vehicle mass configuration that is obtained by reducing the wingspan parameter for a conceptual lifting body configuration. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of joint power management and radar assignment to detect multiple targets in surveillance region for a distributed mono-static radar network while a low probability of interception (LPI) has been attained. Based on detection probabilities and target location estimates, the targeted problem is formulated as a combinatorial non-convex optimization problem which needs exhaustive search through all assignment schemes to reach optimal solution. Due to NP-hardness and non-convexity of the problem, some relaxations are proposed to transform the problem to a more tractable form. The main problem can be considered from two viewpoints, without information fusion and with information fusion. As another relaxation, we separated power allocation from radar assignments through two sub-problems in which first, the optimum power allocation is obtained for each assignment scheme and second, the target assignment schemes are selected based on the allocated powers. Simulation results show that our proposed algorithms not only guarantee detection performances but also considerably improve LPI specification in comparison with benchmark algorithms. [ABSTRACT FROM AUTHOR]

The aircraft trajectory optimization problem can be approached by first considering a large set of possible spatial aircraft trajectories that connect a departure point and an arrival point. For each spatial trajectory in this set, the minimization of the total flight cost leads unavoidably to the problem of selecting a minimal-cost speed-profile, also called optimal speed-profile (OSP). Once the OSP is known for each spatial trajectory, the optimal trajectory is simply the lowest-cost trajectory in the trajectory set, assuming that each trajectory is covered with the OSP. The determination of the OSP for a given spatial trajectory is a fundamental problem that must be resolved to find the optimal trajectory. In this paper, we define the speed-profile optimization problem. If the fuel consumption function is smooth and strictly convex, then we show that using a cost-index to account for the cost of time is equivalent to fixing the total travel-time in the optimization problem. We derive the optimality conditions of the OSP for constant altitude trajectories, both for the continuous problem and for a discretized version of the same problem. The analysis of these conditions reveals the relationship between the OSP and the locally-optimal speed-profile (LOSP), which is the speed profile that maximizes the specific range locally: for zero cost-index, the OSP and the LOSP are identical; for positive cost-index, speeds are higher on the OSP than on the LOSP. To compare quantitatively the OSP with the LOSP, we propose a first method to compute the OSP for constant altitude trajectories. The resulting numerical experiments reveal that the OSP uses more fuel but reaches destination sooner, which results in a small reduction of the direct operating cost. The magnitude of the cost savings associated with the OSP appears to be negligible in practice. We conclude that the LOSP is a good approximation of the OSP for constant altitude trajectories. [ABSTRACT FROM AUTHOR]

Control Momentum Gyros (CMGs) have many advantages over other actuators for attitude control of a spacecraft. Compared with Single Gimbal Control Moment Gyroscopes (SGCMGs), the Variable Speed Control Moment Gyros (VSCMGs) can be easily implemented. The VSCMGs have great advantages in performing complex mission of attitude control and easing power shortage of the entire spacecraft. In this paper, the singularity avoidance characteristic of the steering law of Weighted Pseudo-inverse with Null Motion (WPINM) is firstly analyzed by the method of the inner product. A new steering law is designed based on optimization theory, which can satisfy the requirement of attitude control and energy storage. At last, the singular point can be avoided by the new design steering law based on the simulation result. [ABSTRACT FROM AUTHOR]

This paper presents a wing aerostructural optimization framework based on the Individual Discipline Feasible (IDF) architecture. Using the IDF architecture the aerodynamic and the structure disciplines are decoupled in the analysis level and the optimizer is responsible for the consistency of the design. The SU2 CFD code is used for the aerodynamic analysis and the FEMWET software is used for the structural analysis. The SU2 code is modified in a way to receive the structural deformation as inputs and compute the sensitivity of the outputs, e.g. drag, with respect to the deformation. An Airbus A320 type aircraft is used as a test case for the optimization. A reduction of the aircraft fuel weight of 11% is achieved. This reduction was attained by increasing the wing span, reducing the wing sweep, improving the lift distribution and improving airfoil shapes. [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]

This paper develops a Nonlinear Model Predictive Control (NMPC) strategy for robust tracking of multi-impulse smooth transfer trajectories in J 2 -perturbed orbital environments. The reference trajectories are designed for long-range rendezvous of servicing satellites with orbiting targets. In the proposed NMPC, the control signals are velocity increments at the impulse times, and the prediction horizon is variable due to the impulsive nature of the reference trajectories. The reference control input and the impulse times are pre-specified by the reference trajectory. Then, the NMPC calculates the optimal correction control vector to track the trajectory with minimum deviation and control effort, during the time between two consecutive impulses. To arrive near the target at the end of the transfer, the optimization in the last horizon is modified to be free-final-time, with an added soft constraint to minimize the final distance between the servicer and the target satellite. To avoid singularities in the mean dynamics of the J 2 -perturbed servicer, the equinoctial orbital elements are used in the process model. We also include the first-order long-periodic effects in this model. We investigate the robustness of the proposed NMPC in a simulation environment that additionally models: (i) the first-order short-periodic effects, (ii) a bounded uncertain acceleration to capture unmodelled dynamics, and (iii) burning time for satellite thrusters. Finally, an evolutionary optimization algorithm is implemented and embedded in the controller to decrease the computational complexity and to handle impulsive control inputs. Simulation results are provided to illustrate the tracking effectiveness of the developed controller. [ABSTRACT FROM AUTHOR]

