Robust Estimation of Propulsion parameters using Optimization Techniques based on Evolutionary Algorithms

Abstract

Robust estimation generally refers to the process of identifying the most probable set of parameters in the presence of wild points or outliers in the measurement. But besides removing the influence of outliers a robust estimator should also return the most probable set of parameters even when multiple minima in the cost function are there. When minima are almost of the same order this requirement amounts to finding out the minimum value with the largest basin of attraction. Traditional optimization algorithms are not very suitable for robust estimators because cost function obtained with various robust estimation norms may not have continuous first and second derivatives with respect to parameters. This precludes the possibility of using many of the traditional optimization algorithms. Also when multiple minima exist the convergence of traditional algorithm to a particular solution depends largely on the initial guess. In this scenario optimization tools based on evolutionary algorithms can be a very attractive option. Evolutionary algorithms require only function value evaluations and through population based approach the dependence on an initial guess to converge to a particular solution is overcome. Here a new method for the robust estimation of propulsion parameters using evolutionary optimization techniques is proposed. There are both, equality and inequality constraints, to take into account the expected range of deviations in parameters. The method incorporates GA in C, which is an optimization code based on evolutionary algorithms and is developed at KANGAL, Indian Institute of Technology, Kanpur. The method has been tested with various robust estimation norms using simulated data for a typical solid motor in the presence of multiple minima in the cost function. The proposed method has been able to estimate propulsions parameters in all the cases successfully.