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.

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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 1 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 1 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.

The aim of "Robustness Analysis" is to assess aircraft stability in the presence of all admissible uncertainties. Models that are able to describe the aircraft dynamics by taking into account all uncertainties over a region inside the flight envelope have therefore been developed, using Linear Fractional Representation (LFR). In this paper Part 1 a friendly Graphical User Interface is developed to facilitate the generation of Linear Fractional Representation uncertainty models for the

Setting the appropriate controllers for aircraft stability and control augmentation systems are complicated and time consuming tasks. As in the Linear