检索结果-内蒙古大学图书馆

Time-optimal trajectory planning for a race car considering variable tyre-road friction coefficients

VEHICLE SYSTEM DYNAMICS 2021年第4期59卷 588-612页

作者： Christ, Fabian Wischnewski, Alexander Heilmeier, Alexander Lohmann, Boris Tech Univ Munich Chair Automat Control Munich Germany Tech Univ Munich Chair Automot Technol Munich Germany

This paper shows the planning of time-optimal trajectories, which allows an autonomous race car to drive at the handling limits, taking into account locally changing road friction values. For this purpose, the minimum lap time problem is described as an optimal control problem, converted to a nonlinear programme using direct orthogonal Gauss-Legendre collocation and then solved by the interior-point method IPOPT. Reduced computing times are achieved using a curvilinear abscissa approach for track description, algorithmic differentiation using the software framework CasADi, and a smoothing of the track input data by approximate spline regression. The vehicle's behaviour is approximated as a single track and double track model with quasi-steady state tyre load simplification and nonlinear tyre model. The results are used to evaluate which vehicle physics are important for the calculation of the time-optimal trajectory. The novelty of this work is the consideration of wheel-specific tyre-road friction coefficients along the racetrack using a track friction map. It is shown that variable friction coefficients have a significant impact on the trajectory, and therefore significantly improve lap times on inhomogenous racetracks. The proposed trajectory planning has proven its practical suitability in first tests on an autonomous race car and will be used in the coming racing season in the Roborace competition.

关键词： Trajectory planning numerical optimal control nonlinear programming variable tyre-road friction coefficients autonomous race car

来源：评论

学校读者我要写书评

暂无评论

Finite-Thrust Natural-Motion Circumnavigation Injection by Convex Optimization

引用

JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2021年第3期45卷 453-467页

作者： Lu, Ping Lewis, Alexander Adams, Richard J. DeVore, Michael D. Petersen, Christopher D. San Diego State Univ San Diego CA 92182 USA Barron Associates Inc Charlottesville VA 22901 USA US Air Force Res Lab US Airtland Air Force Base Albuquerque NM 87117 USA

A methodology is developed to use convex optimization for finding the propellant-optimal finite-thrust trajectory of a spacecraft to inject into a specified natural-motion circumnavigation (NMC) orbit around another spacecraft. The problem is nonconvex. A philosophically new perspective is introduced to take advantage of modern convex optimization. Through a novel analysis the NMC problem is shown to be equivalent to a two-dimensional constrained optimization problem. This conceptually simpler interpretation enables two numerical approaches to be investigated, one based on convex relaxation and the other linearization-projection. It is established that while each approach is able to lead to the solution to the NMC-injection problem in a subset of the possible cases, their domains of applicability complement each other to cover all possible cases. A hybrid algorithm is designed that combines the strengths of the two approaches and enables the application of convex optimization to solve the NMC-injection problem in all cases where the solution exists, without the need for any user-supplied parameters or initial guesses. The effectiveness of the hybrid algorithm is demonstrated in finding the numerical solutions to the NMC problem reliably and rapidly.

关键词： Thrust nonlinear programming Propellant Thrust Vectoring Sequential Quadratic programming Orbital Elements Optimal Control Model Predictive Control Earth Spacecraft Formation

来源：评论

学校读者我要写书评

暂无评论

Computation Offloading Optimization for UAV-Based Cloud-Edge Collaborative Task Scheduling Strategy

引用

IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Chen, Haosheng Cui, Haixia Wang, Jiahuan Cao, Peng He, Yejun Guizani, Mohsen Foshan528225 China Shenzhen University School of Electronic and Information Engineering Shenzhen518060 China Mohamed Bin Zayed University of Artificial Intelligence Machine Learning Department Abu Dhabi United Arab Emirates

