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Survey of autonomous guidance methods for powered planetary landing Review

Zheng-yu Song, Cong Wang, Stephan Theil, David Seelbinder, Marco Sagliano, Xin-fu Liu, Zhi-jiang Shao,zycalt12@sina.com

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 5,   Pages 649-808 doi: 10.1631/FITEE.1900458

Abstract: This paper summarizes the s (AGMs) for on celestial surfaces. We first review the development of guidance methods, focusing on their contributions for dealing with constraints and enhancing computational efficiency. With the increasing demand for reusable launchers and more scientific returns from space exploration, has become a basic requirement. Unlike the kilometer-level precision for previous activities, the position accuracy of future planetary landers is within tens of meters of a target respecting all constraints of velocity and attitude, which is a very difficult task and arouses renewed interest in AGMs. This paper states the generalized three- and six-degree-of-freedom optimization problems in the phase and compares the features of three typical scenarios, i.e., the lunar, Mars, and Earth landing. On this basis, the paper details the characteristics and adaptability of AGMs by comparing aspects of analytical guidance methods, numerical optimization algorithms, and learning-based methods, and discusses the convexification treatment and solution strategies for non-convex problems. Three key issues related to AGM application, including physical feasibility, model accuracy, and real-time performance, are presented afterward for discussion. Many space organizations, such as those in the United States, China, France, Germany, and Japan, have also developed free-flying demonstrators to carry out related research. The guidance methods which have been tested on these demonstrators are briefly introduced at the end of the paper.

Keywords： 自主制导方法；定点软着陆；动力下降；非线性规划    

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Pulse Transiently Chaotic Neural Network for Nonlinear Constrained Optimization

Li Ying,Cao Hongzhao,Hu Yunchang,Shang Xiuming

Strategic Study of CAE 2004, Volume 6, Issue 5,   Pages 45-48

Abstract:

Pulse Transiently Chaotic Neural Network (PTCNN) can find almost all optima including the part optima and the global with its abundance dynamical characteristic, when is used in nonlinear non-constrained optimization. The optimization problem is first unconstrained by virtue of non-differentiable exact penalty function, and is further solved by PTCNN. It is showed by an example that this method is efficient.

Keywords： PTCNN     penalty function     nonlinear constrained optimization    

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Linear programming method with LR type fuzzy numbersfor network scheduling

Gao Peng,Feng Junwen

Strategic Study of CAE 2009, Volume 11, Issue 2,   Pages 70-74

Abstract:

Estimation of activity duration is a basic problem for project scheduling. However, the uncertainty of activity duration originates from both probability and fuzziness in the real world. This paper develops a linear programming method with LR type fuzzy numbers, which aims to estimate activity duration and identify critical path, and applies the λ - cut to indicate the degree of optimism of a decision maker. Finally, an example is given to demonstrate the application and validity of the proposed method.

Keywords： network scheduling     fuzzy linear programming     activity    

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Nonlinear Model-Based Process Operation under Uncertainty Using Exact Parametric Programming

Vassilis M. Charitopoulos,Lazaros G. Papageorgiou,Vivek Dua

Engineering 2017, Volume 3, Issue 2,   Pages 202-213 doi: 10.1016/J.ENG.2017.02.008

Abstract:

In the present work, two new, (multi-)parametric programming (mp-P)-inspired algorithms for the solution of mixed-integer nonlinear programming (MINLP) problems are developed, with their main focus being on process synthesis problems. The algorithms are developed for the special case in which the nonlinearities arise because of logarithmic terms, with the first one being developed for the deterministic case, and the second for the parametric case (p-MINLP). The key idea is to formulate and solve the square system of the first-order Karush-Kuhn-Tucker (KKT) conditions in an analytical way, by treating the binary variables and/or uncertain parameters as symbolic parameters. To this effect, symbolic manipulation and solution techniques are employed. In order to demonstrate the applicability and validity of the proposed algorithms, two process synthesis case studies are examined. The corresponding solutions are then validated using state-of-the-art numerical MINLP solvers. For p-MINLP, the solution is given by an optimal solution as an explicit function of the uncertain parameters.

