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Safe flight corridor constrained sequential convex programming for efficient trajectory generation of fixed-wing UAVs

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Chinese Journal of Aeronautics 2025年第1期38卷 537-550页

作者： Jing SUN Guangtong XU Zhu WANG Teng LONG Jingliang SUN School of Aerospace Engineering Beijing Institute of TechnologyBeijing 100081China Huzhou Institute Zhejiang UniversityHuzhou 313000China Department of Automation North China Electric Power University(Baoding)Baoding 071003China

Generating dynamically feasible trajectory for fixed-wing Unmanned Aerial Vehicles(UAVs)in dense obstacle environments remains computationally *** paper proposes a Safe Flight Corridor constrained sequential convex programming(SFC-SCP)to improve the computation efficiency and reliability of trajectory ***-SCP combines the front-end convex polyhedron SFC construction and back-end SCP-based trajectory optimization.A Sparse A^(*)Search(SAS)driven SFC construction method is designed to efficiently generate polyhedron SFC according to the geometric relation among obstacles and collision-free *** transforming the nonconvex obstacle-avoidance constraints to linear inequality constraints,SFC can mitigate infeasibility of trajectory planning and reduce computation ***,SCP casts the nonlinear trajectory optimization subject to SFC into convex programming subproblems to decrease the problem *** addition,a convex optimizer based on interior point method is customized,where the search direction is calculated via successive elimination to further improve *** experiments on dense obstacle scenarios show that SFC-SCP can generate dynamically feasible safe trajectory *** studies with state-of-the-art SCP-based methods demonstrate the efficiency and reliability merits of ***,the customized convex optimizer outperforms off-the-shelf optimizers in terms of computation time.

关键词： Fixed-wing unmanned aerial vehicle Efficient trajectory planning Safe flight corridor sequential convex programming Customized convex optimizer

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Time-Coordinated Motion Planning for Multiple Unmanned Vehicles Using Distributed Model Predictive Control and sequential convex programming

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL 2025年第8期35卷 3240-3255页

作者： Liu, Xiaoming Wu, Fuchun Deng, Yunshan Wang, Ming Xia, Yuanqing Beijing Inst Technol Sch Automat Beijing Peoples R China North Automat Control Technol Inst Taiyuan Peoples R China Zhongyuan Univ Technol Zhengzhou Peoples R China

This paper introduces an integrated approach for time-coordinated motion planning of multiple unmanned vehicles using distributed model predictive control (DMPC) and sequential convex programming (SCP). This approach employs a unified framework that integrates trajectory planning and tracking into a single optimization problem, effectively expanding the domain of attraction for the MPC controller and addressing the challenge of time-coordination among multiple vehicles. Non-uniform discrete time scales are introduced to mitigate the dimensionality of the optimization problem, thereby enhancing computational efficiency. By combining the ability of DMPC to distribute computational efforts across multiple vehicles with the iterative convexification method of SCP, our approach efficiently handles the complexities of non-linear optimization. Theoretical analysis has confirmed the feasibility and stability of the proposed method. Based on this approach, the time-coordinated sequential convex programming-based distributed model predictive control (TC-SCP-DMPC) algorithm is proposed. Numerical simulations are conducted to validate the effectiveness and efficiency of the proposed algorithm in achieving time-coordinated control of multiple unmanned vehicles.

关键词： Distributed model predictive control multi-vehicle motion planning sequential convex programming time-coordinated control

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Motion planning of multiple unmanned vehicles using sequential convex programming-based distributed model predictive control

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL 2025年第1期35卷 141-167页

作者： Liu, Xiaoming Wu, Fuchun Deng, Yunshan Wang, Ming Xia, Yuanqing North Automat Control Technol Inst Taiyuan Peoples R China Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China Zhongyuan Univ Technol Zhengzhou 450007 Peoples R China

This article tackles the complex challenge of multi-vehicle motion planning by presenting the novel sequential convex programming-based Distributed Model Predictive Control (SCP-DMPC) algorithm. Existing methods often struggle with the nonconvex nature of optimization in multi-vehicle motion planning, frequently compromising on computational efficiency. SCP-DMPC innovatively combines the predictive control of DMPC with the optimization prowess of SCP to address these issues efficiently, while adhering to both physical and operational constraints. To ensure precise attainment of target poses, a three-stage control strategy is introduced, with theoretical analysis on its recursive feasibility and asymptotic stability, enhancing the reliability of the algorithm. Moreover, a heuristic-based deadlock resolution scheme is devised to prevent vehicle stalling, a common issue in cooperative motion. Validated through simulations in challenging scenarios, including symmetric position swapping and obstacle-laden formation transition, SCP-DMPC demonstrates superior adaptability, precision, and efficiency. These results underscore its potential for robust, real-time unmanned vehicle applications, suggesting new directions for future research and development in the field.

