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A convex Approach to High-fidelity Landing Trajectory Optimization for Advanced Air Mobility

A Convex Approach to High-fidelity Landing Trajectory Optimi...

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AIAA SciTech Forum

作者： Wu, Yufei Deniz, Sabrullah Wang, Zhenbo Huang, Daning Univ Tennessee Dept Mech Aerosp & Biomed Engn Knoxville TN 37996 USA Penn State Univ Dept Aerosp Engn University Pk PA 16802 USA

ISBN: (数字)9781624107115

ISBN: (纸本)9781624107115

Urban Air Mobility (UAM) presents an innovative solution for intra-urban and inter-urban transportation, promising enhanced flexibility, efficiency, and sustainability. However, the integration of UAM into densely populated city environments brings significant challenges, particularly in the precision landing of multi-rotor vehicles amid complex and dynamic urban landscapes. To address this challenge, our paper introduces a novel convex optimization approach to solve the high-fidelity landing problem of electric vertical take-off and landing (eVTOL) vehicles. In our method, we first conceptualize the eVTOL vehicle landing trajectory optimization as a high-dimensional, highly nonconvex optimal control problem. We then implement a series of convenient convexification techniques to transform this problem into a convex form. The core of our approach lies in the application of sequential convex programming (SCP), an advanced method known for its efficacy and real-time performance in handling complex optimization challenges. We conduct a comparative analysis of our SCP-based solution with results obtained from the GPOPS-II solver, a widely recognized general-purpose tool in optimal control. This comparison not only benchmarks the performance of our method but also highlights its potential advantages in solving complicated, dynamic trajectory optimization problems in the context of UAM.

关键词： Optimal Control Problem Advanced Air Mobility Electric Vertical Take off and Landing convex Optimization sequential convex programming Precision Landing Multi Rotor Aerodynamic Coefficients Blade Element Momentum Theory Flight Dynamics

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Energy Efficient Altitude Optimization of an Aerial Access Point 31

Energy Efficient Altitude Optimization of an Aerial Access P...

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31st Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (IEEE PIMRC)

作者： Babu, Nithin Ntougias, Konstantinos Papadias, Constantinos B. Popovski, Petar Amer Coll Greece Res Technol & Innovat Network RTIN Athens Greece Aalborg Univ Dept Elect Syst Aalborg Denmark Univ Cyprus Nicosia Cyprus

ISBN: (纸本)9781728144900

In this paper, we propose an energy-efficient optimal altitude for an aerial access point (AAP), which acts as a flying base station to serve a set of ground user equipment (UE). Since the ratio of total energy consumed by the aerial vehicle to the communication energy is very large, we include the aerial vehicle's energy consumption in the problem formulation. After considering the energy consumption model of the aerial vehicle, our objective is translated into a non-convex optimization problem of maximizing the global energy efficiency (GEE) of the aerial communication system, subject to altitude and minimum individual data rate constraints. At first, the non-convex fractional objective function is solved by using sequential convex programming (SCP) optimization technique. To compare the result of SCP with the global optimum of the problem, we reformulate the initial problem as a monotonic fractional optimization problem (MFP) and solve it using the polyblock outer approximation (PA) algorithm. Numerical results show that the candidate solution obtained from SCP is the same as the global optimum found using the monotonic fractional programming technique. Furthermore, the impact of the aerial vehicle's energy consumption on the optimal altitude determination is also studied.

关键词： Global energy efficiency UAV communication altitude Optimization sequential convex programming monotonic optimization

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Regularized System Identification: A Hierarchical Bayesian Approach 21st

Regularized System Identification: A Hierarchical Bayesian A...