Keywords： Parametric programming     Uncertainty     Process synthesis     Mixed-integer nonlinear programming     Symbolic manipulation    

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Connected Vehicle Based Traffic Signal Coordination Article

Wan Li, Xuegang Ban

Engineering 2020, Volume 6, Issue 12,   Pages 1463-1472 doi: 10.1016/j.eng.2020.10.009

Abstract:

This study presents a connected vehicles (CVs)-based traffic signal optimization framework for a coordinated arterial corridor. The signal optimization and coordination problem are first formulated in a centralized scheme as a mixed-integer nonlinear program (MINLP). The optimal phase durations and offsets are solved together by minimizing fuel consumption and travel time considering an individual vehicle’s trajectories. Due to the complexity of the model, we decompose the problem into two levels: an intersection level to optimize phase durations using dynamic programming (DP), and a corridor level to optimize the offsets of all intersections. In order to solve the two-level model, a prediction-based solution technique is developed. The proposed models are tested using traffic simulation under various scenarios. Compared with the traditional actuated signal timing and coordination plan, the signal timing plans generated by solving the MINLP and the two-level model can reasonably improve the signal control performance. When considering varies vehicle types under high demand levels, the proposed two-level model reduced the total system cost by 3.8% comparing to baseline actuated plan. MINLP reduced the system cost by 5.9%. It also suggested that coordination scheme was beneficial to corridors with relatively high demand levels. For intersections with major and minor street, coordination conducted for major street had little impacts on the vehicles at the minor street.

Keywords： Connected vehicles     Traffic signal coordination     Dynamic programming     Two-level optimization     Mixed-integer nonlinear program    

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Application of Genetic Algorithm in the Optimization of Parameters in Engineering Blasting

Xu Hongtao,Lu Wenbo

Strategic Study of CAE 2005, Volume 7, Issue 1,   Pages 76-80

Abstract:

The optimization of blasting parameters in engineering blasting is a complicated nonlinear programming problem. Based on the mathematical model of blasting optimization in open pit mine, the optimization problem is solved with genetic algorithm in this paper, and the feasibility and high effectiveness of optimizing blasting parameters with genetic algorithm are proved by the results. It has provided a new effective approach for solving this problem.

Keywords： engineering blasting     mining     optimization     mathematical model     nonlinear programming     genetic algorithm    

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Nonlinear programming control using differential aerodynamic drag for CubeSat formation flying Article

Sheng-chao DENG, Tao MENG, Zhong-he JIN

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 7,   Pages 867-881 doi: 10.1631/FITEE.1500493

Abstract: Because of their volume and power limitation, it is difficult for CubeSats to configure a traditional propulsion system. Atmospheric drag is one of the space environmental forces that low-orbit satellites can use to realize orbit adjustment. This paper presents an integrated control strategy to achieve the desired in-track formation through the atmospheric drag difference, which will be used on ZJUCubeSat, the next pico-satellite of Zhejiang University and one of the participants of the international QB50 project. The primary mission of the QB50 project is to explore the near-Earth thermosphere and ionosphere at the orbital height of 90–300 km. Atmospheric drag cannot be ignored and has a major impact on both attitude and orbit of the satellite at this low orbital height. We conduct aerodynamics analysis and design a multidimensional nonlinear constraint programming (MNLP) strategy to calculate different desired area–mass ratios and corresponding hold times for orbit adjustment, taking both the semimajor axis and eccentricity into account. In addition, area–mass ratio adjustment is achieved by pitch attitude maneuver without any deployable mechanism or corresponding control. Numerical simulation based on ZJUCubeSat verifies the feasibility and advantage of this design.

Keywords： QB50     ZJUCubeSat     Atmospheric drag     Formation flying    

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Hohmann transfer via constrained optimization None

Li XIE, Yi-qun ZHANG, Jun-yan XU

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 11,   Pages 1444-1458 doi: 10.1631/FITEE.1800295

Abstract:

Inspired by the geometric method proposed by Jean-Pierre MAREC, we first consider the Hohmann transfer problem between two coplanar circular orbits as a static nonlinear programming problem with an inequality constraint. By the Kuhn-Tucker theorem and a second-order sufficient condition for minima, we analytically prove the global minimum of the Hohmann transfer. Two sets of feasible solutions are found: one corresponding to the Hohmann transfer is the global minimum and the other is a local minimum. We next formulate the Hohmann transfer problem as boundary value problems, which are solved by the calculus of variations. The two sets of feasible solutions are also found by numerical examples. Via static and dynamic constrained optimizations, the solution to the Hohmann transfer problem is re-discovered, and its global minimum is analytically verified using nonlinear programming.

