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Erschienen in:
2012 | OriginalPaper | Buchkapitel
Iterative Learning Control of Batch Processes Using Adaptive Differential Evolution Algorithm
verfasst von : Guohua Zhang, Kai Yuan, Li Jia
Erschienen in: System Simulation and Scientific Computing
Verlag: Springer Berlin Heidelberg
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Considering the potentials of iterative learning control as framework for industrial batch process control and optimization, an iterative learning control based on adaptive differential evolution algorithm is proposed in this paper. At first, quadratic criterion-iterative learning control with adaptive differential evolution algorithm is used to improve the performance of iterative learning control. In addition, the strategy of eliminating error using iterative algorithm is employed to drive the solution to the optimal point. As a result, the proposed method can avoid the problem of falling into local extreme points when solving the objective function with multiple local extreme points, which usually exists in traditional gradient-based iterative learning control. Lastly, example is used to illustrate the performance and applicability of the proposed method.