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<article article-type="research-article" dtd-version="1.3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xml:lang="en"><front><journal-meta><journal-id journal-id-type="publisher-id">donstu</journal-id><journal-title-group><journal-title xml:lang="en">Advanced Engineering Research (Rostov-on-Don)</journal-title><trans-title-group xml:lang="ru"><trans-title>Advanced Engineering Research (Rostov-on-Don)</trans-title></trans-title-group></journal-title-group><issn pub-type="epub">2687-1653</issn><publisher><publisher-name>Don State Technical University</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.23947/2687-1653-2025-25-4-2209</article-id><article-id custom-type="edn" pub-id-type="custom">OZLBEC</article-id><article-id custom-type="elpub" pub-id-type="custom">donstu-2542</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="en"><subject>INFORMATION TECHNOLOGY, COMPUTER SCIENCE AND MANAGEMENT</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="ru"><subject>ИНФОРМАТИКА, ВЫЧИСЛИТЕЛЬНАЯ ТЕХНИКА И УПРАВЛЕНИЕ</subject></subj-group></article-categories><title-group><article-title>Observer-Based Finite-Time Adaptive Reinforced Super-Twisting Sliding Mode Control for Robotic Manipulators</article-title><trans-title-group xml:lang="ru"><trans-title>Наблюдательно-адаптивное управление скользящим режимом с конечным временем сходимости на основе усиленного супер-скручивающего алгоритма для роботизированных манипуляторов</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0003-4975-9547</contrib-id><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Лонг</surname><given-names>Хоанг Дык</given-names></name><name name-style="western" xml:lang="en"><surname>Long</surname><given-names>Hoang Duc</given-names></name></name-alternatives><bio xml:lang="ru"><p>Хоанг Дык Лонг, PhD, преподаватель кафедры «Автоматизация и вычислительная техника»</p><p>10065, Ханой, ул. Хоанг Куок Вьет, 236</p><p>Scopus Author ID: 57213158359</p></bio><bio xml:lang="en"><p>Hoang Duc Long, PhD, Lecturer of the Department of Automation and Computing Techniques,</p><p>236, Hoang Quoc Viet, Hanoi, 10065</p><p>Scopus Author ID: 57213158359</p></bio><email xlink:type="simple">longhd@lqdtu.edu.vn</email><xref ref-type="aff" rid="aff-1"/></contrib></contrib-group><aff-alternatives id="aff-1"><aff xml:lang="ru"><institution>Технический университет имени Ле Куи Дона</institution><country>Вьетнам</country></aff><aff xml:lang="en"><institution>Le Quy Don Technical University</institution><country>Viet Nam</country></aff></aff-alternatives><pub-date pub-type="collection"><year>2025</year></pub-date><pub-date pub-type="epub"><day>28</day><month>12</month><year>2025</year></pub-date><volume>25</volume><issue>4</issue><fpage>337</fpage><lpage>349</lpage><permissions><copyright-statement>Copyright &amp;#x00A9; Long H.D., 2025</copyright-statement><copyright-year>2025</copyright-year><copyright-holder xml:lang="ru">Лонг Х.Д.</copyright-holder><copyright-holder xml:lang="en">Long H.D.</copyright-holder><license xml:lang="ru" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>Данная работа распространяется под лицензией Creative Commons Attribution 4.0.</license-p></license><license xml:lang="en" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>This work is licensed under a Creative Commons Attribution 4.0 License.