Mission planning optimization for the visual inspection of multiple geosynchronous satellites

In this article, a new manoeuvring strategy for the multiple geosynchronous satellites inspection problem is proposed. In contrast to previous research, it can be used to visit multiple geosynchronous satellites orbiting in different orbital planes in an economical way. In the proposed mission scenario, several chasers are initially placed in an equatorial high eccentric orbit. Two orbital manoeuvres are exerted at perigee to adjust the apogee of the chaser for every inspection. Subsequently, the targets will be visited when they fly through the ascending or descending nodes of their orbits. Based on the proposed strategy, a two-level optimization approach is developed to optimize visitation order and time regarding fuel consumption. Meanwhile, the manoeuvre velocity and time are determined. Finally, the proposed method is applied to several numerical test cases to demonstrate its validity for mission planning optimization for the visual inspection of multiple geosynchronous satellites.     

Parametric Optimization of Hybrid Car Engines

We consider the problem of optimal design of hybrid car engines which combine thermal and electric power. The optimal configuration of the different motors composing the hybrid system involves the choice of certain design parameters. For a given configuration, the goal is to minimize the fuel consumption along a trajectory. This is an optimal control problem with one state variable.The simultaneous optimization of design parameters and trajectories can be formulated as a bilevel optimization problem. The lower level computes the optimal control for a given architecture. The higher level seeks for the optimal design parameters by solving a nonconvex nonsmooth optimization problem with a bundle method.     

Trajectory optimization of spacecraft high-thrust orbit transfer using a modified evolutionary algorithm

This article introduces a new method to optimize finite-burn orbital manoeuvres based on a modified evolutionary algorithm. Optimization is carried out based on conversion of the orbital manoeuvre into a parameter optimization problem by assigning inverse tangential functions to the changes in direction angles of the thrust vector. The problem is analysed using boundary delimitation in a common optimization algorithm. A method is introduced to achieve acceptable values for optimization variables using nonlinear simulation, which results in an enlarged convergence domain. The presented algorithm benefits from high optimality and fast convergence time. A numerical example of a three-dimensional optimal orbital transfer is presented and the accuracy of the proposed algorithm is shown.     

基于改进粒子群算法的月地转移轨道优化

月地转移轨道优化是月球返回任务的技术难题之一,其搜索空间大、约束条件多。该文通过罚函数法将多约束优化问题转化为无约束优化问题,提出了一种改进粒子群算法,利用适应度函数来更新惯性权重,对粒子的速度加以约束,还对粒子的位置参数引入随机反馈控制,分析了算法的收敛性。在月地返回窗口内获得了逃逸速度增量最小的月地转移轨道优化结果,并利用目标函数的等高线图分析,对优化结果进行了验证。     

On high-resolution manoeuvres control via trajectory optimization

This research is on a realization of control approach in line with the trajectory optimization for the purpose of dealing with overactuated spacecraft in the process of the high-resolution manoeuvres. The idea behind the research is to realize closed control loops to cope with the rotational angles and the corresponding angular rates, synchronously, to handle the spacecraft manoeuvres. It is to be noted that the traditional techniques may not have sufficient merit to deal with such a complicated process, suitably. The proposed trajectory optimization is designed to provide the three-axis referenced commands, in finite burn, for transferring the aforementioned overactuated spacecraft from the initial orbit to its final outcomes in the orbital transfer process. The outcomes are realized through the variations of the orbital parameters, including the inclination, the eccentricity, the angular momentum, the semi-major axis and so on, in the high-resolution manoeuvres. It aims to get the system under control to guarantee the performance of the three-dimensional rotational angles tracking to be desirable, instantly. The contribution of the research is to make the high-thrust optimization trajectory, which is organized in association with the new configuration of the three-axis attitude control approach, to be applicable to manage the present overactuated spacecraft in the procedure of high-resolution orbital transfer process. The investigated outcomes of the research are efficient and competitive along with the potential materials through a series of experiments, as long as the desirable tracking performance in the three-dimensional space manoeuvres is apparently guaranteed.     

