{"corpus_id":224816185,"paper_sha":"48512e4326ce6a8b92e359acefdc69961e1c5149","doi":"10.1016/J.YMSSP.2020.107235","arxiv_id":null,"pmid":null,"pmcid":null,"mag_id":3087642603,"dblp_id":null,"acl_id":null,"title":"Robust adaptive motion tracking of piezoelectric actuated stages using online neural-network-based sliding mode control","year":2021,"publication_date":"2021-03-01","venue":"Mechanical systems and signal processing","journal":{"name":"Mechanical Systems and Signal Processing","pages":null,"volume":null},"journal_issn":null,"journal_title":null,"publication_types":[],"pubmed_pub_types":null,"s2_fields_of_study":["Computer Science","Engineering"],"reference_count":43,"citation_count":77,"influential_citation_count":0,"is_open_access":false,"arxiv_categories":null,"arxiv_license":null,"arxiv_journal_ref":null,"mesh_headings":null,"chemicals":null,"comments_corrections":null,"source_flags":1,"s2_open_access_pdf_url":null,"s2_open_access_landing_url":null,"s2_open_access_license":null,"s2_open_access_status":null,"pmc_open_access_pdf_url":null,"pmc_open_access_landing_url":null,"pmc_open_access_license":null,"pmc_open_access_status":null,"unpaywall_open_access_pdf_url":null,"unpaywall_open_access_landing_url":null,"unpaywall_open_access_license":null,"unpaywall_open_access_status":null,"abstract":"Abstract Robust and precise motion tracking for micro-electro-mechanical systems in the presence of inherent nonlinearity and external disturbance is of great importance in many applications. Due to high sensitivity to environmental variations, the entire model or some parameters of the system tend to change unexpectedly. Existing offline nonlinearity models are computationally intensive and may be not suitable under system perturbations. In this work, for a class of piezoelectric actuated (PEA) system, a new online neural-network-based sliding mode control (OLNN-SMC) scheme is developed to obtain robust adaptive precision motions. The nonlinearity of the PEA system is identified online and compensated using singularity-free neural networks (NNs). To alleviate the residual NN approximation errors and meanwhile maintain robust stability under external disturbance, a feedback sliding-mode is synthesized into the control law. Considering unknown and varying disturbances, an adaptive mechanism is designed to achieve robust adaptive motion tracking. The controller is implemented and evaluated through experiments on a PEA platform. Results show that the proposed OLNN-SMC is superior to existing proportional-integral-derivative control with disturbance observer (PID+DOB) and adaptive sliding mode control (ASMC) in terms of sinusoidal tracking and disturbance rejection. In particular, the root-mean-square (RMS) errors for sinusoidal tracking at 0.1–10 Hz using the proposed OLNN-SMC are reduced by 83.5% compared with the cases using PID+DOB or ASMC.","claims":[{"public_id":"cl_b927ae688102642cf71346028a19738d","status":"active","text":"The OLNN-SMC is superior to proportional-integral-derivative control with disturbance observer (PID+DOB) and adaptive sliding mode control (ASMC) in sinusoidal tracking and disturbance rejection on a piezoelectric actuated (PEA) platform.","confidence":0.9,"contributors":[{"id":1165,"public_id":"ezd9qvkvax","public_label":"The Reverser‮ (ezd9qvkvax)","roles":["extraction"],"url":"https://sah.borca.ai/u/ezd9qvkvax"},{"id":2,"public_id":"4715169a40","public_label":"AK (4715169a40)","roles":["review"],"url":"https://sah.borca.ai/u/4715169a40"},{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["review"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/claims/cl_b927ae688102642cf71346028a19738d"},{"public_id":"cl_48faa6e30002d24cdfb2307cb77e7271","status":"active","text":"The online neural-network-based sliding mode control (OLNN-SMC) uses singularity-free neural networks for online nonlinearity identification and compensation, a feedback sliding-mode for residual errors and disturbances, and an adaptive mechanism for varying disturbances.","confidence":0.9,"contributors":[{"id":1165,"public_id":"ezd9qvkvax","public_label":"The Reverser‮ (ezd9qvkvax)","roles":["extraction"],"url":"https://sah.borca.ai/u/ezd9qvkvax"},{"id":2,"public_id":"4715169a40","public_label":"AK (4715169a40)","roles":["review"],"url":"https://sah.borca.ai/u/4715169a40"},{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["review"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/claims/cl_48faa6e30002d24cdfb2307cb77e7271"},{"public_id":"cl_0ed62ab3e9b6d74b20f427b8fdb85770","status":"active","text":"The root-mean-square (RMS) errors for sinusoidal tracking at 0.1–10 Hz using OLNN-SMC are reduced by 83.5% compared with PID+DOB or ASMC.","confidence":0.95,"contributors":[{"id":1165,"public_id":"ezd9qvkvax","public_label":"The