Real-time trajectory planning for UCAV air-to-surface attack using inverse dynamics optimization method and receding horizon control

This paper presents a computationally efficient real-time trajectory planning framework for typical unmanned combat aerial vehicle (UCAV) performing autonomous air-to-surface (A/S) attack. It combines the benefits of inverse dynamics optimization method and receding horizon optimal control technique. Firstly, the ground attack trajectory planning problem is mathematically formulated as a receding horizon optimal control problem (RHC-OCP). In particular, an approximate elliptic launch acceptable region (LAR) model is proposed to model the critical weapon delivery constraints. Secondly, a planning algorithm based on inverse dynamics optimization, which has high computational efficiency and good convergence properties, is developed to solve the RHCOCP in real-time. Thirdly, in order to improve robustness and adaptivity in a dynamic and uncer- tain environment, a two-degree-of-freedom (2-DOF) receding horizon control architecture is introduced and a regular real-time update strategy is proposed as well, and the real-time feedback can be achieved and the not-converged situations can be handled. Finally, numerical simulations demon- strate the efficiency of this framework, and the results also show that the presented technique is well suited for real-time implementation in dynamic and uncertain environment.     

Direct dynamic-simulation approach to trajectory optimization

This paper proposes a new direct method for an efficient trajectory optimization using the point that the dynamics of a deterministic system are uniquely determined by initial states and controls imposed over the time horizon of interest. To effectively implement this concept, the Hermite spline is adopted to interpolate the continuous controls and the system dynamics are integrated with corresponding control parameters in prior. As a result, the optimal control problem can be transcribed into a nonlinear programming problem which has no dynamic equality constraints and no intermediate states in its design variables. In addition, the paper proposes an efficient recursive Jacobian estimation technique and introduces a Jacobian transformation matrix to straightforwardly handle the general state constraints. Important properties of the present method are thoroughly investigated through its applications to the trajectory optimization for a soft lunar landing from a parking orbit, including the detailed analyses for the de-orbiting phase. The computed results are compared with those using the pseudo-spectral method to demonstrate an extreme outperformance of the proposed method in the aerospace applications over the traditional direct method.     

基于Radau伪谱法的无人作战飞机四维轨迹规划

针对无人作战飞机(UCAV)打击时敏目标和多机协同攻击问题,提出一种基于Radau伪谱法(RPM)的无人作战飞机四维攻击轨迹规划方法。在综合考虑UCAV气动力特性、大气环境特性基础上建立了高精度UCAV(3-DOF)质点模型,并设计了约束条件和目标函数;基于动态RCS建立威胁模型,构建了最优控制理论框架的UCAV四维攻击轨迹规划模型;通过RPM将动态RCS威胁下的四维攻击轨迹规划问题转换为非线性优化问题,然后利用SNOPT软件包进行求解。仿真结果表明,该方法能够以较快的速度和较高的精度生成满足多种复杂约束条件的四维攻击轨迹。     

基于混合优化的运载器大气层内上升段轨迹快速规划方法

针对运载器大气层内的最优轨迹快速规划问题,提出一种将求解最优控制问题的间接法与直接法相结合的混合优化方法。首先,基于最优控制问题的一阶必要条件,将运载器大气层内的三维最优上升问题转化为Hamiltonian两点边值问题;然后,采用直接法中能以较少的节点获得较高求解精度的Gauss伪谱法进行求解,提高算法的求解效率;最后,采用真空解析解初值及密度同伦技术,解决初值猜测与算法收敛困难的问题。仿真结果表明,混合优化算法能够准确、快速地对运载器大气层内的最优上升轨迹问题进行求解,并在计算精度与效率上均优于间接法,可应用于运载器的轨迹在线规划与闭环制导。     

Novel minimum time trajectory planning in terrain following flights

A new methodology has been proposed to enhance inverse dynamics applications in the process of trajectory planning and optimization in terrain following flights (TFFs). The new approach uses a least square scheme to solve a general two-dimensional (2-D) TFF in a vertical plane. In the mathematical process, Chebyshev polynomials are used to model the geographical data of the terrain in a given route in a manner suitable for the aircraft at hand. The aircraft then follows the modeled terrain with sufficient clearance. In this approach the terrain following (TF) problem is effectively converted to an optimal tracking problem. Results show that this method provides a flexible approach to solve the TFF problem especially in conditions where the existing terrain to be flown over is not mathematically well behaved     

