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Adaptive-Mesh sequential convex programming for Space Trajectory Optimization

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2024年第10期47卷 2213-2220页

作者： Kumagai, Naoya Oguri, Kenshiro Purdue Univ Sch Aeronaut & Astronaut W Lafayette IN 47907 USA

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Layout optimization for maximizing wind farm power production using sequential convex programming

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APPLIED ENERGY 2015年第Aug.1期151卷 320-334页

作者： Park, Jinkyoo Law, Kincho H. Stanford Univ Dept Civil & Environm Engn Stanford CA 94305 USA

This paper describes an efficient method for optimizing the placement of wind turbines to maximize the expected wind farm power. In a wind farm, the energy production of the downstream wind turbines decreases due to reduced wind speed and increased level of turbulence caused by the wakes formed by the upstream wind turbines. As a result, the wake interference among wind turbines lower the overall power efficiency of the wind farm. To improve the overall efficiency of a wind farm, researchers have studied the wind farm layout optimization problem to find the placement locations of wind turbines that maximize the expected wind farm power. Most studies on wind farm layout optimization employ heuristic search-based optimization algorithms. In spite of their simplicity, optimization algorithms based on heuristic search are computationally expensive and have limitation in optimizing the locations of a large number of wind turbines since the computational time for the search tends to increase exponentially with increasing number of wind turbines. This study employs a mathematical optimization scheme to efficiently and effectively optimize the locations of a large number of wind turbines with respect to maximizing the wind farm power production. To formulate the mathematical optimization problem, we derive a continuous wake model and express the expected wind farm power as a continuous and smooth function in terms of the locations of the wind turbines. The constructed wind farm power function is then maximized using sequential convex programming (SCP) for the nonlinear mathematical problem. We show how SCP can be used to evaluate the efficiency of an existing wind farm and to optimize a wind farm layout consisting of 80 wind turbines. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Wind power Wind farm layout optimization sequential convex programming

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Multi-stage trajectory planning of dual-pulse missiles considering range safety based on sequential convex programming and artificial neural network

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART G-JOURNAL OF AEROSPACE ENGINEERING 2023年第6期237卷 1449-1463页

作者： Liu, Chaoyue Zhang, Cheng Xiong, Fenfen Wang, Jin Beijing Inst Technol Sch Aerosp Engn Beijing Peoples R China China Acad Launch Vehicle Technol Beijing Peoples R China Minist Educ Key Lab Dynam & Control Flight Vehicle Beijing Peoples R China Beijing Inst Space Launch Technol Beijing Peoples R China

An algorithm based on sequential convex programming and artificial neural networks is proposed to solve the multi-stage trajectory planning problem of dual-pulse missiles considering range safety. Besides nonlinear dynamics and constraints, the dual-pulse missile introduces many discrete optimization variables (such as the ignition time of the second-stage thrust), and the algorithm needs to consider throwing the engine off to a safe location to ensure range safety, which makes trajectory planning for the dual-pulse missile more difficult to solve. In this study, the whole trajectory is first divided into four stages according to the working characteristics of the dual-pulse engine. Second, three new control variables are introduced to realize nonlinear dynamics convexification, and the relaxation technique is used to relax the constraints between the control variables to avoid non-convexity. Then, a range prediction function is designed to predict the landing location of the engine in real time. To improve the real-time prediction speed, an artificial neural network is further used to fit the range prediction function. Finally, an algorithm combining sequential convex optimization and an artificial neural network is proposed to solve the multi-stage trajectory planning problem of dual-pulse missiles accurately and rapidly. By comparing with pseudospectral method, two trajectory planning cases are solved by simulation, and the effectiveness and rapidity of the proposed method are verified.

关键词： dual-pulse missile multi-stage trajectory planning range safety sequential convex programming artificial neural network

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New Adaptive Mesh Refinement Strategy for Entry Guidance via sequential convex programming

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2024年第4期47卷 711-727页

作者： Bae, Juho Kim, Boseok Lee, Chang-Hun Korea Adv Inst Sci & Technol Dept Elect Engn Daejeon 34141 South Korea Korea Adv Inst Sci & Technol Dept Aerosp Engn Daejeon 34141 South Korea

This study focuses on the entry guidance problem, which is a highly nonlinear and constrained optimal control problem. The entry guidance problem is formulated as a nonlinear trajectory optimization problem with six-degrees-of-freedom dynamics, path constraints, and boundary constraints. The formulated problem is discretized and solved using sequential convex programming (SCP) with successive linearization. However, during the discretization process, conventional collocation methods encounter a challenge with high-frequency jittering in the control and bank angle profiles near active path constraints. Because the considered problem especially involves tight path constraints, the solution jittering issue becomes severe. Therefore, this study proposes a novel mesh refinement strategy to address this problem. Unlike conventional mesh refinement strategies based on interpolation error, the proposed approach resolves the convergence and solution jittering issue of the SCP framework by linking the direct and indirect methods with the costate estimation under successive linearization. Along with the development, we introduce a novel costate estimation method and provide proof of its validity. We present numerical simulation results of the proposed method and conduct a comparison with conventional approaches. The results obtained demonstrate successful mitigation of the solution jittering issue and a significant improvement in solution accuracy regarding compliance with the necessary conditions of optimality.