关键词： collision avoidance distributed model predictive control multi-vehicle motion planning sequential convex programming

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Lossless Control-convex Formulation for Solar-Sail Trajectory Optimization via sequential convex programming

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2025年第2期48卷 311-326页

作者： Oguri, Kenshiro Lantoine, Gregory Purdue Univ Sch Aeronaut & Astronautics W Lafayette IN 47907 USA CALTECH Mission Design & Nav Sect Pasadena CA 91109 USA

sequential convex programming (SCP) has been gaining popularity for space trajectory optimization. However, application of SCP for solar-sail trajectory optimization has suffered from the nonlinear coupling between the magnitude and direction of solar radiation pressure acceleration, which renders the optimal control problem highly nonconvex in the control variables. To address this issue, this paper proposes a formulation that transforms the solar-sail optimal control problem into a problem that is convex with respect to control (called control-convex). This convexification is achieved by introducing a slack variable and applying a change of variable. This paper mathematically shows the lossless property of the proposed control-convex formulation by using Pontryagin's minimum principle, and develops an SCP-based trajectory optimization algorithm for solar sails. We then apply the developed trajectory optimization algorithm to two interplanetary-transfer scenarios, demonstrating its effectiveness in solving complex sail optimal control problems efficiently and robustly, with better optimality compared to a conventional formulation.

关键词： sequential convex programming Pontryagin's Minimum Principle Asteroids Lagrange Multipliers Space Mission Design Solar Sail Guidance Navigation Control Trajectory Optimization convex Optimization

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Formation tracking control for unmanned vehicles using enhanced sequential convex programming-based model predictive control

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JOURNAL OF THE FRANKLIN INSTITUTE 2025年第6期362卷

作者： Liu, Xiaoming Wu, Fuchun Deng, Yunshan Wang, Ming Xia, Yuanqing Beijing Inst Technol Sch Automat 5 South St Beijing 100081 Peoples R China North Automat Control Technol Inst 351 Tiyu Rd Taiyuan 030006 Peoples R China Zhongyuan Univ Technol 41 Zhongyuan Rd Zhengzhou 450007 Peoples R China

This paper proposes an enhanced sequential convex programming-based model predictive control (ESCPMPC) scheme for formation tracking control problems. Considering coupled input constraints, a tracking error dynamic equation is established based on the position error between the leader and the follower, and a model predictive controller (MPC) is formulated for formation tracking. To improve the real-time control capability, we integrate MPC with sequential convex programming (SCP) by linearizing kinematics and convexifying obstacle avoidance constraints, thereby transforming the nonconvex optimization into a series of convex subproblems. While this approach efficiently approximates the solution to the original nonconvex problem, the linearization errors introduced during each SCP iteration can accumulate and potentially make the optimization problem infeasible. To address this issue, we propose an enhanced SCP (ESCP) method, which corrects these linearization errors. To ensure system stability, a terminal controller and a corresponding terminal set are computed. The recursive feasibility and stability of the proposed method are theoretically demonstrated. Finally, numerical simulations validate the effectiveness and computational efficiency of the proposed method in achieving formation tracking control for unmanned vehicles.

关键词： Formation tracking control Model predictive control sequential convex programming Unmanned vehicle

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Smoothing-homotopy-based sequential convex programming for trajectory optimization

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AEROSPACE SCIENCE AND TECHNOLOGY 2025年 159卷

作者： Zhao, Mengxin Pan, Binfeng Hou, Xiyu Huang, Longxin Northwestern Polytech Univ Sch Astronaut Xian 710072 Shaanxi Peoples R China

This paper proposes a new smoothing-homotopy-based sequential convex programming (SCP) method for general trajectory optimization problems. The surrogates, derived from convolving the smoothing kernel with the terminal states, are firstly incorporated into the terminal constraints as replacements of the original ones. The smoothing parameter, taken as the homotopic parameter, decreases from a larger value to zero, with which the corresponding optimization problem gradually transitions from an easier and smoothed counterpart to the original one. Both the modified Chebyshev-Picard iteration (MCPI) approach and the trapezoidal rule are employed to transcribe the continuous-time optimization problem into a series of finite-dimensional subproblems, respectively, which are then solved by the primal-dual interior-point solver with the aid of convexity techniques. Numerical simulations for an ascent trajectory optimization problem are provided to demonstrate the performance of the proposed method, showcasing its superior convergence compared to the standard SCP methods.

