Multistage Trajectory Optimization with Radar Range Constraints

Norsell, Martin

doi:10.2514/1.8544

Cited by 15 publications

(22 citation statements)

References 18 publications

(39 reference statements)

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“…In the following subsections, the constraints for each flight mode in the descent and approach phase are specified, and the corresponding continuous dynamics of the aircraft with wind disturbance are derived from the general equations of motion [Eqs. (4)(5)(6)(7)(8)] [17,18]. …”

Section: Continuous State Dynamics Of Aircraftmentioning

confidence: 99%

Hybrid System Modeling and Estimation for Arrival Time Prediction in Terminal Airspace

Lee

Hwang

2016

Journal of Guidance, Control, and Dynamics

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Section: Continuous State Dynamics Of Aircraftmentioning

confidence: 99%

Hybrid System Modeling and Estimation for Arrival Time Prediction in Terminal Airspace

Lee

Hwang

2016

Journal of Guidance, Control, and Dynamics

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“…To this date, the analysis of the trajectory in MDO has been used in only a few case studies in order to either identify the mission performance (Yan et al, 2012) or the radar constraints (Norsell, 2005), but the main limitation is that it has been restricted to two-dimensions and low fidelity mathematical models. The common approach that is also followed in this work is to discretize the trajectory into several segments, and then perform an evaluation of the design at each one of them.…”

Section: Advanced Analysis Functionsmentioning

confidence: 99%

Optimization of Unmanned Aerial Vehicles: Expanding the Multidisciplinary Capabilities

Παπαγεωργίου¹

2018

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Over the last decade, Unmanned Aerial Vehicles (UAVs) have experienced an accelerated growth, and nowadays they are being deployed in a variety of missions that have traditionally been covered by manned aircraft. This unprecedented market expansion has created new and unforeseen challenges for the manufacturing industry which is now called to further reduce the idea-to-market times while simultaneously delivering designs of even higher performance. In this environment of uncertainty and risk, it is without a doubt crucial for the involved actors to find ways to secure their strategic advantage, and hence, implementing the latest design tools has become a critical consideration in every Product Development Process (PDP).To this end, a method that has been frequently applied in the PDP and has shown many successful results in the development of complex engineering products is Multidisciplinary Design Optimization (MDO). In general, MDO can bring additional knowledge regarding the best-suited designs much earlier in the process, and in this respect, it can lead to significant cost and time savings by reducing the total number of refinement iterations. Nevertheless, the organizational and cultural integration of MDO has been often overlooked, while at the same time, several technical aspects of the method for UAV design are still at an elementary level. On the whole, research on MDO is showing a slow progress, and to this date, there are many limitations in both the disciplinary models and the available analysis capabilities.In light of the above, this thesis focuses on the particulars of the MDO methodology, and more specifically, on how it can be best adapted and evolved in order to enhance the development process of UAVs. The primary objective is to study the current trends and gaps of the MDO practices in UAV applications, and subsequently to build upon that and explore how these can be included in a roadmap that will be able to serve a guide for newcomers in the field. Compared to other studies, the problem is herein approached from both a technical as well as organizational perspective, and thus, this research not only aims to propose techniques that can lead to better designs but also solutions that will be meaningful to the PDP. Having established the above foundation, this work shows that the traditional MDO frameworks for UAV design have been neglecting several important features, and it elaborates on how those novel elements can be modeled in order to enable a better integration of MDO into the organizational functions. Overall, this thesis presents quantitative and qualitative data which illustrate the effectiveness of the new framework enhancements in the development process of UAVs, and concludes with discussions on the possible improvement directions towards achieving more and better MDO capabilities. vi vii

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“…With the advancement of computing power, the use of direct collocation with nonlinear programming (DCNLP) to convert TPBVPs into nonlinear programming problems (NLPPs), a so-called direct optimal control method is feasible and has been applied widely [6][7][8][9][10][11]. By discretizing the trajectory into multiple segments, characterized by state and control variables as parameters, a TPBVP is transformed into a problem of determining the parameters that satisfy the constraints.…”

Section: Introductionmentioning

confidence: 99%

Three-Dimensional Trajectory Optimization in Constrained Airspace

Dai

Cochran

2009

Journal of Aircraft

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The operational airspace of aerospace vehicles, including airplanes and unmanned aerial vehicles, is often restricted so that constraints on three-dimensional climbs, descents, and other maneuvers are necessary. In this paper, the problem of determining constrained, three-dimensional, minimum time-to-climb, and minimum fuel-toclimb trajectories for an aircraft in an airspace defined by a rectangular prism of arbitrary height is considered. The optimal control problem is transformed to a parameter optimization problem. Because a helical geometry appears to be a natural choice for climbing and descending trajectories subject to horizontal constraints, helical curves are chosen as starting trajectories. A procedure for solving the minimum time-to-climb and minimum fuel-to-climb problems by using the direct collocation and nonlinear programming methods including Chebyshev pseudospectral and Gauss pseudospectral discretization is discussed. Results obtained when different constraints are placed on airspace and state variables are presented to show their effect on the performance index. The question of "optimality" of the numerical results is also considered.

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Multistage Trajectory Optimization with Radar Range Constraints

Cited by 15 publications

References 18 publications

Hybrid System Modeling and Estimation for Arrival Time Prediction in Terminal Airspace

Hybrid System Modeling and Estimation for Arrival Time Prediction in Terminal Airspace

Optimization of Unmanned Aerial Vehicles: Expanding the Multidisciplinary Capabilities

Three-Dimensional Trajectory Optimization in Constrained Airspace

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