Keywords： Hohmann transfer     Nonlinear programming     Constrained optimization     Calculus of variations    

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第17届全国非线性振动暨第14届全国非线性动力学、运动稳定性学术会议

Conference Date: 10 May 2019

Conference Place: 中国/江苏/南京

Administered by: 中国振动工程学会非线性振动专业委员会

Optimal Scheduling of Variable-pressure Variable-flow Operation of Inverter-drive Pumps Connected in Parallel

Li Hongbin,Zhang Chenghui,Song Jun

Strategic Study of CAE 2001, Volume 3, Issue 9,   Pages 52-57

Abstract:

The paper analyzes common methods for the optimal scheduling modeling problem of variable-pressure variable-flow operation of inverter-drive pumps connected in parallel and compares the merits and defects of them. In the common method, the objective function is the shaft horsepower of the pumps and the constrained conditions are water supply target and high-efficiency area of pumps. In another method, the objective function is the square of the difference between the actual and needed flow when satisfying the lift target and the constrained conditions are high-efficiency areas of pumps. The paper proposes a new modeling method, which is compatible with the actual operation and has higher precision and satisfies the engineering requirement on lower switching frequency. Simulation results have proved its validity.

Keywords： optimal scheduling     nonlinear bounded programming     speed regulation of pumps    

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Indirect adaptive fuzzy-regulated optimal control for unknown continuous-time nonlinear systems

Haiyun Zhang, Deyuan Meng, Jin Wang, Guodong Lu,gray_sun@zju.edu.cn,tinydreams@126.com,dwjcom@zju.edu.cn,lugd@zju.edu.cn

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 2,   Pages 141-286 doi: 10.1631/FITEE.1900610

Abstract: We present a novel indirect adaptive fuzzy-regulated optimal control scheme for continuous-time nonlinear systems with unknown dynamics, mismatches, and disturbances. Initially, the Hamilton-Jacobi-Bellman (HJB) equation associated with its performance function is derived for the original nonlinear systems. Unlike existing adaptive dynamic programming (ADP) approaches, this scheme uses a special non-quadratic variable performance function as the reinforcement medium in the actor-critic architecture. An adaptive structure is correspondingly constructed to configure the weighting matrix of the performance function for the purpose of approximating and balancing the HJB equation. A concurrent self-organizing learning technique is designed to adaptively update the critic weights. Based on this particular critic, an adaptive optimal feedback controller is developed as the actor with a new form of augmented Riccati equation to optimize the fuzzy-regulated variable performance function in real time. The result is an online mechanism implemented as an , which involves continuous-time adaptation of both the optimal cost and the optimal control policy. The convergence and closed-loop stability of the proposed system are proved and guaranteed. Simulation examples and comparisons show the effectiveness and advantages of the proposed method.

Keywords： Indirect adaptive optimal control     Hamilton-Jacobi-Bellman equation     Fuzzy-regulated critic     Adaptive optimal control actor     Actor-critic structure     Unknown nonlinear systems    

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A Novel MILP Model Based on the Topology of a Network Graph for Process Planning in an Intelligent Manufacturing System Article

Qihao Liu, Xinyu Li, Liang Gao

Engineering 2021, Volume 7, Issue 6,   Pages 807-817 doi: 10.1016/j.eng.2021.04.011

Abstract:

Intelligent process planning (PP) is one of the most important components in an intelligent manufacturing system and acts as a bridge between product designing and practical manufacturing. PP is a nondeterministic polynomial-time (NP)-hard problem and, as existing mathematical models are not formulated in linear forms, they cannot be solved well to achieve exact solutions for PP problems. This paper proposes a novel mixed-integer linear programming (MILP) mathematical model by considering the network topology structure and the OR nodes that represent a type of OR logic inside the network. Precedence relationships between operations are discussed by raising three types of precedence relationship matrices. Furthermore, the proposed model can be programmed in commonly-used mathematical programming solvers, such as CPLEX, Gurobi, and so forth, to search for optimal solutions for most open problems. To verify the effectiveness and generality of the proposed model, five groups of numerical experiments are conducted on well-known benchmarks. The results show that the proposed model can solve PP problems effectively and can obtain better solutions than those obtained by the state-ofthe- art algorithms.

Keywords： Process planning     Network     Mixed-integer linear programming     CPLEX    

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Research on Nonlinear Combination Forecasting Approach Based on BP-AGA

Wang Shuo,Zhang Youfu,Jin Juliang

Strategic Study of CAE 2005, Volume 7, Issue 4,   Pages 83-87

Abstract:

A nonlinear combination forecasting model was established by using neural network and accelerating genetic algorithm (AGA) in the paper. AGA was used to optimize the network parameters as BP approach was slow with training network. Optimization results of AGA were taken as original values of BP approach, the network was trained with BP approach. Network convergence rate was increased with running BP approach and AGA alternately. Meanwhile the part least problem was improved. Examples were presented finally, as a result, the forecasting precision high in evidence.