关键词： Smoothing homotopy Modified Chebyshev-Picard iteration sequential convex programming Trajectory optimization

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A New Trust-Region Constraint Method for sequential convex programming Framework for Entry Guidance

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INTERNATIONAL JOURNAL OF AERONAUTICAL AND SPACE SCIENCES 2025年第3期26卷 1216-1233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 paper introduces an improved method for imposing trust-region constraints within the sequential convex programming framework, particularly aiming to reduce unnecessary iterations in applications for the entry guidance problem. The proposed method leverages the dual solution to detect oscillations in the solution across iterations and to determine whether the solution is near optimal. Subsequently, a penalized form of the trust-region constraint is applied to enhance convergence and accurately satisfy the nonlinear constraints. Numerical simulations on the entry guidance problem are presented to validate the performance of the proposed method. The results demonstrate a significant reduction in the number of iterations, with only a minimal trade-off in cost functional value and convergence success rate.

关键词： Entry guidance sequential convex programming Trust region constraint Gradient descent

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sequential convex programming for nonlinear optimal control problems in UAV path planning

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AEROSPACE SCIENCE AND TECHNOLOGY 2018年 76卷 280-290页

作者： Zhang, Zhe Li, Jianxun Wang, Jun Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Dept Automat 800 Dong Chuan Rd Shanghai Peoples R China Luoyang Elect Equipment Test Ctr China Luoyang Peoples R China

Usually, an UAV (Unmanned Aerial Vehicle) path planning problem can be modeled as a nonlinear optimal control problem with non-convex constraints in practical applications. However, it is quite difficult to obtain stable solutions quickly for this kind of non-convex optimization with certain convergence and optimality. In this paper, an algorithm is proposed to solve the problem through approximating the non-convex parts by a series of sequential convex programming problems. Under mild conditions, the sequence generated by the proposed algorithm is globally convergent to a KKT (Karush-Kuhn-Tucker) point of the original nonlinear problem, which is verified by a rigorous theoretical proof. Compared with other methods, the convergence and effectiveness of the proposed algorithm is demonstrated by trajectory planning applications. (C) 2018 Elsevier Masson SAS. All rights reserved.

关键词： UAV path planning sequential convex programming Nonlinear optimal control Globally convergent algorithm Non-convex programming

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sequential convex programming approach for real-time guidance during the powered descent phase of mars landing missions

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ADVANCES IN SPACE RESEARCH 2021年第11期68卷 4398-4417页

作者： Kwon, Dongyoung Jung, Youyeun Cheon, Yee-Jin Bang, Hyochoong Korea Adv Inst Sci & Technol Daejeon 34141 South Korea Korea Aerosp Res Inst Daejeon 34133 South Korea

An on-board guidance generation approach for Mars pinpoint landing has been developed based on sequential convex programming. To optimize the flight time in the formulation, a discretized form of the landing problem was constructed under the conditions of linearized states, controls, and time increment between consecutive time steps. To implement this strategy in real time, on-board demonstrations were conducted with the GR740, which is the next-generation on-board processor of choice for the European Space Agency. The total number of time steps was determined based on the results of these demonstrations. The numerically simulated results also indicate that the solution obtained via the proposed strategy is close to the optimized GPOPS-II solution under the condition of no disturbance. Even under disturbances such as navigation errors, initial prediction errors, and perturbation forces, the proposed strategy ensures that the spacecraft reaches its target position with near-optimal fuel consumption. (C) 2021 COSPAR. Published by Elsevier B.V. All rights reserved.

关键词： convex programming sequential convex programming Powered descent phase On-board guidance

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sequential convex programming without penalty function for reentry trajectory optimization problem

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ACTA ASTRONAUTICA 2024年 225卷 402-416页

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

sequential convex programming (SCP) has been extensively utilized in reentry trajectory optimization due to its high computational efficiency. However, the current SCP approaches primarily rely on penalty function, where the selection of the penalty function weight presents a significant challenge. In this paper, an improved trust region shrinking SCP algorithm is proposed that separates the treatment of the objective function and constraint violation without the need for selecting penalty function weight and introduction of slack variables. Firstly, from the perspective of multi-objective optimization, the filter and acceptance condition are introduced to ensure that the proposed algorithm converges to feasible solutions and then to the optimal solution based on switching condition and sufficient condition. Then an effective feasibility restoration phase is proposed to address infeasibility of subproblems without introducing slack variables, while ensuring the robustness of the proposed algorithm. Additionally, a theoretical analysis is provided to guarantee the convergence of the algorithm. Finally, simulations are conducted to verify that the proposed algorithm demonstrates a 69.54% improvement in average solution time and stronger robustness compared to basic trust region shrinking SCP algorithm. Simultaneously, the proposed algorithm also demonstrates an advantage in solving speed compared to a particular advanced penalty function-based SCP algorithm.

关键词： Reentry trajectory optimization sequential convex programming Penalty function

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