关键词： convex Optimization Reentry Guidance Adaptive Mesh Refinement sequential convex programming

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Very large scale optimization by sequential convex programming

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OPTIMIZATION METHODS & SOFTWARE 2004年第1期19卷 103-120页

作者： Zillober, C Schittkowski, K Moritzen, K Univ Bayreuth Dept Math D-95440 Bayreuth Germany Univ Dortmund Inst Appl Math D-44221 Dortmund Germany

We introduce a method for constrained nonlinear programming that is widely used in mechanical engineering and that is known under the name SCP for sequential convex programming. The algorithm consists of solving a sequence of convex and separable subproblems, where an augmented Lagrangian merit function is used for guaranteeing convergence. Originally, SCP methods were developed in structural mechanical optimization, and are particularly applied to solve topology optimization problems. These problems are extremely large and possess dense Hessians of the objective function. The purpose of the article is to show that constrained dense nonlinear programs with 10 5 -10 6 variables can be solved successfully and that SCP methods can be applied also to optimal control problems based on semilinear elliptic partial differential equations after a full discretization.

关键词： large scale nonlinear programming sequential convex programming topology optimization optimal control of partial differential equations semilinear elliptic equations

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Cooperative wind turbine control for maximizing wind farm power using sequential convex programming

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ENERGY CONVERSION AND MANAGEMENT 2015年 101卷 295-316页

作者： Park, Jinkyoo Law, Kincho H. Stanford Univ Dept Civil & Environm Engn Stanford CA 94305 USA

This paper describes the use of a cooperative wind farm control approach to improve the power production of a wind farm. The power production by a downstream wind turbine can decrease significantly due to reduced wind speed caused by the upstream wind turbines, thereby lowering the overall wind farm power production efficiency. In spite of the interactions among the wind turbines, the conventional (greedy) wind turbine control strategy tries to maximize the power of each individual wind turbine by controlling its yaw angle, its blade pitch angle and its generator torque. To maximize the overall wind farm power production while taking the wake interference into account, this study employs a cooperative control strategy. We first derive the wind farm power as a differentiable function of the control actions for the wind turbines in a wind farm. The wind farm power function is then maximized using sequential convex programming (SCP) to determine the optimum coordinated control actions for the wind turbines. Using an example wind farm site and available wind data, we show how the cooperative control strategy improves the power production of the wind farm. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Power production Wind farm Cooperative control Continuous wake model sequential convex programming

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Oscillation Phenomenon in Trust-Region-Based sequential convex programming for the Nonlinear Trajectory Planning Problem

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2022年第4期58卷 3337-3352页

作者： Xie, Lei He, Rui-Zhi Zhang, Hong-Bo Tang, Guo-Jian Natl Univ Def Technol Coll Aerosp Sci & Engn Changsha 410073 Peoples R China

The trust-region-based sequential convex programming (TSCP) method is designed to solve the nonlinear trajectory planning problem using successive linearization and trust region. The TSCP method has a good real-time performance suitable for onboard aerospace applications. However, the main challenge in practical applications is its poor convergence. In this article, we reveal an oscillation phenomenon, which is an important factor affecting the convergence. The main contribution of this article is threefold: 1) the oscillation phenomenon is proved to be an inherent property of the TSCP method for the nonlinear trajectory planning problem;2) the oscillation condition that determines whether oscillation occurs is obtained;and 3) the oscillation-condition-based remedy to address the oscillation is presented. The effectiveness and robustness of the proposed remedy have been verified via a complex reentry trajectory planning problem.

