An Adaptive Pseudospectral Method for Constrained Dynamic Optimization Problems in Chemical Engineering
Long Xiao
State Key Laboratory of Industry Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, China.
Search for more papers by this authorCorresponding Author
Xinggao Liu
State Key Laboratory of Industry Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, China.
State Key Laboratory of Industry Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, China.Search for more papers by this authorShiming He
State Key Laboratory of Industry Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, China.
Search for more papers by this authorLong Xiao
State Key Laboratory of Industry Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, China.
Search for more papers by this authorCorresponding Author
Xinggao Liu
State Key Laboratory of Industry Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, China.
State Key Laboratory of Industry Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, China.Search for more papers by this authorShiming He
State Key Laboratory of Industry Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, China.
Search for more papers by this authorAbstract
An adaptive pseudospectral method with two novel strategies is proposed to solve constrained dynamic optimization problems in chemical engineering. The first strategy is to introduce slope information in designing appropriate subintervals, so that the approach becomes more efficient to track the optimal control profiles. The second strategy is to redistribute the collocation points based on the approximation error, thus ensuring the accuracy of the method. Two constrained dynamic optimization problems with multiple control variables are tested as illustrations, and the proposed approach is compared with other methods. The research results reveal the effectiveness of the proposed method in improving the solution accuracy of dynamic optimization problems.
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