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Three Dimensional Multi-Objective Terrain Following/Avoidance Optimization Using Heuristic Approach

Kamyar, Reza | 2010

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 40838 (45)
  4. University: Sharif University of Technology
  5. Department: Aerospace Engineering
  6. Advisor(s): Pourtakdoust, Hossein
  7. Abstract:
  8. Thus far, there has been a great attraction toward the optimal flight path planning problem. Terrain following/avoidance (TF/TA) flight is one of the most significant applications of this issue. To prevent from detection by the radars, fighter aircrafts, cruise missiles and helicopters often have to fly as close as possible to the surface during the operations. Such types of maneuvers are much more demanding and effortful than to be designed and implemented by human. The optimal TF/TA guidance system design comprises three phases: optimal path planning, closed-loop control system design for trajectory tracking and sensor blending to match the onboard and measured terrain data. So far, the plant models incorporated by the researchers in TF/TA flight path planning have been limited to kinematic, two dimensional dynamic or three dimensional point mass equations. The adopted optimization methods have been mostly local classical algorithms, regarding the problem as a linear or nonlinear programming. Moreover, the lack of an effective and efficient control system for robust trajectory tracking is overt. In the present study, the single and multi-objective optimal TF/TA flight in the cases of height, time and fuel optimality has been tackled by PSO/SQP, DE/SQP and MOEAD/D-DE hybrid global optimization meta-heuristics. Designing the most real trajectories, a high-fidelity rigid six degrees of freedom nonlinear simulator is introduced to the optimizers. Finally, a MIMO fuzzy nonlinear model predictive controller is utilized to handle the optimal trajectory tracking in a robust manner. Stationary and moving threats, wind disturbances and plant uncertainties unfavorable effects are investigated thoroughly and the results prove the promising performance of the proposed methods
  9. Keywords:
  10. Multidisciplinary Optimization ; Hybrid Meta Heuristic Algorithm ; Fuzzy Predictive Control ; Nonlinear Controller ; Optimal Terrain Following-Avoindance ; Nonlinear Six Degree of Free High-fidelity Modeling ; Online Optimal Tracking

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