Abstract
In this study, singular optimal control problems are solved by line-up competition algorithm (LCA) under the framework of control parametrization. Two steps that promote the convergence quality of LCA are taken. One is to use normal (Gaussian) sampling policy to replace uniform sampling policy to accelerate initial convergence, while the other is to introduce region-relaxing strategy to enhance the refinement of solutions in final convergence. Four typical examples are given to illustrate the proposed algorithm. The results show that such modifications make LCA more robust and efficient in the solutions of singular optimal control problems.
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