三维高超声速飞行器再入轨迹快速优化

高超声速再入轨迹优化问题是一类复杂的最优控制问题。采用高斯伪谱法(GPM)将再入轨迹优化问题转化为非线性规划问题(NLP),对NLP进行归一化处理后,采用SNOPT软件包求解。根据协态变量映射规则计算出协态变量,得出哈密尔顿函数沿最优控制的变化过程。算例中,GPM耗时约7 s即可生成一条严格满足各种约束的三维最优再入轨迹,耗时远少于相关文献,并且优化精度高,满足一阶最优性必要条件。     

基于凸优化的再入轨迹三维剖面规划方法

可重复使用飞行器一般采用大升阻比气动外形,再入轨迹三维剖面规划方法可充分发挥这类飞行器固有的机动能力。计算量大是制约三维剖面规划应用的难题,为提高计算效率,提出了一种基于凸优化的再入轨迹三维剖面规划方法。首先,分析运动方程特性,利用定义新的控制变量、约束松弛、连续线性化等技术,将原始非凸的三维剖面规划问题转化为一个凸优化问题。其次,将指令反解步骤嵌入至序列凸化算法中,通过迭代求解凸优化子问题,获得原问题的可行解。数值仿真结果表明所提方法具有较高的求解精度和确定的收敛性质,飞行器的机动能力得到充分发挥;与伪谱法的结果对比表明凸优化方法在轨迹规划问题上具有更高的求解效率。     

自动微分技术及其在轨迹优化中的应用

研究了自动微分技术在直接配点法中的应用,将其与内点非线性规划算法相结合,用于求解配点法转化得到的非线性规划问题。采用前向自动微分模式,利用ADOL-C实现了基于算子重载的自动微分计算;为验证算法的性能,以经典的航天飞机再入轨迹优化问题为例,针对三种直接配点格式,比较了自动微分法和有限差分法用于微分计算的性能。研究结果表明,自动微分可以提高微分信息计算的精度和效率,进而提高非线性规划算法的稳定性和收敛速度。     

Hypersonic reentry trajectory planning by using hybrid fractional-order particle swarm optimization and gravitational search algorithm

This paper proposes a novel hybrid algorithm called Fractional-order Particle Swarm optimization Gravitational Search Algorithm (FPSOGSA) and applies it to the trajectory planning of the hypersonic lifting reentry flight vehicles. The proposed method is used to calculate the control profiles to achieve the two objectives, namely a smoother trajectory and enforcement of the path constraints with terminal accuracy. The smoothness of the trajectory is achieved by scheduling the bank angle with the aid of a modified scheme known as a Quasi-Equilibrium Glide (QEG) scheme. The aerodynamic load factor and the dynamic pressure path constraints are enforced by further planning of the bank angle with the help of a constraint enforcement scheme. The maximum heating rate path constraint is enforced through the angle of attack parameterization. The Common Aero Vehicle (CAV) flight vehicle is used for the simulation purpose to test and compare the proposed method with that of the standard Particle Swarm Optimization (PSO) method and the standard Gravitational Search Algorithm (GSA). The simulation results confirm the efficiency of the proposed FPSOGSA method over the standard PSO and the GSA methods by showing its better convergence and computation efficiency.     

PDE solution to UAV/UGV trajectory planning problem by spatio-temporal estimation during wildfires

Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) have been used in research and development community due to their strong potential in high-risk missions. One of the most important civilian implementations of UAV/UGV cooperative path planning is delivering medical or emergency supplies during disasters such as wildfires, the focus of this paper. However, wildfires themselves pose risk to the UAVs/UGVs and their paths should be planned to avert the risk as well as complete the mission. In this paper, wildfire growth is simulated using a coupled Partial Differential Equation (PDE) model, widely used in literature for modeling wildfires, in a grid environment with added process and measurement noise. Using principles of Proper Orthogonal Decomposition (POD), and with an appropriate choice of decomposition modes, a low-dimensional equivalent fire growth model is obtained for the deployment of the space–time Kalman Filtering (KF) paradigm for estimation of wildfires using simulated data. The KF paradigm is then used to estimate and predict the propagation of wildfire based on local data obtained from a camera mounted on the UAV. This information is then used to obtain a safe path for the UGV that needs to travel from an initial location to the final position while the UAV’s path is planned to gather information on wildfire. Path planning of both UAV and UGV is carried out using a PDE based method that allows incorporation of threats due to wildfire and other obstacles in the form of risk function. The results from numerical simulation are presented to validate the proposed estimation and path planning methods.     