Reverser‮ (ezd9qvkvax)","roles":["extraction"],"url":"https://sah.borca.ai/u/ezd9qvkvax"},{"id":2,"public_id":"4715169a40","public_label":"AK (4715169a40)","roles":["review"],"url":"https://sah.borca.ai/u/4715169a40"},{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["review"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/claims/cl_0ed62ab3e9b6d74b20f427b8fdb85770"}],"concepts":[{"public_id":"co_00bde323bc925d39fff77154e86eb924","status":"active","name":"root-mean-square (RMS) errors","description":"The error metric used to quantify tracking performance, specifically the root-mean-square of the tracking error over time.","types":["error metric","measurement"],"aliases":["RMS errors"],"contributors":[{"id":1165,"public_id":"ezd9qvkvax","public_label":"The Reverser‮ (ezd9qvkvax)","roles":["extraction"],"url":"https://sah.borca.ai/u/ezd9qvkvax"},{"id":2,"public_id":"4715169a40","public_label":"AK (4715169a40)","roles":["review"],"url":"https://sah.borca.ai/u/4715169a40"},{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["review"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/concepts/co_00bde323bc925d39fff77154e86eb924"},{"public_id":"co_18f415efdd44ef2823a987c127017f4f","status":"active","name":"singularity-free neural networks","description":"Neural networks used in the OLNN-SMC to identify and compensate for nonlinearities online without singularities.","types":["neural network","component"],"aliases":[],"contributors":[{"id":1165,"public_id":"ezd9qvkvax","public_label":"The Reverser‮ (ezd9qvkvax)","roles":["extraction"],"url":"https://sah.borca.ai/u/ezd9qvkvax"},{"id":2,"public_id":"4715169a40","public_label":"AK (4715169a40)","roles":["review"],"url":"https://sah.borca.ai/u/4715169a40"},{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["review"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/concepts/co_18f415efdd44ef2823a987c127017f4f"},{"public_id":"co_23be5a0d6aaee5bf1656bcc9e9ddadbf","status":"active","name":"online neural-network-based sliding mode control (OLNN-SMC)","description":"A robust adaptive control scheme that combines online singularity-free neural networks for nonlinearity identification and compensation, a feedback sliding-mode for residual errors and disturbances, and an adaptive mechanism for varying disturbances, developed for piezoelectric actuated stages.","types":["method","control scheme"],"aliases":["OLNN-SMC"],"contributors":[{"id":1165,"public_id":"ezd9qvkvax","public_label":"The Reverser‮ (ezd9qvkvax)","roles":["extraction"],"url":"https://sah.borca.ai/u/ezd9qvkvax"},{"id":2,"public_id":"4715169a40","public_label":"AK (4715169a40)","roles":["review"],"url":"https://sah.borca.ai/u/4715169a40"},{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["review"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/concepts/co_23be5a0d6aaee5bf1656bcc9e9ddadbf"},{"public_id":"co_2513415776a8a1198f473d4c3651c68a","status":"active","name":"proportional-integral-derivative control with disturbance observer (PID+DOB)","description":"A baseline control method combining PID control with a disturbance observer, used for comparison in the experiments.","types":["baseline","control method"],"aliases":["PID+DOB"],"contributors":[{"id":1165,"public_id":"ezd9qvkvax","public_label":"The Reverser‮ (ezd9qvkvax)","roles":["extraction"],"url":"https://sah.borca.ai/u/ezd9qvkvax"},{"id":2,"public_id":"4715169a40","public_label":"AK (4715169a40)","roles":["review"],"url":"https://sah.borca.ai/u/4715169a40"},{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["review"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/concepts/co_2513415776a8a1198f473d4c3651c68a"},{"public_id":"co_457518fd63ab38bf3522ae6d30a36eeb","status":"active","name":"piezoelectric actuated (PEA) system","description":"The class of micro-electro-mechanical systems actuated by piezoelectric elements, used as the test platform for the proposed control scheme.","types":["system","platform"],"aliases":["PEA system"],"contributors":[{"id":1165,"public_id":"ezd9qvkvax","public_label":"The Reverser‮ (ezd9qvkvax)","roles":["extraction"],"url":"https://sah.borca.ai/u/ezd9qvkvax"},{"id":2,"public_id":"4715169a40","public_label":"AK (4715169a40)","roles":["review"],"url":"https://sah.borca.ai/u/4715169a40"},{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["review"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/concepts/co_457518fd63ab38bf3522ae6d30a36eeb"},{"public_id":"co_51807c6a6b398379a5a7283b935af408","status":"active","name":"feedback sliding-mode","description":"A sliding-mode control component integrated into the OLNN-SMC to handle residual neural network approximation errors and external disturbances.","types":["control component","method"],"aliases":["sliding-mode"],"contributors":[{"id":1165,"public_id":"ezd9qvkvax","public_label":"The