Unmanned aerial vehicles (UAVs) with communication and computing capabilities are essential for supporting mobile edge computing (MEC). They can be deployed flexibly to provide computing services for remote mountainous areas or disaster-stricken areas with damaged communication facilities, thereby enhancing communication quality and coverage area. Considering the 5G ultra-reliable and low-latency communication (URLLC) application requirement issues, this paper focuses on a UAV-based cloud-edge cooperative scheduling system aimed at minimizing the system processing delay while maintaining offloading fairness. We address the optimization of user scheduling, UAV flight trajectory, and task offloading ratio under constraints on the energy consumption and discrete variables. Given the complexity of formulated optimization problem, which involves mixed-integer nonlinear programming (MINLP), non-convexity, high-dimensional state space, and continuous action space, we introduce a deep reinforcement learning (DRL)-based computation offloading approach that leverages the deep deterministic policy gradient (DDPG) algorithm. It explores the continuous action space, encompassing variables like UAV flight angle and speed, takes precise actions according to the current state, and ultimately identifies an optimal offloading strategy. Simulation experiments show that the proposed algorithm notably diminishes the processing delay in contrast to other baseline approaches, while they also confirm that the effectiveness of cloud-edge hybrid offloading surpasses that of either single cloud offloading or UAV offloading strategies. © 2015 IEEE.

关键词： nonlinear programming

来源：评论

学校读者我要写书评

暂无评论

Convex-Concave Decomposition of nonlinear Equality Constraints in Optimal Control

引用

JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2021年第1期44卷 4-14页

作者： Lu, Ping San Diego State Univ Dept Aerosp Engn San Diego CA 92182 USA

In optimal control and optimization problems where partial convexity already exists, it is highly desirable to reformulate the nonconvex parts of the problem in a way amendable to the use of a convex optimization algorithm. The biggest challenge in doing so lies with nonlinear equality constraints. Traditional linearization-based techniques have inherent shortcomings that can cause them to be ineffective in some routine applications. In this paper a novel method is developed to treat a class of nonlinear equality constraints by a convex-concave decomposition. In a relaxed problem each nonlinear equality constraint of the class is represented by three inequality constraints that are either convex or concave. It is theoretically established that the relaxed problem can be set up to have the same solution as the original one. A convergent successive solution approach is designed to find the solution of the relaxed problem by solving a sequence of convex optimization problems. The application of the proposed method in a fuel-optimal finite-thrust spacecraft circumnavigation problem demonstrates the effectiveness of the approach where the conventional linearization method fails under most conditions.

关键词： Optimal Control nonlinear programming Quadratic programming Control Optimization Exhaust Velocity Propellant Aerospace Engineering Earth Spacecraft Proximity Operations Iterative Solution

来源：评论

学校读者我要写书评

暂无评论

Joint Power allocation and target detection in distributed MIMO radars

引用

IET RADAR SONAR AND NAVIGATION 2021年第11期15卷 1433-1447页

作者： Jebali, Safieh Keshavarz, Hengameh Allahdadi, Mehdi Univ Sistan & Baluchestan Fac Elect & Comp Engn Dept Commun Engn Zahedan Iran Univ Sistan & Baluchestan Fac Math Dept Math Zahedan Iran

Here, a power allocation method jointly optimising the total transmit power and the probability of interception is proposed based on target detection applications in distributed multiple-input multiple-output (MIMO) radars. Power allocation is performed by solving a non-linear constrained optimisation problem formulated to minimise the total transmit power based on target detection applications. Then, the Neyman-Pearson detector is designed under the Rayleigh scatter model and the Lagrangian method is used to solve the optimisation problem. Moreover, a positioning algorithm optimising the placement of transmitters and receivers in distributed MIMO radars is applied to minimise the total transmit power satisfying the target detection criterion. The results show that uniform power allocation is not the optimal strategy and the proposed power allocation algorithm provides either better target detection performance for the same power budget, or requires less power to provide the same detection performance. Numerical simulations and the theoretic analysis confirm the effectiveness of the proposed algorithm.