</license-p></license></permissions><self-uri xlink:href="https://www.vestnik-donstu.ru/jour/article/view/2542">https://www.vestnik-donstu.ru/jour/article/view/2542</self-uri><abstract><sec><title>Introduction</title><p>Introduction. Robotic manipulators operate in dynamic environments under uncertainties, external disturbances, and actuator faults, posing a critical challenge to their control design. While traditional control strategies, such as PID or computed torque control, offer simplicity, they often lack robustness to unmodeled dynamics. The development of robust and practically implementable control algorithms is becoming increasingly important with the growing use of manipulators in dangerous, precise and ultra-fast operations (industrial automation, medicine, space and service robots). Conventional PID controllers and torque calculation methods are simple but not robust enough to handle unmodeled effects. Sliding Mode Control (SMC), particularly the Super-Twisting variant (STA), provides strong robustness, but suffers from chattering and typically requires prior knowledge of system bounds. Recent advancements like Adaptive Global Integral Terminal Sliding Mode Control (AGITSMC) improve finite-time convergence but may result in overestimated control gains and residual switching effects. This research addresses a critical gap in current methods: the lack of a unified control approach that ensures finite-time convergence, suppresses chattering, and compensates for both unknown disturbances and actuator faults using observer feedback. The objective of this work is to design and analyze an Observer-Based Finite-Time Adaptive Reinforced Super-Twisting Sliding Mode Control (OFASTSMC) framework that adaptively adjusts its gains, estimates disturbances online, and guarantees smooth, robust performance even in the presence of severe nonlinearities and faults. The objective of this study is to develop and analyze an Observer-Based Finite-Time Adaptive Reinforced Super-Twisting Sliding Mode Control (OFASTSMC) framework that unifies finitetime observer feedback, adaptive gain tuning, and reinforced sliding surfaces to achieve robust trajectory tracking of robotic manipulators under disturbances and actuator faults, while effectively minimizing chattering and ensuring practical implementability.</p></sec><sec><title>Materials and Methods</title><p>Materials and Methods. This study considers the standard dynamic model of an 𝑛𝑛-DOF robotic manipulator derived using Lagrangian mechanics. The model accounts for nonlinear coupling effects, viscous friction, external disturbances, and additive actuator faults. To achieve robust finite-time control, a reinforced sliding surface is constructed using nonlinear error terms with adaptive power exponents, which accelerates error convergence. A finite-time extended state observer (ESO) is incorporated to estimate lumped disturbances and actuator fault torques in real time. Based on these estimates, the control law integrates a super-twisting sliding mode algorithm with adaptive gain tuning and boundarylayer smoothing to reduce chattering while ensuring strong robustness. The closed-loop system stability is formally analyzed within a Lyapunov framework, where rigorous proofs confirm finite-time convergence of the tracking error under the proposed controller. The proposed OFASTSMC algorithm is implemented in MATLAB/Simulink and validated on a 2-DOF planar robotic manipulator. The manipulator is subjected to time-varying disturbances and actuator degradation scenarios. For benchmarking, the method is directly compared with AGITSMC, using identical initial conditions, model parameters, and reference trajectories to ensure a fair and consistent performance evaluation.</p></sec><sec><title>Results</title><p>Results. Simulation results demonstrate that the proposed OFASTSMC method significantly outperforms the benchmark AGITSMC in terms of tracking precision, robustness, and control smoothness. Specifically, the maximum joint position errors were reduced by over 40% compared to AGITSMC, and the settling time to reach the desired trajectory was shortened by approximately 25%. Additionally, the proposed method effectively mitigated chattering in the control signal due to the use of saturation functions and gain limits, resulting in smoother actuator commands. The adaptive observer accurately estimated the lumped disturbance and fault inputs in real time, providing effective fault compensation without prior knowledge. These improvements were validated across multiple scenarios including abrupt actuator failures, nonlinear load torques, and varying trajectory speeds. The sliding surface convergence was achieved in finite time, confirming the theoretical guarantees of the method.