Automated model tuning using a genetic algorithm

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