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21st IFAC World Congress on Automatic Control - Meeting Societal Challenges

作者： Khosravi, Mohammad Iannelli, Andrea Yin, Mingzhou Parsi, Anilkumar Smith, Roy S. ETH Automat Control Lab CH-8092 Zurich Switzerland

In this paper, the hierarchical Bayesian method for regularized system identification is introduced. To this end, a hyperprior distribution is considered for the regularization matrix and then, the impulse response and the regularization matrix are jointly estimated based on a maximum a posteriori (MAP) approach. Toward introducing a suitable hyperprior, we decompose the regularization matrix using Cholesky decomposition and reduce the estimation problem to the cone of upper triangular matrices with positive diagonal entries. Following this, the hyperprior is introduced on a designed sub-cone of this set. The method differs from the current trend in regularized system identification from various aspect, e.g., the estimation is performed by solving a single stage problem. The MAP estimation problem reduces to a multi-convex optimization problem and a sequential convex programming algorithm is introduced for solving this problem. Consequently, the proposed method is a computationally efficient strategy specially when the regularization matrix has a large size. The method is numerically verified on benchmark examples. Owing to the employed full Bayesian approach, the estimation method shows a satisfactory bias-variance trade-off. Copyright (C) 2020 The Authors.

关键词： System identification hierarchical Bayesian sequential convex programming

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Avoidance Trajectory Generation for Quad-Rotors by Polygonal Bounding of Congested Obstacles

Avoidance Trajectory Generation for Quad-Rotors by Polygonal...

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AIAA SciTech Forum

作者： Arai, Yoshihide Sago, Takashi Ueyama, Yuki Harada, Masanori Natl Def Acad Japan Dept Mech Engn Yokosuka Kanagawa 2398686 Japan

ISBN: (数字)9781624107115

ISBN: (纸本)9781624107115

This study investigates a method for generating obstacle avoidance trajectories by clustering congested obstacles with polygons. The method simplifies cluttered obstacles, which would be computationally expensive without clustering, by clustering and enclosing them in convex polygons. To avoid obstacles enclosed in convex polygons, we propose cross product avoidance constraints (CPACs). They are constraints of a continuous equation using the condition that evaluation points are located outside of convex polygons by the cross product and can be incorporated into the optimization computation to generate avoidance trajectories. Numerical simulations are performed to generate avoidance trajectories using various configurations of congested obstacles. The results show that the proposed method can generate avoidance trajectories for various obstacle configurations and reduce the computational complexity.

关键词： Quad Rotor Obstacle Avoidance Numerical Simulation Optimal Control Problem Linkages Unmanned Aerial Vehicle MATLAB Unmanned Underwater Vehicles Unmanned Ground Vehicle sequential convex programming

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Nonlinear programming: Algorithms, software, and applications - From small to very large scale optimization

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21st Conference on System Modeling and Optimization

作者： Schittkowski, K Zillober, C Univ Bayreuth Dept Comp Sci D-95440 Bayreuth Germany

ISBN: (纸本)1402077602

We introduce some methods for constrained nonlinear programming that are widely used in practice and that are known under the names SQP for sequential quadratic programming and SCP for sequential convex programming. In both cases, convex subproblems are formulated, in the first case a quadratic programming problem, in the second case a separable nonlinear program in inverse variables. The methods are outlined in a uniform way and the results of some comparative performance tests are listed. We especially show the suitability of sequential convex programming methods to solve some classes of very large scale nonlinear programs, where implicitly defined systems of equations seem to support the usage of inverse approximations. The areas of interest are structural mechanical optimization, i.e., topology optimization, and optimal control of partial differential equations after a full discretization. In addition, a few industrial applications and case studies are shown to illustrate practical situations under which the codes implemented by the authors are in use.