关键词： MIMO radar probability object detection radar detection nonlinear programming radar transmitters radar receivers numerical analysis

来源：评论

学校读者我要写书评

暂无评论

The Shortest Path Interdiction Problem with Randomized Interdiction Strategies: Complexity and Algorithms

引用

OPERATIONS RESEARCH 2021年第1期69卷 82-99页

作者： Holzmann, Tim Smith, J. Cole US Air Force Dept Operat Sci Inst Technol Dayton OH 45433 USA Syracuse Univ Dept Elect Engn & Comp Sci Syracuse NY 13244 USA

Shortest-path interdiction problems involve a leader and a follower playing a zero-sum game over a directed network. The leader interdicts a set of arcs, and arc costs increase as a function of the number of times they are interdicted. The follower observes the leader's actions and selects a shortest path in response. The leader's optimal interdiction strategy maximizes the follower's minimum-cost path. In classic formulations of these problems, the leader's interdiction actions are deterministic. In this paper the leader selects a policy of randomized interdiction actions, and the follower only knows the probability of where interdictions are deployed on the network. The follower identifies a path having the minimum expected cost, whereas the leader seeks to maximize the follower's objective. When the arc costs are affine functions of the number of times they are interdicted, this problem is solvable by linear programming. However, when the cost functions are convex or when they are concave, we show that the expected costs are Schur concave or convex, respectively. This property allows us to prove that the problem becomes strongly NP-hard in the nonaffine case. We also propose several algorithms for this problem. Some are for special cases, and one is a general algorithm. We examine the efficacy of our algorithms on a test bed of randomly generated instances by comparing our algorithms to a standard solver.

关键词： networks/graphs nonlinear programming military Schur convexity

来源：评论

学校读者我要写书评

暂无评论

Problem Formulation and Solution Methodology for Energy Consumption Optimization in Bernoulli Serial Lines

引用

IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2021年第2期18卷 776-790页

作者： Yan, Chao-Bo Zheng, Ziqian Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Xian 710049 Peoples R China Xi An Jiao Tong Univ Fac Elect & Informat Engn Sch Automat Sci & Engn Xian 710049 Peoples R China Univ Wisconsin Dept Ind & Syst Engn Madison WI 53726 USA

As the main force of energy consumption, machines consume a huge amount of energy in some production systems. To operate the systems in an energy-efficient way, the efficiencies of the machines should be elaborately optimized so that the total energy consumption in the production lines is minimized while maintaining the required production rate. For this purpose, this article investigates the energy consumption optimization problem in Bernoulli serial lines having more than two machines. Specifically, this problem is first formulated as a nonlinear programming with a production rate constraint;due to difficulties in solving this constrained nonlinear programming, structural characteristics of the problem and properties of the objective function, which are inspired by the results and insights obtained in two-machine lines, are analyzed. Finally, based on the structural characteristics and properties explored, a solution methodology is developed to recursively solve the unique optimal solution of the energy consumption optimization problem. Extensive numerical experiments show that the solution provided by this method, which has the reversibility property and is better than the one provided by an existing method in the literature, is numerically optimal (note that the theoretically optimal solution is extremely hard to obtain if not totally impossible). Note to Practitioners-Machines consume a huge amount of energy in energy-intensive (i.e., high energy-consuming) production systems in, e.g., automobile, semiconductor, and steel companies. Due to economic, social, and environmental concerns, it is a high priority to consider ways to reduce the energy consumption in these production systems. In this article, the problem of minimizing the total energy consumption in Bernoulli serial lines while maintaining the required production rate is investigated. Based on the aggregation method developed for performance analysis of long lines and the optimization results obtained f

关键词： Energy consumption Optimization Manufacturing systems programming Robots Aggregation method energy reduction monotonicity nonlinear programming production rate

来源：评论

学校读者我要写书评

暂无评论

Trajectory Optimization for a Connected Automated Traffic Stream: Comparison Between an Exact Model and Fast Heuristics

引用

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2021年第5期22卷 2969-2978页

作者： Xu, Zhigang Wang, Yu Wang, Guanqun Li, Xiaopeng Bertini, Robert L. Qu, Xiaobo Zhao, Xiangmo Changan Univ Sch Informat Engn Xian 710064 Peoples R China Univ S Florida Dept Civil & Environm Engn Tampa FL 33620 USA Chalmers Univ Technol Dept Architecture & Civil Engn S-41296 Gothenburg Sweden