</p></sec><sec><title>Discussion</title><p>Discussion. The results validate that OFASTSMC achieves robust, high-precision tracking for robotic manipulators operating under real-world uncertainties. Its novelty lies in the integration of adaptive exponent tuning, finite-time observer feedback, and gain-limited super-twisting control into a unified and practical framework. Unlike previous methods that rely on fixed gain structures or ignore observer feedback, OFASTSMC adapts in real-time and maintains finite-time convergence guarantees with minimal chattering.</p></sec><sec><title>Conclusion</title><p>Conclusion. The results obtained confirm that OFASTSMC is an efficient and robust solution to the trajectory tracking problem in the presence of uncertainties. The method is computationally efficient and easy to implement in digital control systems, making it suitable for practical deployment in industrial robots, service manipulators, or surgical arms. Future research will focus on extending this method to task-space control and real hardware implementation under sensor noise and model mismatches.</p></sec></abstract><trans-abstract xml:lang="ru"><sec><title>Введение</title><p>Введение. Роботизированные манипуляторы эксплуатируются в условиях изменчивой среды с неопределённостями, внешними возмущениями и возможными отказами приводов, что существенно осложняет проектирование надёжных систем управления. Важность разработки робастных и практично реализуемых алгоритмов управления возрастает с ростом применения манипуляторов в опасных, точных и сверхбыстрых операциях (промышленная автоматизация, медицина, космические и сервисные роботы). Традиционные ПИД-регуляторы и методы вычисления момента просты, но недостаточно устойчивы к немоделированным воздействиям. Управление скользящим режимом, в частности алгоритм супер-скручивания (STA), обеспечивает повышенную робастность и конечную сходимость, однако страдает эффектом дрожания и часто требует априорной информации о границах возмущений. Современные модификации (например, AGITSMC) достигают конечного времени сходимости и снижают дрожание, но могут вызывать завышение управляющих усилий и сохраняющиеся огрехи при оценке возмущений и отказов. В литературе заметен пробел: отсутствует интегрированный подход, который одновременно обеспечивает конечновременную сходимость, адаптивную компенсацию неизвестных возмущений и отказов, подавление дрожания и практическую реализуемость. Поэтому целью данной работы стало разработать и проанализировать новую структуру управления OFASTSMC (Observer-Based Finite-Time Adaptive Reinforced Super-Twisting Sliding Mode Control), объединяющую конечновременный наблюдатель, адаптивную настройку усилений и сглаженное супер-скручивающее управление. Решаемые задачи: построение конечновременного наблюдателя для оценки возмущений и отказов в режиме онлайн; разработка адаптивного механизма настройки усилений для предотвращения завышения управляющих сигналов; внедрение сглаженной STA для минимизации дрожания; проведение анализа устойчивости; выполнение численных и экспериментальных проверок на роботизированных манипуляторах.</p></sec><sec><title>Материалы и методы</title><p>Материалы и методы. Рассматривается стандартная динамическая модель роботизированного манипулятора с n степенями свободы, построенная на основе лагранжевой механики. Модель учитывает нелинейные связи, вязкое трение, внешние возмущения и аддитивные отказы приводов. Для обеспечения робастного управления с конечным временем сходимости была разработана усиленная скользящая поверхность, использующая нелинейные ошибки с адаптивными степенями — это ускоряет процесс сходимости. В схему управления включён конечновременной расширенный наблюдатель состояния (ESO), позволяющий в реальном