Subspace Data-Driven Control for Linear Parameter Varying Systems

Jianhong Wang, Yunfeng Zhang, Ricardo A. Ramirez-Mendoza, Ahmad Taher Azar, Ibraheem Kasim Ibraheem, Nashwa Ahmad Kamal and Farah Ayad Abdulmajeed
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Jianhong Wang: School of Engineering and Sciences, Tecnologico de Monterrey, Mexico
Yunfeng Zhang: College of Electrical Engineering and Automation, Jiangxi University of Science and Technology, China
Ricardo A. Ramirez-Mendoza: School of Engineering and Sciences, Tecnologico de Monterrey, Mexico
Ahmad Taher Azar: College of Computer and Information Sciences, Prince Sultan University, Saudi Arabia & Faculty of Computers and Artificial Intelligence, Benha University, Benha 13518, Egypt
Ibraheem Kasim Ibraheem: Computer Engineering Techniques Department, Al-Mustaqbal University College, Iraq
Nashwa Ahmad Kamal: Faculty of Engineering, Cairo University, Egypt
Farah Ayad Abdulmajeed: College of Technical Engineering, Alfarahidi University, Iraq

International Journal of Service Science, Management, Engineering, and Technology (IJSSMET), 2023, vol. 14, issue 1, 1-25

Abstract: In this research, a unique subspace data driven control for linear parameter changing system with scheduling parameters is presented. This control paves the way for investigating the nonlinear system based on the results regarding the linear system that are already known. Only the data matrix is utilized to represent the output prediction value in the future various time instants, while the input-output observation data matrix is used to identify Markov parameters in the form of state space forms. The cost function in data-driven control is then adjusted using the output prediction value. The optimal control input value of this quadratic cost function is solved using a parallel distribution technique, and the algorithm's iterative convergence is thoroughly examined. Finally, the DC motor, whose mass distribution factor is considered to be one linear parameter varying system, is controlled using the suggested subspace data driven control approach.

Date: 2023
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Citations: View citations in EconPapers (1)

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