关键词： sequential quadratic programming SQP sequential convex programming SCP comparative tests industrial applications topology optimization optimal control of semilinear elliptic equations

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UAV trajectory planning for 5G network coverage

UAV trajectory planning for 5G network coverage

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IEEE/CIC International Conference on Communications in China (ICCC)

作者： Ouyang, Chenyu Zhong, Xiaofeng Zhou, Shidong Li, Jingwei Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol Dept Elect Engn Beijing 100084 Peoples R China

ISBN: (纸本)9798350378412

In this paper, we study the trajectory planning for fixed-wing unmanned aerial vehicles (UAVs) equipped with directional antenna to improve communication network coverage. First, to better depict the connection states between the UAV base stations and ground users, a signal coverage model for UAV equipped with directional antenna is proposed, representing the UAV-to-ground coverage area as a dynamic ellipse. Based on this, we formulate the UAV trajectory planning problem as a mixed integer nonlinear programming (MINP) problem to maximize the coverage rate, accounting for the maneuverability characteristics of fixed-wing UAVs, such as the minimum velocity and safe turning radius. To solve this, an algorithm based on sequential convex programming (SCP) is adopted. Further, for multiple UAVs, we propose to cluster users periodically and assign UAVs to each cluster aiming to decreasing computation overhead and improving overall coverage areas. The optimal trajectory in each cluster can be obtained independently utilizing the preceding algorithm. With knowledge of the time-varying clustering results, we utilize Dubins Curve to design the transition path for a UAV navigating from its current cluster to the one assigned by Hungarian algorithm in the subsequent period. Realistic measurement shows that our signal coverage model is more practical. And simulation results exhibits that our periodic clustering scheme gains improvement on coverage rate compared with baseline methods.

关键词： trajectory planning network coverage directional antenna sequential convex programming fixed-wing UAV

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Network Analysis, Control Valve Placement and Optimal Control of Flow Velocity for Self-Cleaning Water Distribution Systems 18th

Network Analysis, Control Valve Placement and Optimal Contro...

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18th International Conference on Water Distribution System Analysis (WDSA)

作者： Abraham, E. Blokker, E. J. M. Stoianov, I. Imperial Coll London Dept Civil & Environm Engn London SW7 UB England KWR Watercycle Res Inst NL-3430 BE Nieuwegein Netherlands

In this paper, we consider the proactive control of flow velocities to maximise the self-cleaning capacity of the drinking water distribution systems under normal operations both through a change of the network topology and through an optimal control of pressure reducing valve (PRV) settings. Inspired by line outage flow distribution in electrical networks, we show how a fast network graph analysis of link closures can be used to estimate the potential changes in flow velocities, which are then used to determine the most favourable pipes for closure. Where closing of pipes cannot be used because of other conflicting objectives, we consider the optimal control of PRVs to maximise self-cleaning at peak demand periods. We formulate a novel optimisation problem to maximise the network operations for increased self-cleaning capacity, while satisfying hydraulic and regulatory pressure constraints at demand nodes. A new smooth objective function approximation for cleaning capacity of the network is proposed along with a scalable sequential convex programming method to solve the resulting valve optimization problems. We use a published benchmark network as a case study to show the efficacy of these new approaches. (C) 2016 The Authors. Published by Elsevier Ltd.

关键词： Water distribution systems discolouration proactive management self-cleaning network optimal PRV control water flow-redistribution matrix control valve placement multi-objective trade-offs sequential convex programming

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A convex Optimization Method for Multiple Encounters Collision Avoidance Maneuvers

A Convex Optimization Method for Multiple Encounters Collisi...

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AIAA SciTech Forum

作者： Pavanello, Zeno Pirovano, Laura Armellin, Roberto De Vittori, Andrea di Lizia, Pierluigi Univ Auckland Te Punaha Atea Space Inst Dept Mech Engn 20 Symonds St Auckland 1010 New Zealand Politecn Milan Dept Aerosp Sci & Thehnol DAER Piazza Leonardo Da Vinci 32 I-20133 Milan Italy

