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Trajectory optimization for spacecraft autonomous rendezvous and docking with compound state-triggered constraints

AEROSPACE SCIENCE AND TECHNOLOGY 2022年第0期127卷

作者： Zhang, Yanquan Zhu, Baolong Cheng, Min Li, Shunli Harbin Inst Technol Sch Astronaut Harbin 150001 Peoples R China Qilu Univ Technol Shandong Acad Sci Sch Informat & Automation Engn Jinan 250353 Peoples R China

In this paper, a guidance algorithm is presented based on successive convexification for the generation of fuel-optimal 6-DOF spacecraft close proximity trajectory subject to multiple path constraints on sensor field-of-view, collision avoidance, and plume impingement. The whole maneuver process is divided into fly-around and docking phases, where the collision avoidance constraints are only considered in the first stage, while the plume impingement restrictions are implemented when the chaser spacecraft lies within the docking corridor. These discrete decisions are embedded into continuous optimization framework by using the scheme of compound state-triggered constraints. Besides, in order to make full use of the control ability of actuator, the translational and rotational dynamics are reformulated with the thruster configuration. The original optimal guidance problem is converted into a series of convex subproblems by the linearization and discretization of nonlinear system dynamics and constraint functions around the reference trajectory, and iteratively solved by convex programming. Particularly, the algorithm is initialized with an imprecise trajectory obtained from pseudospectral method, and the reference states in the subsequent iterations are obtained by the propagation of the system dynamics in each discrete time interval rather than directly inheriting the latest solution. The convergence analysis is presented to prove that the proposed guidance algorithm converges to the stationary point of original problem under mild conditions. Finally, the effectiveness of the algorithm is demonstrated by numerical simulations. (C) 2022 Elsevier Masson SAS. All rights reserved.

关键词： Spacecraft autonomous rendezvous Trajectory optimization sequential convex programming Compound state -triggered constraints Plume impingement constraints

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UNMANNED AERIAL VEHICLE PATH PLANNING USING WATER STRIDER ALGORITHM

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INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING-THEORY APPLICATIONS AND PRACTICE 2024年第3期31卷 429-438页

作者： Sampath, Madhusudhanan Duraisamy, Abitha Kumari Samuel, Amalorpava Mary Rajee Malu, Yamuna Devi Manickam Prathyusha Engn Coll Dept Artificial Intelligence & Data Sci Chennai India RMK Engn Coll Dept Comp Sci & Engn Chennai India Univ Witwatersrand Dept Elect & Informat Engn Johannesburg South Africa Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Vijayawada India

This paper presents a multi-agent optimal path planning and obstacle avoidance using a water strider algorithm (WSA) based on sequential convex programming (SCP). The outcome is to find optimal collision-free trajectories. The best collision-free trajectories with minimum control effect is needed in the multi-agent route planning technique, which makes use of a centralized WSA algorithm that can guide drones over congested environments while avoiding both static and moving objects. By applying convex constraints on the drones' such as acceleration, velocity input and jerk, the feasibility of the trajectory is ensured. The optimal trajectory path is iteratively created using SCP and followed by WSA. The outcome guarantees the correctness of the linearization. Since the optimization is centralized, it is possible to find a feasible collisionfree path, and the results are validated pre-determined formation. It is shown that the WSA algorithm scales with O 3 (n), where n is the number of drones.

关键词： Drone sequential convex programming Water Strider Algorithm Optimization

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Fair Energy-Efficient Resource Allocation for Downlink NOMA Heterogeneous Networks

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IEEE ACCESS 2020年 8卷 200129-200145页

作者： Ali, Zuhura J. Noordin, Nor K. Sali, Aduwati Hashim, Fazirulhisyam Univ Putra Malaysia Fac Engn Dept Comp & Commun Syst Engn Serdang 43400 Malaysia Univ Putra Malaysia Fac Engn Wireless & Photon Networks Res Ctr Excellence WiP Serdang 43400 Malaysia

