Genetic algorithms solution for unconstrained optimal crane control

Crane control is a difficult problem for conventional control methods because of the highly nonlinear equations that must be satisfied. Usually the necessary conditions for solving an optimal control problem require finding the initial co-state vector. In this paper real-coded genetic algorithms are used to find the desired initial value of the costates of the system with no constraints. In our genetic representation, each chromosome represents a set of co-states and each gene (co-state) has an associated cost based on its ability to move the system to desired state after a given amount of time. The objective is to evolve a minimum cost co-state. Our results for this unconstrained crane problem are quite encouraging.