Georges Ghazi

Optimal flight control on cessna X aircraft using differential evolution

Usually, setting the appropriate optimal gains for Stability Augmentation System and Control Augmentation System for aircrafts depends on the system knowledge by the engineer. When this setting depends on tuning gains such as Proportional Integrator Derivative control or weights as in Linear Quadratic Regulator method, the engineer will use the trial and error process, which is time consuming procedure. In this research, a study of modeling and control system design will be conducted for a business aircraft using heuristic algorithm. A linear model of Cessna Citation aircraft was designed. Then a Linear Quadratic Regulator technology was used to achieve desirable dynamic characteristics with respect to the flying qualities requirements on the stability augmentation system for the Cessna Citation X aircraft. The Proportional Integral controller was further used in the Control Augmentation System, the weighting matrix of the LQR method and the PI parameters were optimised by using the differential evolutions method. The heuristic algorithm here used has given very good results. This algorithm was used in this form for the first time to optimize linear quadratic regulation and proportional Integral controllers on an aircraft control, using one fitness function.

Flight control clearance of the Cessna Citation X using evolutionary algorithms

In this paper, an Aircraft Research Flight Simulator equipped with Flight Dynamics Level D (highest level) was used to collect flight test data and develop new controller methodologies. The changes in the aircraft’s mass and center of gravity position are affected by the fuel burn, leading to uncertainties in the aircraft dynamics. A robust controller was designed and optimized using the H∞ method and two different metaheuristic algorithms; in order to ensure acceptable flying qualities within the specified flight envelope despite the presence of uncertainties. The H∞ weighting functions were optimized by using both the genetic algorithm, and the differential evolution algorithm. The differential evolution algorithm revealed high efficiency and gave excellent results in a short time with respect to the genetic algorithm. Good dynamic characteristics for the longitudinal and lateral stability control augmentation systems with a good level of flying qualities were achieved. The optimal controller was used on the Cessna Citation X aircraft linear model for several flight conditions that covered the whole aircraft’s flight envelope. The novelty of the new objective function used in this research is that it combined both time-domain performance criteria and frequency-domain robustness criterion, which led to good level aircraft flying qualities specifications. The use of this new objective function helps to reduce considerably the calculation time of both algorithms, and avoided the use of other computationally more complicated methods. The same fitness function was used in both evolutionary algorithms (differential evolution and genetic algorithm), then their results for the validation of the linear model in the flight points were compared. Finally, robustness analysis was performed to the nonlinear model by varying mass and gravity center position. New tools were developed to validate the results obtained for both linear and nonlinear aircraft models. It was concluded that very good performance of the business Cessna Citation X aircraft was achieved in this research.

Development of a High-Fidelity Simulation Model for a Research Environment

During aircraft development, mathematical models are elaborated from our knowledge of fundamental physical laws. Those models are used to gain knowledge in order to make the best decisions at all development stages. Depending on the application, different models can be used to describe, in one way or another, the aircraft behavior. The goal of this paper is to develop a high-fidelity aircraft simulation model that is exceptionally capable, flexible and responsive to the needs of the researchers. The proposed model includes nonlinear aerodynamic coefficients, a generic engine model and a complete autopilot with auto-landing. The simulation model has been designed to help researchers develop and validate new algorithms for trajectory optimization, control design, stability analysis and parameter estimation. To make it easy to use, the simulation model also includes algorithms for stability and control analysis. Methodologies based on Nelder-Mead’s optimization algorithm with a friendly user interface have been developed, allowing the trimming and linearizing of an aircraft’s model for any flight condition and any configuration. Similarly, the simulation model includes a flight control system (FCS) and a complete autopilot (AP), allowing aircraft to follow a specific trajectory. The FCS and the AP have been designed and tuned using a modified Genetic Algorithm and the Particle Swarm Optimization algorithm. A level D flight simulator of the Cessna Citation X was used to validate the proposed methodology. The results show that the simulation model presented in this paper is accurate and could be further used to analyze the business aircraft Cessna Citation X’s behavior. The simulation model could also be adapted for its use on other aircrafts.

New methodology for a business aircraft model Hawker 800 XP stability analysis using Presagis FLsim

Aircraft simulation is one of the ways to design aircraft because it gives the opportunity to virtually flight the aircraft to see its reactions. Most of the time, the design of a simulation model is complicated because it needs aircraft geometrical and aerodynamical data such as stability coefficients and engine thrust. This article explains the development of an aircraft model only based on its geometrical and generic engine data. To validate the results, model generated data are compared with data provided by the manufacturer.