Cross-country sailplane flight as a dynamic optimization problem

A minimum?time, thermal?to?thermal sailplane trajectory optimization problem is formulated as a non?linear optimal control problem. Numerical solutions are obtained using a gradiaent projection algorithm which incorporates conjugate directions of search. Further insight into the nature of the solutions and the computational process is ontained through an analysis of the linearized sailplane dynamics and the necessary conditions for optimality. Numerical results are presented for two sailplane types and various values of thermal strength and distance between thermals. An additional problem is formulated and solved for the case of bounded control rate.     

Efficient unmanned aerial vehicle formation rendezvous trajectory planning using Dubins path and sequential convex programming

Trajectory planning of formation rendezvous of multiple unmanned aerial vehicles (UAVs) is formulated as a mixed-integer optimal control problem, and an efficient hierarchical planning approach based on the Dubins path and sequential convex programming is proposed. The proposed method includes the assignment of rendezvous points (high level) and generation of cooperative trajectories (low level). At the high level, the assignment of rendezvous points to UAVs is optimized to minimize the total length of Dubins-path-based approximate trajectories. The assignment results determine the geometric relations between the UAVs’ goals, which are used as equality constraints for generating trajectories. At the low level, trajectory generation is treated as a non-convex optimal control problem, which is transformed to a non-convex parameter optimization and then solved via sequentially performing convex optimization. Numerical experiments demonstrate that the proposed method can generate feasible trajectories and can outperform a typical nonlinear programming method in terms of efficiency.     

Reliability based design optimization using response surfaces in application to multidisciplinary systems

Traditionally, reliability based design optimization (RBDO) is formulated as a nested optimization problem. For these problems the objective is to minimize a cost function while satisfying the reliability constraints. The reliability constraints are usually formulated as constraints on the probability of failure corresponding to each of the failure modes or a single constraint on the system probability of failure. The probability of failure is usually estimated by performing a reliability analysis. The difficulty in evaluating reliability constraints comes from the fact that modern reliability analysis methods are themselves formulated as an optimization problem. Solving such nested optimization problems is extremely expensive for large scale multidisciplinary systems which are likewise computationally intensive. In this research, a framework for performing reliability based multidisciplinary design optimization using approximations is developed. Response surface approximations (RSA) of the limit state functions are used to estimate the probability of failure. An outer loop is incorporated to ensure that the approximate RBDO converges to the actual most probable point of failure. The framework is compared with the exact RBDO procedure. In the proposed methodology, RSAs are employed to significantly reduce the computational expense associated with traditional RBDO. The proposed approach is implemented in application to multidisciplinary test problems, and the computational savings and benefits are discussed.     

Turning-lane and signal optimization at intersections with multiple objectives

Traffic congestion at intersections is a serious problem in cities. In order to discharge turning vehicles efficiently at intersections to relieve traffic jams, multiple left-turn and right-turn lanes are often used. This article proposes a novel multi-objective optimization method for signal setting and multiple turning-lane assignment at intersections based on microscopic traffic simulations and a cell-mapping method. Vehicle conflicts and pedestrian interference are considered. The intersection multi-objective optimization problem (MOP) is formulated. The cell-mapping method is adopted to solve the MOP. Three measures of traffic performance are studied including transportation efficiency, energy consumption and road safety. The influence of turning-lane assignment on intersection performance is investigated in the optimization. Significant impacts of the number of turning lanes on the traffic are observed. An algorithm is proposed to assist traffic engineers to select and implement the optimal designs. In general, more turning lanes help increase turning traffic efficiency and lower fuel consumption in most cases. Remarkable improvement in traffic performance can be achieved with combined optimization of lane assignment and signal setting, which cannot be obtained with signal setting optimization alone. The studies reported in this article provide general guidance for intersection planning and operation. The proposed optimization methodology represents a promising emerging technology for traffic applications.     

Multi-step optimization strategy for fuel-optimal orbital transfer of low-thrust spacecraft

An effective method for the design of fuel-optimal transfers in two- and three-body dynamics is presented. The optimal control problem is formulated using calculus of variation and primer vector theory. This leads to a multi-point boundary value problem (MPBVP), characterized by complex inner constraints and a discontinuous thrust profile. The first issue is addressed by embedding the MPBVP in a parametric optimization problem, thus allowing a simplification of the set of transversality constraints. The second problem is solved by representing the discontinuous control function by a smooth function depending on a continuation parameter. The resulting trajectory optimization method can deal with different intermediate conditions, and no a priori knowledge of the control structure is required. Test cases in both the two- and three-body dynamics show the capability of the method in solving complex trajectory design problems.     

