{"title":"Finite-time optimal control for MMCPS via a novel preassigned-time performance approach","authors":"Yilin Chen , Yingnan Pan , Zhechen Zhu","doi":"10.1016/j.neunet.2024.106916","DOIUrl":null,"url":null,"abstract":"<div><div>This paper studies the finite-time optimal stabilization problem of the macro–micro composite positioning stage (MMCPS). The dynamic model of the MMCPS is established as an interconnected system according to the Newton’s second law. Different from existing MMCPS control schemes, the convergence time of errors generated by control algorithms and coupling effects in the positioning process of the MMCPS is limited to the specific range depending on the initial value of the system, which is crucial for ensuring the cooperative work of macro and micro components. Meanwhile, the reinforcement learning strategy based on actor–critic neural networks is used to optimize the controller performance while ensuring the propulsion force on voice coil motor (VCM) and vibration reduction force on piezoelectric element actuator. Furthermore, a novel preassigned-time performance function is designed to guarantee that the displacements of the VCM axis and stage can be limited to the preassigned area in the preassigned time, thereby reducing vibration amplitude. All signals of the MMCPS system are proven to be semi-global practical finite-time stable. Finally, some simulation results demonstrate the feasibility of the designed algorithm.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"182 ","pages":"Article 106916"},"PeriodicalIF":6.0000,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neural Networks","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0893608024008451","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
This paper studies the finite-time optimal stabilization problem of the macro–micro composite positioning stage (MMCPS). The dynamic model of the MMCPS is established as an interconnected system according to the Newton’s second law. Different from existing MMCPS control schemes, the convergence time of errors generated by control algorithms and coupling effects in the positioning process of the MMCPS is limited to the specific range depending on the initial value of the system, which is crucial for ensuring the cooperative work of macro and micro components. Meanwhile, the reinforcement learning strategy based on actor–critic neural networks is used to optimize the controller performance while ensuring the propulsion force on voice coil motor (VCM) and vibration reduction force on piezoelectric element actuator. Furthermore, a novel preassigned-time performance function is designed to guarantee that the displacements of the VCM axis and stage can be limited to the preassigned area in the preassigned time, thereby reducing vibration amplitude. All signals of the MMCPS system are proven to be semi-global practical finite-time stable. Finally, some simulation results demonstrate the feasibility of the designed algorithm.
期刊介绍:
Neural Networks is a platform that aims to foster an international community of scholars and practitioners interested in neural networks, deep learning, and other approaches to artificial intelligence and machine learning. Our journal invites submissions covering various aspects of neural networks research, from computational neuroscience and cognitive modeling to mathematical analyses and engineering applications. By providing a forum for interdisciplinary discussions between biology and technology, we aim to encourage the development of biologically-inspired artificial intelligence.