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Volume 6, Issue 2, 1 April 2022, Pages 113-124
Abstract. In this paper, we study the dynamic optimization of the acceleration process in the engine transition state. It is difficult for general linear and nonlinear models to portray the complex mechanical characteristics of an aero-engine. We consider a non-smooth linear parameter variation (LPV) system with nonlinear terms as a mathematical model for this acceleration process. First, a control parameterization method is used to transform the problem into a parameter selection problem. Then, a smoothing technique is used to deal with the non-smooth state constraints. Finally, in order to obtain the global optimal solution of this dynamic optimization problem, an optimization algorithm based on a combination of a gradient descent method and a modified particle swarm optimization is designed to solve the equivalent nonlinear programming problem. The effectiveness and superiority of the proposed algorithm is computationally verified by using the LPV model identified from the actual data.
How to Cite this Article:
J. Teng, K. Guo, L. Wang, Dynamic optimization with a non-smooth LPV system in aero-engine transition state acceleration process, J. Nonlinear Var. Anal. 6 (2022), 113-124.