A robust optimization methodology for preliminary aircraft design

This article focuses on a robust optimization of an aircraft preliminary design under operational constraints. According to engineers' know-how, the aircraft preliminary design problem can be modelled as an uncertain optimization problem whose objective (the cost or the fuel consumption) is almost affine, and whose constraints are convex. It is shown that this uncertain optimization problem can be approximated in a conservative manner by an uncertain linear optimization program, which enables the use of the techniques of robust linear programming of Ben-Tal, El Ghaoui, and Nemirovski [Robust Optimization, Princeton University Press, 2009]. This methodology is then applied to two real cases of aircraft design and numerical results are presented.     

A computational homogenization approach for limit analysis of heterogeneous materials

The macroscopic strength domain and failure mode of heterogeneous or composite materials can be quantitatively determined by solving an auxiliary limit analysis problem formulated on a periodic representative volume element. In this paper, a novel numerical procedure based on kinematic theorem and homogenization theory for limit analysis of periodic composites is developed. The total velocity fields, instead of fluctuating (periodic) velocity, at microscopic level are approximated by finite elements, ensuring that the resulting optimization problem is similar to the limit analysis problem formulated for structures, except for additional constraints, which are periodic boundary conditions and averaging relations. The optimization problem is then formulated in the form of a standard second‐order cone programming problem to be solved by highly efficient solvers. Effects of loading condition, representative volume element architecture, and fiber/air void volume fraction on the macroscopic strength of perforated and fiber reinforced composites are studied in numerical examples. Copyright © 2017 John Wiley & Sons, Ltd.     

Nonlinear programming applied to the optimum control of a gas compressor station

The paper concerns the problem of optimal control of a compressor station of natural gas supplied with motor-compressor. A mathematical model of the object considered is formulated. The control of a compressor station is variable staircase function. The objective function, taking into account the cost of energy consumed by the motor-compressor during each time interval, is proposed. Moreover, it is assumed that the values of suction pressure, compression pressure and the compressor station capacity are given. The optimization problem stated consists in evaluating the number of simultaneously operating motor-compressors and determining such a distribution of the capacity that the total unit fuel consumption in each time interval is minimized subject to the constraints imposed. The paper presents an algorithm of automatic search for the optimal values of the operating parameters of the compressor station. The algorithm proposed can be implemented on a digital computer. The method presented has been verified experimentally on the controlled object.     

Trajectory optimization of nonholonomic mobile manipulators departing to a moving target amidst moving obstacles

How to plan the optimal trajectory of nonholonomic mobile manipulators in dynamic environments is a significant and challenging task, especially in the system with a moving target. This paper presents trajectory optimization of a nonholonomic mobile manipulator in dynamic environment pursuing a moving target. Full nonlinear dynamic equations of the system considering the nonholonomic constraints of wheels are presented. Then, dynamic motion planning of the system is formulated as an optimal control problem considering moving obstacle avoidance conditions. Accordingly, a new formulation of dynamic potential function was proposed based on the dynamic distance between colliding objects. In addition, an appropriate boundary value for a moving target was defined, and the resulted boundary value problem was solved to optimize the trajectory of the system. To solve the problem, an indirect solution of optimal control was applied which leads to transform the optimal control problem into a set of coupled differential equations. To demonstrate the efficiency and applicability of the method a number of simulations and experiments was performed for a spatial nonholonomic mobile manipulator.     

Study on the vehicle routing problem considering congestion and emission factors

To meet the requirement of greening transportation in poor traffic condition, vehicle routing problem (VRP) with consideration of fuel consumption and congestion is studied. We formulated a time-dependent green vehicle routing problem (TD-GVRP) model with minimised total cost as the objective function which includes fuel consumption cost, and the measurement of fuel consumption is based on the Comprehensive Modal Emissions Model (CMEM). In the model, the situation of waiting at customer nodes to avoid bad traffic is defined. To solve this model, a Response Surface Method (RSM)-based hybrid algorithm (HA) that combines genetic algorithm (GA) and particle swarm optimisation (PSO) is constructed. Finally, using instances from PRPLIB database, the following experiments are carried out and the corresponding conclusions are drawn. (i) Comparison of the proposed objective and traditional VRP objectives shows that fuel consumption can be greatly reduced by introducing fuel consumption factor into the objective function. (ii) Sensitivity analysis of congestion duration provides the influence of congestion duration on fuel consumption and travel time. (iii) Experiments based on different waiting time reveal that the optimisation of departure time can reduce fuel consumption and total cost to some extent.     