Reverser‮ (ezd9qvkvax)","roles":["extraction"],"url":"https://sah.borca.ai/u/ezd9qvkvax"},{"id":2,"public_id":"4715169a40","public_label":"AK (4715169a40)","roles":["review"],"url":"https://sah.borca.ai/u/4715169a40"},{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["review"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/concepts/co_51807c6a6b398379a5a7283b935af408"},{"public_id":"co_9c5e11cd4a3c8243cd4292b48368b63a","status":"active","name":"adaptive mechanism","description":"A mechanism designed in the OLNN-SMC to adapt to unknown and varying disturbances for robust motion tracking.","types":["mechanism","component"],"aliases":[],"contributors":[{"id":1165,"public_id":"ezd9qvkvax","public_label":"The Reverser‮ (ezd9qvkvax)","roles":["extraction"],"url":"https://sah.borca.ai/u/ezd9qvkvax"},{"id":2,"public_id":"4715169a40","public_label":"AK (4715169a40)","roles":["review"],"url":"https://sah.borca.ai/u/4715169a40"},{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["review"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/concepts/co_9c5e11cd4a3c8243cd4292b48368b63a"},{"public_id":"co_a9f18f89e7344ee89f09eb5758819a05","status":"active","name":"disturbance rejection","description":"The ability of the control system to mitigate the effects of external disturbances, evaluated in the experiments.","types":["performance metric"],"aliases":[],"contributors":[{"id":1165,"public_id":"ezd9qvkvax","public_label":"The Reverser‮ (ezd9qvkvax)","roles":["extraction"],"url":"https://sah.borca.ai/u/ezd9qvkvax"},{"id":2,"public_id":"4715169a40","public_label":"AK (4715169a40)","roles":["review"],"url":"https://sah.borca.ai/u/4715169a40"},{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["review"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/concepts/co_a9f18f89e7344ee89f09eb5758819a05"},{"public_id":"co_e10b73a6057520fd0f47c1d235ad09bb","status":"active","name":"sinusoidal tracking","description":"A performance test involving tracking sinusoidal reference trajectories at frequencies from 0.1 to 10 Hz.","types":["task","evaluation metric"],"aliases":[],"contributors":[{"id":1165,"public_id":"ezd9qvkvax","public_label":"The Reverser‮ (ezd9qvkvax)","roles":["extraction"],"url":"https://sah.borca.ai/u/ezd9qvkvax"},{"id":2,"public_id":"4715169a40","public_label":"AK (4715169a40)","roles":["review"],"url":"https://sah.borca.ai/u/4715169a40"},{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["review"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/concepts/co_e10b73a6057520fd0f47c1d235ad09bb"},{"public_id":"co_f8f1b6da3b50b1aa26ea5a2bc962ba05","status":"active","name":"adaptive sliding mode control (ASMC)","description":"A baseline adaptive sliding mode control method used for comparison in the experiments.","types":["baseline","control method"],"aliases":["ASMC"],"contributors":[{"id":1165,"public_id":"ezd9qvkvax","public_label":"The Reverser‮ (ezd9qvkvax)","roles":["extraction"],"url":"https://sah.borca.ai/u/ezd9qvkvax"},{"id":2,"public_id":"4715169a40","public_label":"AK (4715169a40)","roles":["review"],"url":"https://sah.borca.ai/u/4715169a40"},{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["review"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/concepts/co_f8f1b6da3b50b1aa26ea5a2bc962ba05"}],"external_ids":{"DOI":"10.1016/J.YMSSP.2020.107235","ArXiv":null,"PubMed":null,"PubMedCentral":null,"MAG":3087642603,"DBLP":null,"ACL":null},"open_access":{"is_open_access":false,"pdf_url":null,"landing_url":"https://sah.borca.ai/papers/224816185","source":null,"pdf_url_source":null,"license":null,"reason":"pdf_url_not_indexed"},"reference_availability":{"status":"available","references_indexed":true,"full_text_available":false,"full_text_source":null,"count_basis":"semantic_scholar_metadata","extraction_status":"not_applicable","reason":null},"source":{"provider":"episteme2","base_corpus":"semantic_scholar_dump","freshness_mode":"unknown","basis":["semantic_scholar_metadata","postgres_metadata"],"limits":["paper metadata is based on indexed upstream scholarly datasets","claims and concepts are available only for extracted papers","absence of claims or concepts means no extracted graph data is available in this response"],"status":"available","degraded":false,"degraded_reasons":[],"diagnostics":{"status":"available","degraded":false,"degraded_reasons":[],"metadata_status":"available","graph_status":"available","abstract_status":"available"},"source_flags":1},"paper_id":630958,"paper_uid":"f4c2ae32-017c-4dea-829d-a4eb044e4b71","canonical_identity":{"paper_id":630958,"paper_uid":"f4c2ae32-017c-4dea-829d-a4eb044e4b71","identity_status":"available","lookup_basis":"semantic_scholar_external_id","compatibility_path":"corpus_id"},"url":"https://sah.borca.ai/papers/224816185"}