Direct data-driven design of LPV controllers with soft performance specifications |
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| Affiliation: | 1. Department of Mechanical Engineering, Building 15, Gemini-Zuid, Eindhoven University of Technology, Eindhoven, The Netherlands;2. Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Via G. Ponzio 34/5 - 20133 Milano, Italy;1. School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, China;2. Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, China;3. Key Laboratory of Vehicle Advanced Manufacturing, Measuring and Control Technology (Beijing Jiaotong University), Ministry of Education, China;1. Department of Electrical and Computer Engineering, University of Alberta, Edmonton T6G 2W3, Canada;2. Department of Electrical Engineering, Sahand University of Technology, Tabriz, Iran;1. Institute for Problems of Mechanical Engineering of the Russian Academy of Sciences, 61 Bolshoy Prospekt V.O., St. Petersburg 199178, Russia;2. Gubkin University, 65 Leninsky Prospekt, Moscow 119991, Russia;1. College of Information Engineering, Nanjing University of Finance and Economics, Nanjing 210023, China;2. CAS Key Laboratory of Planetary Sciences, Purple Mountain Observatory, Chinese Academy of Sciences, Nanjing 210023, China;3. NARI Group Corporation (State Grid Electric Power Research Institute), Nanjing 211106, China;1. AnHui Province Key Laboratory of Special Heavy Load Robot and School of Electrical and Information Engineering, Anhui University of Technology, Ma’anshan 243002, PR China;2. School of Automation and Electrical Engineering, Linyi University, Linyi 276005, PR China;3. School of Information Science and Engineering, Chengdu University, Chengdu 610106, PR China |
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| Abstract: | If only experimental measurements are available, direct data-driven control design becomes an appealing approach, as control performance is directly optimized based on the collected samples. The direct synthesis of a feedback controller from input-output data typically requires the blind choice of a reference model, that dictates the desired closed-loop behavior. In this paper, we propose a data-driven design scheme for linear parameter-varying (LPV) systems to account for soft performance specifications. Within this framework, the reference model is treated as an additional hyper-parameter to be learned from data, while the user is asked to provide only indicative performance constraints. The effectiveness of the proposed approach is demonstrated on a benchmark simulation case study, showing the improvement achieved by allowing for a flexible reference model. |
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