Keywords： neural network     accelerating genetic algorithm     nonlinear combination forecasting     forecasting precision    

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A double-layered nonlinear model predictive control based control algorithm for local trajectory planning for automated trucks under uncertain road adhesion coefficient conditions Research Articles

Hong-chao Wang, Wei-wei Zhang, Xun-cheng Wu, Hao-tian Cao, Qiao-ming Gao, Su-yun Luo,17721336541@163.com,zwwsues@163.com,longxd2714@163.com,yjs_liqing@163.com,mosxsues@163.com,ly18362885604@163.com

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 7,   Pages 963-1118 doi: 10.1631/FITEE.1900185

Abstract: We present a double-layered control algorithm to plan the local trajectory for s equipped with four hub motors. The main layer of the proposed control algorithm consists of a main layer (MLN-MPC) controller and a secondary layer nonlinear MPC (SLN-MPC) controller. The MLN-MPC controller is applied to plan a dynamically feasible trajectory, and the SLN-MPC controller is designed to limit the of wheels within a stable zone to avoid the tire excessively slipping during traction. Overall, this is a closed-loop control system. Under the off-line co-simulation environments of AMESim, Simulink, dSPACE, and TruckSim, a dynamically feasible trajectory with collision avoidance operation can be generated using the proposed method, and the longitudinal wheel slip can be constrained within a stable zone so that the driving safety of the truck can be ensured under uncertain road surface conditions. In addition, the stability and robustness of the method are verified by adding a driver model to evaluate the application in the real world. Furthermore, simulation results show that there is lower computational cost compared with the conventional PID-based control method.

Keywords： Automated truck     Trajectory planning     Nonlinear model predictive control     Longitudinal slip    

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SpaceX Starship Lands on Earth, But Manned Missions to Mars Will Require More

Chris Palmer

Engineering 2021, Volume 7, Issue 10,   Pages 1345-1347 doi: 10.1016/j.eng.2021.08.005

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Title Author Date Type Operation

Survey of autonomous guidance methods for powered planetary landing

Zheng-yu Song, Cong Wang, Stephan Theil, David Seelbinder, Marco Sagliano, Xin-fu Liu, Zhi-jiang Shao,zycalt12@sina.com

Journal Article

Pulse Transiently Chaotic Neural Network for Nonlinear Constrained Optimization

Li Ying,Cao Hongzhao,Hu Yunchang,Shang Xiuming

Journal Article

Linear programming method with LR type fuzzy numbersfor network scheduling

Gao Peng,Feng Junwen

Journal Article

Nonlinear Model-Based Process Operation under Uncertainty Using Exact Parametric Programming

Vassilis M. Charitopoulos,Lazaros G. Papageorgiou,Vivek Dua

Journal Article

Connected Vehicle Based Traffic Signal Coordination

Wan Li, Xuegang Ban

Journal Article

Application of Genetic Algorithm in the Optimization of Parameters in Engineering Blasting

Xu Hongtao,Lu Wenbo

Journal Article

Nonlinear programming control using differential aerodynamic drag for CubeSat formation flying

Sheng-chao DENG, Tao MENG, Zhong-he JIN

Journal Article

Hohmann transfer via constrained optimization

Li XIE, Yi-qun ZHANG, Jun-yan XU

Journal Article

第17届全国非线性振动暨第14届全国非线性动力学、运动稳定性学术会议

10 May 2019

Conference Information

Optimal Scheduling of Variable-pressure Variable-flow Operation of Inverter-drive Pumps Connected in Parallel

Li Hongbin,Zhang Chenghui,Song Jun

Journal Article

Indirect adaptive fuzzy-regulated optimal control for unknown continuous-time nonlinear systems

Haiyun Zhang, Deyuan Meng, Jin Wang, Guodong Lu,gray_sun@zju.edu.cn,tinydreams@126.com,dwjcom@zju.edu.cn,lugd@zju.edu.cn

Journal Article

A Novel MILP Model Based on the Topology of a Network Graph for Process Planning in an Intelligent Manufacturing System

Qihao Liu, Xinyu Li, Liang Gao

Journal Article

Research on Nonlinear Combination Forecasting Approach Based on BP-AGA

Wang Shuo,Zhang Youfu,Jin Juliang

Journal Article

A double-layered nonlinear model predictive control based control algorithm for local trajectory planning for automated trucks under uncertain road adhesion coefficient conditions

Hong-chao Wang, Wei-wei Zhang, Xun-cheng Wu, Hao-tian Cao, Qiao-ming Gao, Su-yun Luo,17721336541@163.com,zwwsues@163.com,longxd2714@163.com,yjs_liqing@163.com,mosxsues@163.com,ly18362885604@163.com

Journal Article

SpaceX Starship Lands on Earth, But Manned Missions to Mars Will Require More

Chris Palmer

Journal Article