Patent Document ID: 20120036096
Application ID: 12851498
Patent Flag: 0

Claim One:
1. A computerized method of generating an integrated guidance law for aerodynamic missiles, comprising the steps of: (a) establishing a missile launch guidance law f 1 (z), a missile midcourse guidance law f 2 (z) and a missile terminal guidance law f 3 (z), wherein z represents a vector containing fuzzy membership functions and guidance rules associated with each of the missile guidance laws; (b) optimizing the missile launch guidance law f 1 (z), the missile midcourse guidance law f 2 (z) and the missile terminal guidance law f 3 (z) by simultaneously minimizing the following set of equations: Minmize { f 1 ( z ) = t f f 2 ( z ) = ∫ 0 t f a M 2  t f 3 = r ( t f ) , wherein t f represents missile interception time, t represents time, r represents a distance between the missile and a target, and a M represents a missile normal acceleration, and further |r(t f )<r miss-allowed , where r miss-allowed represents a pre-selected allowable miss distance; wherein the minimization is performed by a strength Pareto evolutionary algorithm having the following steps: (c) initializing a feasible population by generating an initial population and generating an empty external Pareto-optimal set, the feasible population being selected to satisfy a set of missile guidance constraints, wherein the set of missile guidance constraints includes the pre-selected allowable miss distance; (d) searching the feasible population for non-dominated individuals and copying the non-dominated individuals into the external Pareto set; (e) searching the external Pareto set for the non-dominated individuals and removing all dominated solutions from the external Pareto set; (f) if the number of the individuals stored in the external Pareto set exceeds a pre-specified maximum size, then reducing the set by clustering; (g) assigning a strength to each individual in the external Pareto set, wherein the strength is proportional to the number of individuals covered by that individual; (h) calculating a fitness of each individual in the population as the sum of the strengths of all external Pareto solutions which dominate that individual; (i) combining the population and the individuals of the external Pareto set; (j) randomly selecting two individuals and comparing their respective fitnesses; (k) selecting the individual with the greater fitness and copying the individual with the greater fitness to a mating pool; (l) performing crossover and mutation operations to generate a new population; and (m) checking for pre-selected stopping criteria and if a pre-selected stopping criterion is satisfied, then ceasing optimization and recording the optimal population, and if a pre-selected stopping criterion is not satisfied, then replacing the previous population with the new population and returning to step d), wherein the pre-selected stopping criteria includes a miss distance that is less than the pre-selected allowable miss distance.