Grammatical Evolution of Robust Controller Structures Using Wilson Scoring and Criticality Ranking

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• 关键词：Robust control ; Grammatical evolution ; Wilson score ; Nonlinear systems ; Genetic algorithms ; Controller synthesis ; Criticality ranking
• 刊名：Lecture Notes in Computer Science
• 出版年：2017
• 出版时间：2017
• 年：2017
• 卷：10196
• 期：1
• 页码：194-209
• 丛书名：Genetic Programming
• ISBN：978-3-319-55696-3
• 卷排序：10196

文摘

In process control it is essential that disturbances and parameter uncertainties do not affect the process in a negative way. Simultaneously optimizing an objective function for different scenarios can be solved in theory by evaluating candidate solutions on all scenarios. This is not feasible in real-world applications, where the scenario space often forms a continuum. A traditional approach is to approximate this evaluation using Monte Carlo sampling. To overcome the difficulty of choosing an appropriate sampling count and to reduce evaluations of low-quality solutions, a novel approach using Wilson scoring and criticality ranking within a grammatical evolution framework is presented. A nonlinear spring mass system is considered as benchmark example from robust control. The method is tested against Monte Carlo sampling and the results are compared to a backstepping controller. It is shown that the method is capable of outperforming state of the art methods.