Multi-objective component sizing of plug-in hybrid electric vehicle for optimal energy management

This paper presents a methodology for component sizing optimization of a parallel plug-in hybrid electric vehicle by considering it as a multi-objective optimization problem. In this approach, two objective functions are defined to minimize the drivetrain cost, fuel consumption, and exhaust emissions simultaneously. Also, the driving performance requirements are considered as constraints. In addition, fuzzy logic controller including blended control strategy is developed for the PHEV. Finally, by means of multi-objective particle swarm optimization algorithm, the best choices of components are selected for 32 miles of the both TEH-CAR and UDDS driving cycles. Simulation results demonstrate the effectiveness and practicality of the approach, which prepare different optimal component sizes with various drivetrain costs, equivalent fuel consumption, and exhaust emissions.     

Optimal condition based maintenance with imperfect information and the proportional hazards model

Condition based maintenance (CBM) is based on collecting observations over time, in order to assess equipment's state, to prevent its failure and to determine the optimal maintenance strategies. In this paper, we derive an optimal CBM replacement policy when the state of equipment is unknown but can be estimated based on observed condition. We use a proportional hazards model (PHM) to represent the system's degradation. Since equipment's state is unknown, the optimization of the optimal maintenance policy is formulated as a partially observed Markov decision process (POMDP), and the problem is solved using dynamic programming. Practical advantages of combining the PHM with the POMDP are shown.     

Using multicriterion optimization for strength design of composite laminates

Using multicriterion optimization methods for weight minimization of failure strength controlled composite structures is described. A composite lay-up design problem for minimizing the number of layers under strength constraints with respect to multiple loading conditions is formulated. The constrained problem is transferred into a sequence of unconstrained problems and solved with an interactive descent method. A typical design cycle that comprises the finite element mesh generator and solver as well as the laminate analysis and optimization modules is illustrated with a numerical example.     

基于极小值原理的最优眼动控制模型

在眼动系统中,由于视网膜中央凹区域受到噪声的干扰,使得眼跳时间和准确度不能同时达到最优化。为了得到最优眼动轨迹,在权衡眼跳时间和准确度的基础上,从眼动消耗最小入手,应用最优控制理论和Pontryagin极小值原理,提出了最优眼动控制模型;并利用眼动主序关系对此模型进行了分析;结果表明,任何给定的眼跳幅度条件下,都有唯一的最优眼跳时间使眼动总消耗达到最小,并且此时的眼动轨迹为最优眼动轨迹。该模型对优化飞行员、管制员等工作人员的眼动策略和眼动轨迹具有一定意义。     

Could Intelligent Speed Adaptation make overtaking unsafe?

This driving simulator study investigated how mandatory and voluntary ISA might affect a driver's overtaking decisions on rural roads, by presenting drivers with a variety of overtaking scenarios designed to evaluate both the frequency and safety of the manoeuvres. In half the overtaking scenarios, ISA was active and in the remainder ISA was switched off. A rural road was modelled with a number of 2 + 1 road sections, thus allowing drivers a protected overtaking opportunity. The results indicate that drivers became less inclined to initiate an overtaking manoeuvre when the mandatory ISA was active and this was particularly so when the overtaking opportunity was short. In addition to this, when ISA was activated drivers were more likely to have to abandon an overtaking, presumably due to running out of road. They also spent more time in the critical hatched area—a potentially unsafe behaviour. The quality of the overtaking manoeuvre was also affected when mandatory ISA was active, with drivers pulling out and cutting back in more sharply. In contrast, when driving with a voluntary ISA, overtaking behaviour remained mostly unchanged: drivers disengaged the function in approximately 70% of overtaking scenarios. The results of this study suggest that mandatory ISA could affect the safety of overtaking manoeuvres unless coupled with an adaptation period or other driver support functions that support safe overtaking.