времени оценивать суммарные возмущения и моменты отказов приводов. На основе этих оценок закон управления реализован в виде суперскручивающего алгоритма скользящего режима с адаптивной настройкой коэффициентов и использованием граничного слоя для снижения дрожания при сохранении высокой робастности. Устойчивость замкнутой системы строго проанализирована с использованием аппарата теории Ляпунова — это позволило доказать достижение конечного времени сходимости ошибок слежения под действием предложенного регулятора. Предложенный алгоритм OFASTSMC реализован в среде MATLAB/Simulink и проверен на примере плоского роботизированного манипулятора с двумя степенями свободы. Манипулятор подвергался действию переменных возмущений и сценариев деградации привода. Для объективного сравнения эффективности метод сопоставлялся с AGITSMC при идентичных начальных условиях, параметрах модели и опорных траекториях.</p></sec><sec><title>Результаты</title><p>Результаты. Численные эксперименты демонстрируют, что предложенный метод OFASTSMC значительно превосходит AGITSMC по точности слежения, устойчивости и плавности управления. В частности, максимальные ошибки по положению звеньев снижены более чем на 40 %, а время установления траектории уменьшено примерно на 25 %. Метод эффективно устраняет дрожание в управляющем сигнале за счёт функций насыщения и ограничений усиления, обеспечивая более плавное управление приводами. Адаптивный наблюдатель точно оценивает суммарные возмущения и входы отказов в реальном времени, обеспечивая компенсацию без предварительной информации. Эффективность метода подтверждена в различных сценариях: резкие отказы приводов, нелинейные нагрузки, переменные скорости траектории. Сходимость на скользящей поверхности достигается за конечное время, что подтверждает теоретические гарантии.</p></sec><sec><title>Обсуждение</title><p>Обсуждение. OFASTSMC обеспечивает высокоточную и робастную траекторию слежения в условиях неопределённостей. Основное преимущество метода — интеграция адаптивной настройки степеней, наблюдательной обратной связи и ограниченного супер-скручивающего управления в единую структуру. В отличие от подходов с фиксированными усилениями или без наблюдательной обратной связи, предложенная схема адаптируется в реальном времени, что позволяет поддерживать сходимость и существенно снижать дрожание управления. Метод сочетает адаптивность, наблюдательную коррекцию и ограниченное супер-скручивание, обеспечивая устойчивую сходимость и минимизацию дрожания.</p></sec><sec><title>Заключение</title><p>Заключение. Полученные результаты подтверждают, что OFASTSMC является эффективным и робастным решением для задачи траекторного слежения в присутствии неопределённостей. Метод демонстрирует вычислительную эффективность и простоту реализации, что делает его пригодным для практического применения. Для дальнейшего развития исследования планируется переход к реализации управления в пространстве задач и проведение экспериментов на физическом оборудовании с учётом шумов и модельных несоответствий.</p></sec></trans-abstract><kwd-group xml:lang="ru"><kwd>роботизированные манипуляторы</kwd><kwd>устойчивость за конечное время</kwd><kwd>алгоритм супер-скручивания</kwd><kwd>управление на основе скользящего режима</kwd><kwd>отказ привода</kwd><kwd>адаптивное управление</kwd><kwd>управление с наблюдателем</kwd></kwd-group><kwd-group xml:lang="en"><kwd>robotic manipulators</kwd><kwd>finite-time stability</kwd><kwd>super-twisting algorithm</kwd><kwd>sliding mode control</kwd><kwd>actuator fault</kwd><kwd>adaptive control</kwd><kwd>observer-based control</kwd></kwd-group></article-meta></front><back><ref-list><title>References</title><ref id="cit1"><label>1</label><citation-alternatives><mixed-citation xml:lang="ru">Tianli Li, Gang Zhang, Tan Zhang, Jing Pan. Adaptive Neural Network Tracking Control of Robotic Manipulators Based on Disturbance Observer. Processes. 