Numerous fast heuristic algorithms, including shooting heuristics (SH), have been developed for real-time trajectory optimization, although their optimality has not yet been quantified. This paper compares the performance between fast heuristics and exact optimization models. We investigate a core trajectory optimization problem as a building block for numerous trajectory optimization problems, i.e., guiding movements of connected automated vehicles on a one-lane highway when the arrival and departure times and velocity are given. To apply the SH algorithm to this problem, we adapt it to a fast-simplified shooting heuristic (FSSH) model to solve the trajectory smoothing problems with different arrival and departure velocities. An exact trajectory optimization (ETO) model is formulated that takes the vehicle position and velocity as the decision variables, and the fuel consumption and driving comfort as the objective function. The constraints of the model are based on the limits and safety of the vehicle dynamics between consecutive vehicles. We demonstrate the convexity of the ETO objective function, ensuring the solvability of the ETO model at the true optimum using gradient descent algorithms supplied by the MATLAB optimization toolbox. Six groups of numerical experiments using different input parameters and one experiment using real Next Generation Simulation (NGSIM) data are conducted. ETO can improve the objective values by a few to tens of percentage points. However, FSSH achieves a greater solution efficiency with an average solution time of less than 0.1 s compared to similar to 450 s for ETO.

关键词： Connected automated vehicle fuel economy speed control trajectory optimization shooting heuristics nonlinear programming

来源：评论

学校读者我要写书评

暂无评论

Onboard Generation of Optimal Trajectories for Hypersonic Vehicles Using Deep Learning

引用

JOURNAL OF SPACECRAFT AND ROCKETS 2021年第2期58卷 400-414页

作者： Shi, Yang Wang, Zhenbo Univ Tennessee Dept Mech Aerosp & Biomed Engn Knoxville TN 37996 USA

Recent development of deep learning has shown that a deep neural network (DNN) is capable of learning the underlying nonlinear relationship between the state and the optimal actions for nonlinear optimal control problems. In terms of hypersonic flight, this suggests that the DNN-based trajectory controller may be considered to take over all or part of the onboard trajectory generation and guidance system. This Paper investigates the possibility of training the DNN-based controller off line using the optimal state-action samples obtained from high-fidelity algorithms. The time-consuming computation is carried out off line, and the resulting DNN-based controller is potentially capable of near-optimal control with real-time performance and stable convergence. First, the system dynamics of hypersonic flight are described, and the trajectory optimization problem is formulated as a highly nonlinear optimal control problem. Then, the state-action vectors are extracted from the optimal trajectories generated by solving the formulated optimal control problem from random initial states using a homotopy method. Thereafter, DNNs are designed to learn the functional relationship between the flight states and the optimal actions to enable the capability of optimal action predictions. At last, the performance of the proposed approach is demonstrated through numerical simulations.

关键词： Feedforward Neural Network Hypersonic Vehicles Hamilton Jacobi Bellman Equation Hypersonic Flight Guidance System Numerical Simulation Flight Path Angle Lift Coefficient Earth nonlinear programming

来源：评论

学校读者我要写书评

暂无评论

Real-Time Guidance for Low-Thrust Transfers Using Deep Neural Networks

引用

JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2021年第2期44卷 315-327页

作者： Izzo, Dario Ozturk, Ekin ESA European Space Res & Technol Ctr Adv Concepts Team NL-2201 AZ Noordwijk Netherlands

We consider the Earth-Venus mass-optimal interplanetary transfer of a low-thrust spacecraft and show that the optimal guidance can be represented by deep networks in a large portion of the state space. This opens up the possibility to compute the guidance profile continuously on board the spacecraft, circumventing all convergence issues associated to optimal solvers, and thus constituting a valid alternative to solutions based on the convexification of the underlying optimal control problem. A new general methodology called "backward generation of optimal examples" is proposed to create the data necessary to train the artificial neural networks. With respect to previous works, our databases contain orders of magnitude more optimal trajectories. Several schemes to train representations of either the optimal policy (thrust profile) or the value function (optimal mass) are proposed and tested. We find that they accurately approximate the optimal thrust and that a spacecraft employing these networks would be able to reach the target conditions using only 2% more propellant than in the corresponding mathematically optimal transfer. All networks trained are tested during simulations of interplanetary transfers with respect to their ability to reach optimally the target conditions starting from nominal and off-nominal conditions.

关键词： Artificial Neural Network Thrust Hamilton Jacobi Bellman Equation Propellant Earth Powered Descent Guidance Astronomical Unit nonlinear programming International Celestial Reference Frame Stochastic Gradient Descent

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：