Gaussian pigeon-inspired optimization approach to orbital spacecraft formation reconfiguration

With the rapid development of space technology, orbital spacecraft formation has received great attention from international and domestic academics and industry. Compared with a single monolithic, the orbital spacecraft formation system has many advantages. This paper presents an improved pigeon-inspired optimization(PIO) algorithm for solving the optimal formation reconfiguration problems of multiple orbital spacecraft. Considering that the uniform distribution random searching system in PIO has its own weakness, a modified PIO model adopting Gaussian strategy is presented and the detailed process is also given. Comparative experiments with basic PIO and particle swarm optimization(PSO) are conducted, and the results have verified the feasibility and effectiveness of the proposed Gaussian PIO(GPIO) in solving orbital spacecraft formation reconfiguration problems.     

航空发动机过渡态全局寻优控制方法研究

为了对发动机的动态过程进行控制, 提出了一种基于SQP的全局寻优控制方法.每一步寻优过程分为长程优化和短程优化两个阶段.根据长程优化结果, 重构了关联目标函数, 在此基础上, 进行了短程优化修正, 并利用修正结果进行控制.将这种方法应用于某型航空发动机的过渡态控制中, 得到了理想的控制结果.仿真结果表明该方法能较好地解决一般局部优化方法在有纯延迟、模型失配等情况下的优化控制效果下降的问题.      

Comprehensive approach to mathematical modeling and helicopter flight task optimization

A comprehensive approach is proposed that allows modeling the flight of helicopter over the entire range of flight speeds. We also consider a technique for optimizing the trajectory corresponding to the flight task taking into account the terrain and operating conditions.     

A multiobjective optimization approach to determine the parameters of stepped frequency pulse train

Frequency stepping techniques are commonly used in modern radar system to get high range resolution with the disadvantage that its autocorrelation function (ACF) yield undesirable “grating lobes”. Wider mainlobe deteriorates the range resolution capability of the waveform and higher peak sidelobe either hides the small targets or causes the false target detection. Several techniques have been used to choose the parameters of linear frequency modulated (LFM) pulse train to suppress the grating lobes without paying much attention to the mainlobe width and peak sidelobe level. In this paper a multiobjective optimization (Nondominated Sorting Genetic Algorithm-II (NSGA-II)) approach is proposed to optimize the parameters of the LFM pulse train to achieve reduced grating lobes, low peak sidelobe level and narrow mainlobe width. The optimization problem has been studied in two different ways: first one is associated with the reduction of grating lobes and the minimization of peak sidelobe level of the ACF with constraints and second one is related to the minimization of the peak sidelobe level and mainlobe width of the ACF with constraints. Simulation studies have been carried out to justify the potentiality of the proposed approach.     

On a certain approach to modeling aerodynamics and dynamics of spatial motion of single rotor helicopters

An approach that can be used for modeling the situation of unanticipated yaw is presented. A software is developed that allows us to obtain the helicopter equilibrium attitude at an arbitrary speed and consider the helicopter dynamics under influence of a certain disturbing factor.     

A multi-strategy pigeon-inspired optimization approach to active disturbance rejection control parameters tuning for vertical take-off and landing fixed-wing UAV

In this paper, Active Disturbance Rejection Control(ADRC) is utilized in the pitch control of a vertical take-off and landing fixed-wing Unmanned Aerial Vehicle(UAV) to address the problem of height fluctuation during the transition from hover to level flight. Considering the difficulty of parameter tuning of ADRC as well as the requirement of accuracy and rapidity of the controller, a Multi-Strategy Pigeon-Inspired Optimization(MSPIO) algorithm is employed. Particle Swarm Optimization(PSO), Gen...