2024;12(3):499. https://doi.org/10.3390/pr12030499</mixed-citation><mixed-citation xml:lang="en">Tianli Li, Gang Zhang, Tan Zhang, Jing Pan. Adaptive Neural Network Tracking Control of Robotic Manipulators Based on Disturbance Observer. Processes. 2024;12(3):499. https://doi.org/10.3390/pr12030499</mixed-citation></citation-alternatives></ref><ref id="cit2"><label>2</label><citation-alternatives><mixed-citation xml:lang="ru">Aohua Liu, Bo Zhang, Weiliang Chen, Yiang Luo, Shuxian Fang, Ouyang Zhang. Reinforcement Learning Based Control for Uncertain Robotic Manipulator Trajectory Tracking. In: Proc. China Automation Congress (CAC). New York City: IEEE; 2022. P. 2740–2745. https://doi.org/10.1109/CAC57257.2022.10055583</mixed-citation><mixed-citation xml:lang="en">Aohua Liu, Bo Zhang, Weiliang Chen, Yiang Luo, Shuxian Fang, Ouyang Zhang. Reinforcement Learning Based Control for Uncertain Robotic Manipulator Trajectory Tracking. In: Proc. China Automation Congress (CAC). New York City: IEEE; 2022. P. 2740–2745. https://doi.org/10.1109/CAC57257.2022.10055583</mixed-citation></citation-alternatives></ref><ref id="cit3"><label>3</label><citation-alternatives><mixed-citation xml:lang="ru">Zeeshan Anjum, Zhe Sun, Bo Chen. Disturbance-Observer-Based Fault-Tolerant Control of Robotic Manipulator: A Fixed-Time Adaptive Approach. IET Control Theory and Applications. 2024;18(11):1398–1413. https://doi.org/10.1049/cth2.12672</mixed-citation><mixed-citation xml:lang="en">Zeeshan Anjum, Zhe Sun, Bo Chen. Disturbance-Observer-Based Fault-Tolerant Control of Robotic Manipulator: A Fixed-Time Adaptive Approach. IET Control Theory and Applications. 2024;18(11):1398–1413. https://doi.org/10.1049/cth2.12672</mixed-citation></citation-alternatives></ref><ref id="cit4"><label>4</label><citation-alternatives><mixed-citation xml:lang="ru">Pertuz SA, Podlubne A, Goehringer D. An Efficient Accelerator for Nonlinear Model Predictive Control. In: Proc. IEEE 34th International Conference on Application-specific Systems, Architectures and Processors (ASAP). New York City: IEEE; 2023. P. 180–187. https://doi.org/10.1109/ASAP57973.2023.00038</mixed-citation><mixed-citation xml:lang="en">Pertuz SA, Podlubne A, Goehringer D. An Efficient Accelerator for Nonlinear Model Predictive Control. In: Proc. IEEE 34th International Conference on Application-specific Systems, Architectures and Processors (ASAP). New York City: IEEE; 2023. P. 180–187. https://doi.org/10.1109/ASAP57973.2023.00038</mixed-citation></citation-alternatives></ref><ref id="cit5"><label>5</label><citation-alternatives><mixed-citation xml:lang="ru">Cruz-Ortiz D, Chairez I, Poznyak A. Adaptive Sliding-Mode Trajectory Tracking Control for State Constraint Master–Slave Manipulator Systems. ISA Transactions. 2022;127:273–282. https://doi.org/10.1016/j.isatra.2021.08.023</mixed-citation><mixed-citation xml:lang="en">Cruz-Ortiz D, Chairez I, Poznyak A. Adaptive Sliding-Mode Trajectory Tracking Control for State Constraint Master–Slave Manipulator Systems. ISA Transactions. 2022;127:273–282. https://doi.org/10.1016/j.isatra.2021.08.023</mixed-citation></citation-alternatives></ref><ref id="cit6"><label>6</label><citation-alternatives><mixed-citation xml:lang="ru">Yung-Hsiang Chen. Nonlinear Adaptive Fuzzy Hybrid Sliding Mode Control Design for Trajectory Tracking of Autonomous Mobile Robots. Mathematics. 2025;13(8):1329. https://doi.org/10.3390/math13081329</mixed-citation><mixed-citation xml:lang="en">Yung-Hsiang Chen. Nonlinear Adaptive Fuzzy Hybrid Sliding Mode Control Design for Trajectory Tracking of Autonomous Mobile Robots. Mathematics. 2025;13(8):1329. https://doi.org/10.3390/math13081329</mixed-citation></citation-alternatives></ref><ref id="cit7"><label>7</label><citation-alternatives><mixed-citation xml:lang="ru">Marinelli M. From Industry 4.0 to Construction 5.0: Exploring the Path towards Human–Robot Collaboration in Construction. Systems. 