ISBN: (数字)9781624107115

ISBN: (纸本)9781624107115

This work presents a novel approach for the optimization of fuel-optimal collision avoidance maneuvers (CAMs) in scenarios involving multiple encounters. The considered risk mitigation strategy spans beyond immediate conjunctions, encompassing subsequent encounters over an extended window of interest. Two operational cases are considered: multiple encounters between a maneuverable primary spacecraft and a set of secondaries and repeating encounters between a spacecraft and a single secondary. The optimization's objective is the minimization of the total fuel consumption while respecting conditions on the minimization of the total probability of collision (TPoC). Only short-term encounters are addressed in this work, but the proposed approach is also valid for long-term encounters. In the former case, the TPoC is a nonlinear combination of the probability of collision for the single conjunctions. Moreover, a Gaussian mixture model method is used to propagate the uncertainty of the secondary spacecraft. This allows for the splitting of the secondary into multiple mixands, which are treated as if they were different objects. The problem is solved using convex optimization due to its demonstrated effectiveness in addressing complex aerospace engineering problems. It combines sequential convex programming, second-order cone programming, and differential algebra to approximate the non-convex optimal control problem progressively. Additionally, a trust region technique is introduced to enhance convergence. The efficiency of the approach is demonstrated through case studies involving multiple encounters, showcasing the generation of fuel-efficient CAMs.

关键词： convex Optimization Collision Avoidance Optimal Control Problem Gaussian Mixture Models sequential convex programming Aerospace Engineering Fuel Consumption Orbital Period Earth Centered Inertial Space Debris

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A JOINT DESIGN APPROACH FOR SPECTRUM SHARING BETWEEN RADAR AND COMMUNICATION SYSTEMS 41

A JOINT DESIGN APPROACH FOR SPECTRUM SHARING BETWEEN RADAR A...

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41st IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Li, Bo Kumar, Harshat Petropulu, Athina P. Rutgers State Univ ECE Dept Piscataway NJ 08901 USA

ISBN: (纸本)9781479999880

A joint design approach is proposed for the coexistence of MIMO radars and a communication system, for a scenario in which the targets fall in different range bins. Radar transmit precoding and adaptive communication transmission are adopted, and are jointly designed to maximize signal-to-interference-plus-noise ratio (SINR) at the radar receiver subject to the communication system meeting certain rate and power constraints. We start with the design of a system in which knowledge of the target information is used. Such design can be used to benchmark the performance of schemes that do not use target information. Then, we propose a design which does not require target information. In both cases, the optimization problems are nonconvex with respect to the design variables and have high computational complexity. Alternating optimization and sequential convex programming techniques are used to find a local maximum. Based on the analysis of the obtained solution, we propose a reduced dimensionality design, which has reduced complexity without degrading the radar SINR. Simulation results validate the effectiveness of the proposed spectrum sharing framework.

关键词： Collocated MIMO radar spectrum sharing alternating optimization sequential convex programming

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Robust Control Optimization for Quantum Approximate Optimization Algorithms 21st

Robust Control Optimization for Quantum Approximate Optimiza...

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21st IFAC World Congress on Automatic Control - Meeting Societal Challenges

作者： Dong, Yulong Meng, Xiang Lin, Lin Kosut, Robert Whaley, K. Birgitta Berkeley Ctr Quantum Informat & Computat Berkeley CA 94720 USA Univ Calif Berkeley Dept Chem Berkeley CA 94720 USA Univ Calif Berkeley Dept Math Berkeley CA 94720 USA Lawrence Berkeley Natl Lab Computat Res Div Berkeley CA 94720 USA SC Solut Sunnyvale CA 94085 USA

Quantum variational algorithms have garnered significant interest recently, due to their feasibility of being implemented and tested on noisy intermediate scale quantum (NISQ) devices. We examine the robustness of the quantum approximate optimization algorithm (QAOA), which can be used to solve certain quantum control problems, state preparation problems, and combinatorial optimization problems. We demonstrate that the error of QAOA simulation can be significantly reduced by robust control optimization techniques, specifically, by sequential convex programming (SCP), to ensure error suppression in situations where the source of the error is known but not necessarily its magnitude. We show that robust optimization improves both the objective landscape of QAOA as well as overall circuit fidelity in the presence of coherent errors and errors in initial state preparation. Copyright (C) 2020 The Authors.

关键词： quantum approximate optimization algorithm robust control sequential convex programming error mitigation

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