The increasing in energy consumptions of the current wireless networks, leads towards designing energy-efficient 5G networks. The application of non-orthogonal multiple access (NOMA) in the heterogeneous networks (HetNets) improves the spectrum utilization with the cost of efficient resource allocation. Hence, this article proposes optimal user-pairing and power allocation solutions towards achieving fair energy-efficient resource allocation in downlink femtocell NOMA-HetNets. In the proposed optimization process, the considered constraints are the user's transmission rate, transmit power budget at the base station (BS), and the interference. The energy consumption of both the transmitter and the receiver are considered to simulate the real system design. The Greedy Algorithm (GA) is used to achieve a low-complex optimal solution during the user-pairing process. Simultaneously, the max-min energy efficiency optimization approach is employed to maximize the minimum energy efficiency of the femtocell users to achieve the optimal power allocation solution. The mathematical formulation of the max-min energy efficiency is a non-convex fractional programming problem and is intractable. Thus, the fractional programming theory is adopted to transform the problem into a sequence of subtractive form, followed by the sequential convex programming (SCP) approach to determine the optimal solution. Simulation results show that the proposed NOMA with optimal power allocation method using SCP and GA (NOMA-SCP-GA) achieves fair energy efficiency performance with lower complexity compared to the benchmark methods. Moreover, the minimum energy efficiency of the femtocell user is 38.22% higher than NOMA with Difference of convex programming (NOMA-DC). The NOMA-SCP-GA method can assure 5G capability demands.

关键词： Greedy algorithm heterogeneous network non-orthogonal multiple access power allocation sequential convex programming

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Energy-efficient Power Allocation based on worst-case performance optimization under channel uncertainties

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KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS 2020年第11期14卷 4595-4610页

作者： Song, Xin Dong, Li Huang, Xue Qin, Lei Han, Xiuwei Northeastern Univ Shenyang 110819 Peoples R China

In the practical communication environment, the accurate channel state information (CSI) is difficult to obtain, which will cause the mismatch of resource and degrade the system performance. In this paper, to account for the channel uncertainties, a robust power allocation scheme for a downlink Non-orthogonal multiple access (NOMA) heterogeneous network (HetNet) is designed to maximize energy efficiency (EE), which can ensure the quality of service (QoS) of users. We conduct the robust optimization model based on worse-case method, in which the channel gains belong to certain ellipsoid sets. To solve the non-convex non-liner optimization , we transform the optimization problem via Dinkelbach method and sequential convex programming, and the power allocation of small cell users (SCUs) is achieved by Lagrange dual approach. Finally, we analysis the convergence performance of proposed scheme. The simulation results demonstrate that the proposed algorithm can improve total EE of SCUs, and has a fast convergence performance.

关键词： Non-orthogonal multiple access technology heterogeneous networks energy efficiency channel state information robust power allocation Dinkelbach method sequential convex programming Lagrange dual approach

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An optimal operation strategy for an active power filter using cascaded H-bridges in delta-connection

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ELECTRIC POWER SYSTEMS RESEARCH 2019年 175卷 105918-000页

作者： Wang, Hengyi Liu, Steven Univ Kaiserslautern Inst Control Syst D-67663 Kaiserslautern Germany

An optimal strategy is presented to determine the combined control reference for the terminal and circulating currents of a shunt active power filter (APF) based on the delta-connected cascaded H-bridge (CHB) topology, hereby minimizing the operational power under ideal and non-ideal grid voltages, where the ideal grid voltages contain only the positive-sequence fundamental frequency component and the non-ideal grid voltages contain unbalance and/or harmonic components. The optimization is carried out to simultaneously satisfy the desired grid current distortion limits, the source current imbalance characteristics and the power factor as well as power balance requirement. By properly coordinating terminal current control and circulating current injection of the delta-connected CHB and taking possibly unbalanced and/or distorted grids the total power rating of the APF can be kept at minimum, leading to an economical operation. The optimization problem is formulated using a convex quadratic objective function and non-convex quadratic constraints and solved by an iterative procedure. The simulation results show that the proposed strategy is applicable and effective for reactive power and harmonic compensation with an optimal rating of the APF under ideal and non-ideal grid conditions.

关键词： Optimal operation strategy sequential convex programming Active power filter Delta-connected cascaded H-bridge multilevel converter Non-ideal point of common coupling (PCC) voltages

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Robust scheduling of integrated electricity and gas systems: A cost and flexibility assessment

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2024年 160卷

作者： Akbari, Behnam Sansavini, Giovanni Swiss Fed Inst Technol Inst Energy & Proc Engn Reliabil & Risk Engn Lab Leonhardstr 21 CH-8092 Zurich Switzerland