This paper studies a novel guidance framework of the vehicle against a high-speed and maneuvering target based on deep reinforcement learning (DRL) considering the energy consumption, autopilot lag dynamics, and input saturation, which can effectively cope with the high flight-path angle error flight phase and various uncertainties. The guidance framework proposes an end-to-end mapping transformation between the guidance command and observation states consisting of line-of-sight (LOS) angle, relative distance, and their rate measured by the seeker. At the same time, the observability of the LOS angle and relative distance is included in constructing the reward function. Besides, the relative engagement kinematic model of the interceptor-target is established and combined with the PPO guidance algorithm, jointly described as a Markov decision process (MDP). Notably, the guidance framework is optimized using the improved proximal policy optimization (PPO) algorithm and demonstrated in a simulated terminal phase in the near-space. Specifically, the PPO guidance algorithm is structured by the policy (actor) neural network and the critic neural network, and both are standard fully-connected neural networks. Subsequently, observation states and rewards are fully collected and applied by introducing the experience replay method. Also, the exponential decay learning rate method, mini-batch stochastic gradient ascent (SGA) method, zero-score standardization, and Adam optimizer are proposed to train the reinforcement learning algorithm more efficiently. Moreover, the proposed guidance framework has an excellent generalization capability and guarantees the implementation of fixed and stochastic engagement scenarios, which means that the interceptor can realize the unlearned practical combat scenarios. The robust capacity is indicated and validated using Monte Carlo simulation under various uncertainties. Moreover, the DRL guidance framework can satisfy the onboard application requirement. [ABSTRACT FROM AUTHOR]

This paper presents an online trajectory optimization algorithm for launch vehicles based on convex programming to ensure flight safety in case of power system fault. Due to high complexity of the power system, the engine may break down during the flight, causing significant decrease of thrust or energy. In this case, the nominal trajectory will be infeasible as the dynamical model and energy state is different from the normal status, thus the online trajectory optimization and re-planning are considered. For different kinds of engine failures, different terminal orbital constraints are proposed. When the mass flow rate of fuel decreases, the energy loss is little but the dynamical model changes obviously, so the location of the injection point cannot be guaranteed. In this case, the terminal orbital elements are constrained except the true anomaly, so that the payload of launch vehicles can still settle into the nominal orbit, and the true anomaly is optimized for minimum fuel consumption. As for the energy-loss failure, the strategy to change the target orbit is proposed considering the requirement of launch mission and subsequent orbit transfer insertion. The terminal constraints are proposed analytically in this paper. In order to solve the nonconvex trajectory optimization problem accurately and rapidly, the optimization problem is transformed into convex optimization problems by various convexification techniques, including the lossless convexification and successive convexification. Finally, the high efficiency and accuracy of the proposed algorithm is verified by numerical experiments. The algorithm proposed in this paper has potential applications in onboard trajectory optimization and re-planning of launch vehicles in case of power system fault to ensure the accomplishment of the launch mission. [ABSTRACT FROM AUTHOR]

Aerodynamic shape design may involve complicated geometric optimization problems. When the number of geometric decision variables increases, the optimization algorithms can still search for solutions automatically, but it would work more like a black-box, for which the explanation will be of great help. This paper focuses on further mining the explanatory information of the shape optimization. A multi-objective optimization of a spacecraft is performed as a simplified case study. The LightGBM meta-model is built for the response from the geometric variables to the objectives, followed the SHAP explainer to quantify the contributions of variables. To visually interpret the deformation on shape, we propose the SHAP-Shape Map method to translate the attribution from the deformation features to the shape and make the attribution visible on the mesh surface. Much explanatory knowledge is easily obtained, for example, without seeing any flow field or pressure distribution image, the explainer successfully inferred that the significant influence of the drag coefficient occurs at the tip of the shape's nose. Furthermore, it has almost no barrier to learn that the trade-offs (conflicts) between the objectives are mainly concentrated in the nose as well. More importantly, with the proposed method, the above analysis results can be reached by anyone without specialized expertise of aerodynamics or geometric parameterization. [ABSTRACT FROM AUTHOR]

In this paper, based on the Model Predictive Control (MPC) and Improved Grey Wolf Optimizer (IGWO), a hybrid method is proposed to plan the optimal trajectories of multi-UAVs for target tracking in urban environment. Firstly, the target tracking problem in urban environment is modeled detailedly by formulating the visibility region, the sensor coverage area, the restricted region, the space constraints, etc. Based on the model of target tracking, the centralized MPC method is then adopted as the framework to obtain the trajectories of UAVs in real time, the objective of which is to optimize tracking performance under various constraints. In consideration of the computational complexity of this problem, IGWO, a novel intelligent algorithm with the advantages of good stability and strong search ability is utilized to solve the MPC formulation. It imitates the social hierarchy and predatory strategy of wolf pack, and some improvement strategies are also introduced e.g. the individual memory and the principle of survival of the fittest. Finally, the proposed method is demonstrated in a simulated urban environment. The simulation results show the effectiveness of the proposed hybrid method to solve target tracking problem. [ABSTRACT FROM AUTHOR]