Evolutionary search has been widely implemented for the adjustment of controllers’ parameters. Nevertheless, the structure of controllers, which has a more important role in control systems, has been seldom studied. To this end, an evolutionary design method of controllers is proposed to optimize both structures and parameters simultaneously in this article. A controller is made up of a combination of some basic controller components and relevant parameters. The design of controllers can be transformed into an optimization problem involving the structure (represented by discrete vectors) and parameters (represented by real numbers). A generalized structure encoding/decoding scheme is developed. Guided by the performance indicators, intelligent algorithms for both combinatorial and numerical optimization are employed to iteratively and cooperatively evolve the controller structure and parameters, respectively. In order to effectively reduce some redundant or infeasible solutions, a set of generation rules for the controller structure are put forward, which also ensures the feasibility of the structure. Furthermore, this method is applied to a magnetic levitation ball system with nonlinear dynamics and external disturbance. Both simulation and experiment results demonstrate the effectiveness and practicability of the proposed method. 

　　Publication: 

　　IEEE Transactions on Industrial Electronics (Volume: 69, Issue: 9, Sept. 2022) 

　　Author: 

　　Bin Xin 

　　School of Automation, Beijing Institute of Technology, Beijing, China 

　　Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, Beijing, China 

　　Yipeng Wang 

　　School of Automation, Beijing Institute of Technology, Beijing, China 

　　Peng Cheng Laboratory, Shenzhen, China 

　　Wenchao Xue 

　　Key Laboratory of Systems and Control, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China 

　　School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, China 

　　E-mail: wenchaoxue@amss.ac.cn  

　　Tao Cai 

　　School of Automation, Beijing Institute of Technology, Beijing, China 

　　Zhun Fan 

　　Guangdong Provincial Key Laboratory of Digital Signal and Image Processing and the Key Laboratory of Intelligent Manufacturing Technology, Ministry of Education, Shantou University, Shantou, China 

　　Jiaoyang Zhan 

　　School of Automation, Beijing Institute of Technology, Beijing, China 

　　Jie Chen 

　　School of Automation, Beijing Institute of Technology, Beijing, China 

　　Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, Beijing, China