This project was an investigation into the feasibility of developing a cost-effective highfidelity type-specific small aircraft simulator for use as part of an ab-initio flight training program. The development of the KatanaSim, a Diamond DA20-A1 Katana flight simulator built with commercial off-the-shelf components and original aircraft parts wherever possible, proved the feasibility of the endeavour. Particularly successful was the high level of physical fidelity achieved by the use of original aircraft parts, most notably a Katana fuselage. The development cost of the KatanaSim was significantly less than the six and seven figure costs usually associated with high-fidelity flight simulators. In order to acquire the data necessary for flight model evaluation and tuning, a minimally-intrusive flight testing methodology for small aircraft was developed. A compact instrumentation package was designed and tested, and a flight permit from Transport Canada was obtained. Two flight tests in a DA20-A1 Katana were completed and several hours worth of flight testing data was acquired. X-Plane, by Laminar Research, was chosen as the core simulator software and Plane Maker, a part of the same package, was used to develop the flight model. Aircraft model parameters were obtained from published data sources, and empirical measurements and observations. The resulting flight model required additional tuning to meet the desired performance specifications. A curve-fitting genetic algorithm was developed to automate the tuning process and was validated using a variety of dynamic models. This genetic algorithm was proven to be capable of tuning the stability derivatives of an aircraft given simulated flight performance data. In future, the genetic algorithm can be used with a blade element theory mathematical model to complete the tuning of the KatanaSim flight model using the acquired flight test data.
-A simple but reliable model tuning method was developed in order to tune a flight model for a high-fidelity type-specific small aircraft simulator. A genetic algorithm (GA) was used as a parameter estimation method. GAs are robust parallel heuristic search methods that often use least squares curve fitting methods to solve complex problems. They belong to the class of evolutionary computing algorithms that mimic natural processes, in this case evolution, to predict behaviour and solve optimization problems. A population of possible solution sets is selected at random and the known math model is then used to determine the behaviour of each of these possible solutions. The behaviour of each is then compared to the desired behaviour of the model, i.e., the reference data set, and the error is calculated. Those with the highest error are culled from the population while those with the lowest error are deemed to be "parents". These parent solution sets are then paired together, to create "children" by finding a weighted average of the parents. To ensure the solution space is fully explored, "mutations" are also created by replacing a single part of select solution sets with a randomly-generated value. Two mutation mechanisms were used in the algorithm described in this paper to ensure that the solution space was explored fully while avoiding convergence on a local, rather than global, minimum. The second generation solution set is 50% comprised of parents, 25% comprised of children, and 25% comprised of mutations. The process repeats until the convergence criteria is met. This algorithm was successfully tested with multiple dynamic systems, including simulated flight test data created using X-Plane, a flight simulator software. The algorithm proved to be a capable and adaptive parameter estimation method applicable to a wide variety of dynamic models, including flight models. IntroductionFlight model tuning is an important step in the development of a high-fidelity type-specific aircraft simulator. In an effort to develop a low-cost high-fidelity Diamond Aircraft DA20-A1 Katana flight simulator, an automated method for extracting stability derivatives from flight data was developed using a simple, but powerful, genetic algorithm (GA). As will be shown, not only is the developed algorithm capable of tuning a flight model, but it is readily adaptable to potentially tune any system for which the governing equations of motion and the desired behaviour are known. This paper provides an overview of flight model tuning, describes the operation of genetic algorithms, details the development of the flight model tuning algorithm, presents sample results, and provides relevant discussion.
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