Offset-Free Lyapunov-Stable Model Predictive Control Utilizing Long Short-Term Memory Networks with Parameter Adaptation

Offset-Free Lyapunov-Stable Model Predictive Control Utilizing Long Short-Term Memory Networks with Parameter Adaptation | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 26 June 2025 V1 Latest version Share on Offset-Free Lyapunov-Stable Model Predictive Control Utilizing Long Short-Term Memory Networks with Parameter Adaptation Author : A B 0000-0001-6978-2619 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.175091047.70116023/v1 239 views 114 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Stable and offset-free responses are two desirable properties of nonlinear model predictive control (NMPC). However, traditional long short-term memory (LSTM)-based NMPC lacks inherent stability guarantees, especially in the presence of process-model mismatch. To address this limitation, this study integrates a Lyapunov stability constraint and an integrated output disturbance model into LSTM-based NMPC to enforce closed-loop stability and offset-free performance. Two simulation examples are used to demonstrate the effectiveness of the proposed method: the continuous stirred tank reactor (CSTR) and the forced evaporator process. LSTM models were trained using input-output datasets collected from the two processes and were subsequently employed as prediction models within the NMPC framework. The designed LSTM-based NMPC was then implemented on simulation models of the processes. Validation results for the CSTR process showed that the trained LSTM model achieved excellent fits of 93.72% and 95.12% for concentrations C A and temperature T, respectively, while the linear model yielded poor fits of 9.37% and 19.88%. For the forced evaporator process, the LSTM model also achieved high prediction accuracies with fits of 96.76%, 96.52%, and 85.69% for L 2, X 2, and P 2, respectively, whereas the linear model produced significantly lower fits of 51.31%, 27.69%, and 2.24%. Closed-loop simulation results demonstrated that the proposed LSTM-based NMPC exhibited stable and offset-free setpoint tracking in spite of process/model mismatches, performing comparably to NMPC based on first-principles (FP) models. However, it significantly outperformed the linear model predictive control (LMPC). Supplementary Material File (single file lyapunov stable lstm nmpc_24_06_2025.docx) Download 1.99 MB Information & Authors Information Version history V1 Version 1 26 June 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords long short term memory network (lstm) lyapunov stability nonlinear model predictive control offset-free parameter adaptation Authors Affiliations A B 0000-0001-6978-2619 [email protected] Obafemi Awolowo University View all articles by this author Metrics & Citations Metrics Article Usage 239 views 114 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation A B. Offset-Free Lyapunov-Stable Model Predictive Control Utilizing Long Short-Term Memory Networks with Parameter Adaptation. Authorea . 26 June 2025. DOI: https://doi.org/10.22541/au.175091047.70116023/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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