关键词： Oscillators Trajectory planning programming Convergence Aerospace engineering Trajectory Planning Nonlinear trajectory planning oscillation phenomenon sequential convex programming trust region

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Low-Thrust Transfer Orbit Optimization Using sequential convex programming and Adaptive Mesh Refinement

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JOURNAL OF SPACECRAFT AND ROCKETS 2024年第2期61卷 570-587页

作者： Zhao, Jisong Li, Jia Li, Shuang Nanjing Univ Aeronaut & Astronaut Dept Astronaut Syst Engn Nanjing 210016 Peoples R China Nanjing Univ Aeronaut & Astronaut Dept Aerosp Control Engn Nanjing 210016 Peoples R China

In this paper, a mesh-adaptive Hermite-Simpson sequential convex programming (SCP) method is proposed for solving low-thrust orbit transfer problems efficiently and accurately. First, we develop a Hermite-Simpson convex programming method that utilizes quadratic polynomial control interpolation to improve discretization accuracy. Then, a mesh-adaptive Hermite-Simpson SCP method is proposed by incorporating an improved adaptive mesh refinement method into the Hermite-Simpson SCP method for solving optimal control accurately and efficiently. The proposed method is applied to two-dimensional time-optimal and three-dimensional fuel-optimal low-thrust orbit transfer problems to show its features, and its performance is compared against several existing methods. Numerical simulations show that the proposed method achieves more accurate solutions while being computationally more efficient compared to the trapezoidal SCP method. In comparison to the GPOPS-II software, the proposed method produces solutions of comparable accuracy but is more computationally efficient.

关键词： sequential convex programming Adaptive Mesh Refinement Low-Thrust Transfer Orbit Optimization optimal control

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Trust-region filtered sequential convex programming for multi-UAV trajectory planning and collision avoidance?

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ISA TRANSACTIONS 2022年第PartB期128卷 664-676页

作者： Xu, Guangtong Long, Teng Wang, Zhu Sun, Jingliang Tsinghua Univ Dept Precis Instrument Beijing 100084 Peoples R China Beijing Inst Technol Sch Aerosp Engn Beijing 100081 Peoples R China Minist Educ Key Lab Dynam & Control Flight Vehicle Beijing 100081 Peoples R China North China Elect Power Univ Dept Automat Baoding 071003 Hebei Peoples R China

This paper presents an trust-region filtered sequential convex programming (TRF-SCP) to reduce computational burdens of multi-UAV trajectory planning. In TRF-SCP, the trust-region based filter is proposed to remove the inactive collision-avoidance constraints of the convex programming subproblems for decreasing the complexity. The inactive constraints are detected based on the intersection relations between trust regions and collision-avoidance constraints. The trust-region based filter for different types of obstacles are tailored to address complex scenarios. An adaptive trust -region updating mechanism is also developed to mitigate infeasible iteration in TRF-SCP. The sizes of the trust regions are automatically adjusted according to the constraint violation of the optimized trajectory during the SCP iterations. TRF-SCP is then tested on several numerical multi-UAV formation scenarios involving cylindrical, spherical, conical, and polygon obstacles, respectively. Comparative studies demonstrate that TRF-SCP eliminates a large number of collision-avoidance constraints in the entire iterative process and outperforms SCP and Guaranteed sequential Trajectory Optimization in terms of computational efficiency. The indoor flight experiments are presented to further evaluate the practicability of TRF-SCP.(c) 2021 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Multi-UAV trajectory planning sequential convex programming Trust -region based filter Adaptive trust -region updating Inactive constraints

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Flight trajectory and energy management coupled optimization for hybrid electric UAVs with adaptive sequential convex programming method

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APPLIED ENERGY 2024年 364卷

作者： Tian, Weiyong Liu, Li Zhang, Xiaohui Shao, Jiaqi Beijing Inst Technol Sch Aerosp Engn Beijing 100081 Peoples R China

Improving energy efficiency has important significance for hybrid electric UAVs. This paper aims at improving energy efficiency by maximizing utilization solar energy, reducing demand power and optimizing power allocation. The flight trajectory optimization and energy management are considered synthetically, and the coupled model is established. Adaptive sequential convex programming method (ASCP) is further proposed to solve this coupled model. The adaptive discretization method, fuzzy -based trust region update mechanism and two -stages solving strategy are proposed to improve its optimality and convergence. The optimized flight trajectory and global optimal battery SOC trajectory can be applied to trajectory tracking control and online energy management. Numerical simulation results show that ASCP can improve energy efficiency of hybrid electric UAVs. Compared with uniform discretized sequential convex programming (UDSCP) and Gauss pseudospectral method (GPM), it can improve solar energy by 9.3% and 24.1%. Energy management experiment results indicate that ASCP has excellent energy saving effect. Compared with nonlinear model predictive control (NMPC), fuzzy logic state machine (FLSM) and passive energy management strategy (PEMS), it can save hydrogen by 13.5%, 18.3%, and 22.2%, respectively. This work is conducive to promoting the application of renewable energy in UAVs.

关键词： Energy efficiency Energy management Hybrid electric UAVs Trajectory optimization sequential convex programming Hydrogen energy

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