Gas-fired generators and power-to-gas units support the deployment of variable renewable energy in integrated electricity and gas systems (IEGS) by absorbing the fluctuations in electricity generation and demand. The economical and secure operation of IEGSs with high variable renewable energy penetration requires coordinated uncertainty-aware decision support. This paper proposes an adaptive robust optimization for IEGS scheduling, which minimizes the base-case costs, while ensuring security against generation and demand uncertainties with full-recourse rescheduling. The AC power flow and dynamic gas flow equations are convexified using second- order cone relaxations and treated by sequential convex programming to recover feasible solutions. We assess the flexibility of the solutions in response to the realized uncertainties. Computational experiments on the modified IEEE 39-bus system and the Swiss natural gas system show that sequential convex programming reduces demand shedding by 79%-100% and variable renewable energy curtailment by 1%-68%. Robust scheduling prevents demand shedding at 1%-7% additional cost due to the need for additional generation from conventional generators. Power-to-gas enhances variable renewable energy utilization by synthesizing methane from excess renewable energy, especially when the system lacks other flexible resources.

关键词： Flexibility Integrated energy system Power-to-gas Robust optimization sequential convex programming

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A Hierarchical Autonomous Driver for a Racing Car: Real-Time Planning and Tracking of the Trajectory

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ENERGIES 2021年第19期14卷 6008-6008页

作者： Montani, Margherita Ronchi, Leandro Capitani, Renzo Annicchiarico, Claudio Univ Florence Dept Ind Engn Florence Via Santa Marta 3 I-50139 Florence Italy Meccan 42 Srl Via Ezio Tarantelli 15 I-50019 Florence Italy

The aim of this study was to develop trajectory planning that would allow an autonomous racing car to be driven as close as possible to what a driver would do, defining the most appropriate inputs for the current scenario. The search for the optimal trajectory in terms of lap time reduction involves the modeling of all the non-linearities of the vehicle dynamics with the disadvantage of being a time-consuming problem and not being able to be implemented in real-time. However, to improve the vehicle performances, the trajectory needs to be optimized online with the knowledge of the actual vehicle dynamics and path conditions. Therefore, this study involved the development of an architecture that allows an autonomous racing car to have an optimal online trajectory planning and path tracking ensuring professional driver performances. The real-time trajectory optimization can also ensure a possible future implementation in the urban area where obstacles and dynamic scenarios could be faced. It was chosen to implement a local trajectory planning based on the Model Predictive Control (MPC) logic and solved as Linear programming (LP) by sequential convex programming (SCP). The idea was to achieve a computational cost, 0.1 s, using a point mass vehicle model constrained by experimental definition and approximation of the car's GG-V, and developing an optimum model-based path tracking to define the driver model that allows A car to follow the trajectory defined by the planner ensuring a signal input every 0.001 s. To validate the algorithm, two types of tests were carried out: a Matlab-Simulink, Vi-Grade co-simulation test, comparing the proposed algorithm with the performance of an offline motion planning, and a real-time simulator test, comparing the proposed algorithm with the performance of a professional driver. The results obtained showed that the computational cost of the optimization algorithm developed is below the limit of 0.1 s, and the architecture showed a reduc

关键词： autonomous driving trajectory planning path tracking sequential convex programming linear programming quadratic constraints autonomous racing car

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Regularized System Identification: A Hierarchical Bayesian Approach ⁎

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IFAC-PapersOnLine 2020年第2期53卷 406-411页

作者： Mohammad Khosravi Andrea Iannelli Mingzhou Yin Anilkumar Parsi Roy S. Smith Automatic Control Laboratory ETH Zürich 8092 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.

关键词： System identification hierarchical Bayesian sequential convex programming

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

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Procedia Engineering 2017年 186卷 576-583页

作者： E. Abraham E.J.M. Blokker I. Stoianov Department of Civil and Environmental Engineering Imperial College London London UK SW7 UB KWR Watercycle Research Institute 3430 BE Nieuwegein The 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.

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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Robust Control Optimization for Quantum Approximate Optimization Algorithms

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IFAC-PapersOnLine 2020年第2期53卷 242-249页

作者： Yulong Dong Xiang Meng Lin Lin Robert Kosut K. Birgitta Whaley Berkeley Center for Quantum Information and Computation Berkeley California 94720 USA Department of Chemistry University of California Berkeley California 94720 USA Department of Mathematics University of California Berkeley California 94720 USA Computational Research Division Lawrence Berkeley National Laboratory Berkeley CA 94720 USA SC Solutions Sunnyvale California 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.

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

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