Mesh reconstruction method of direct discrete solution solving of optimal control problem

Abstract

The invention discloses a mesh reconstruction method of the direct discrete solution solving of an optimal control problem, and aims at solving the problems that the optimizing computation consumes alarge amount of time due to excessively large time amount given by an existing mesh reconstruction method or too many iteration times, or an optimizing result is not an ideal result because the discrete accuracy cannot be ensured, and an existing method normally fails to accurately find the structural switching point of a system. The method has the advantages that the direct discrete solution solving variable scale of the complicated optimal control problem can be reduced, the computational cost is lower, the iteration time is lesser, and a high quality solution is obtained through lesser parameters. The method is suitable for the on-line optimization of a large-scale complicated dynamic optimization problem. The method is rapid and effective, the scale of a discrete nonlinear programmingproblem can be reduced to the maximum extent to reduce the iteration time, and the structural switching point of the system can be rapidly and accurately positioned.

Description

technical field [0001] The invention belongs to the technical field of dynamic optimization control, and relates to a grid reconfiguration method for directly discretely solving an optimal control problem. Background technique [0002] There are many complex optimal control problems in the process of chemical reaction process, optimization design, parameter estimation of dynamic process system, switching of working point in production process and optimal control of process system. Such problems typically contain differential and algebraic equations, as well as numerous trajectory equality and inequality constraints. For complex optimal control problems, the indirect solution method is traditionally used, and the general steps of the solution are: (1) first expand the original system into a Hamilton system; (2) derive the necessary conditions for first-order optimality; (3) obtain the mathematical The two-point boundary value problem, and then use the corresponding method to...

AI Technical Summary

Problems solved by technology

Although the iterative dynamic programming method is an optimization method with global convergence and does not need to calculate information such as gradients, it also has its obvious shortcomings: a satisfactory optimal solution will be obtained only after the discrete time period is very thin resulting in a substantial increase in computing costs

Sparse ground division does not approach the optimal control trajectory; dense ground division ensures that the discrete precision meets the requirements, but greatly increases the dimensionality and computational cost of nonlinear programming problems

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