2023;11(3):152. https://doi.org/10.3390/systems11030152</mixed-citation><mixed-citation xml:lang="en">Marinelli M. From Industry 4.0 to Construction 5.0: Exploring the Path towards Human–Robot Collaboration in Construction. Systems. 2023;11(3):152. https://doi.org/10.3390/systems11030152</mixed-citation></citation-alternatives></ref><ref id="cit8"><label>8</label><citation-alternatives><mixed-citation xml:lang="ru">Jinhua Xiao, Kaile Huang. A Comprehensive Review on Human–Robot Collaboration Remanufacturing towards Uncertain and Dynamic Disassembly. Manufacturing Review. 2024;11:17. https://doi.org/10.1051/mfreview/2024015</mixed-citation><mixed-citation xml:lang="en">Jinhua Xiao, Kaile Huang. A Comprehensive Review on Human–Robot Collaboration Remanufacturing towards Uncertain and Dynamic Disassembly. Manufacturing Review. 2024;11:17. https://doi.org/10.1051/mfreview/2024015</mixed-citation></citation-alternatives></ref><ref id="cit9"><label>9</label><citation-alternatives><mixed-citation xml:lang="ru">Dhanda M, Rogers BA, Hall S, Dekoninck E, Dhokia V. Reviewing Human-Robot Collaboration in Manufacturing: Opportunities and Challenges in the Context of Industry 5.0. Robotics and Computer-Integrated Manufacturing. 2025;93:102937. https://doi.org/10.1016/j.rcim.2024.102937</mixed-citation><mixed-citation xml:lang="en">Dhanda M, Rogers BA, Hall S, Dekoninck E, Dhokia V. Reviewing Human-Robot Collaboration in Manufacturing: Opportunities and Challenges in the Context of Industry 5.0. Robotics and Computer-Integrated Manufacturing. 2025;93:102937. https://doi.org/10.1016/j.rcim.2024.102937</mixed-citation></citation-alternatives></ref><ref id="cit10"><label>10</label><citation-alternatives><mixed-citation xml:lang="ru">Mohamed MJ, Oleiwi BK, Abood LH, Azar AT, Hameed IA. Neural Fractional Order PID Controllers Design for 2-Link Rigid Robot Manipulator. Fractal and Fractional. 2023;7(9):693. https://doi.org/10.3390/fractalfract7090693</mixed-citation><mixed-citation xml:lang="en">Mohamed MJ, Oleiwi BK, Abood LH, Azar AT, Hameed IA. Neural Fractional Order PID Controllers Design for 2-Link Rigid Robot Manipulator. Fractal and Fractional. 2023;7(9):693. https://doi.org/10.3390/fractalfract7090693</mixed-citation></citation-alternatives></ref><ref id="cit11"><label>11</label><citation-alternatives><mixed-citation xml:lang="ru">Eltayeb A, Ahmed G, Imran IH, Alyazidi NM, Abubaker A. Comparative Analysis: Fractional PID vs. PID Controllers for Robotic Arm Using Genetic Algorithm Optimization. Automation. 2024;5(3):230–245. https://doi.org/10.3390/automation5030014</mixed-citation><mixed-citation xml:lang="en">Eltayeb A, Ahmed G, Imran IH, Alyazidi NM, Abubaker A. Comparative Analysis: Fractional PID vs. PID Controllers for Robotic Arm Using Genetic Algorithm Optimization. Automation. 2024;5(3):230–245. https://doi.org/10.3390/automation5030014</mixed-citation></citation-alternatives></ref><ref id="cit12"><label>12</label><citation-alternatives><mixed-citation xml:lang="ru">Gold T, Völz A, Graichen K. Model Predictive Interaction Control for Robotic Manipulation Tasks. IEEE Transactions on Robotics. 2023;39(1):76–89. https://doi.org/10.1109/TRO.2022.3196607</mixed-citation><mixed-citation xml:lang="en">Gold T, Völz A, Graichen K. Model Predictive Interaction Control for Robotic Manipulation Tasks. IEEE Transactions on Robotics. 2023;39(1):76–89. https://doi.org/10.1109/TRO.2022.3196607</mixed-citation></citation-alternatives></ref><ref id="cit13"><label>13</label><citation-alternatives><mixed-citation xml:lang="ru">Jinxin Zhang, Hongze Wang. Online Model Predictive Control of Robot Manipulator with Structured Deep Koopman Model. IEEE Robotics and Automation Letters. 2023;8(5):3102–3109. https://doi.org/10.1109/LRA.2023.3264816</mixed-citation><mixed-citation xml:lang="en">Jinxin Zhang, Hongze Wang. Online Model Predictive Control of Robot Manipulator with Structured Deep Koopman Model. IEEE Robotics and Automation Letters. 2023;8(5):3102–3109. https://doi.org/10.1109/LRA.2023.3264816</mixed-citation></citation-alternatives></ref><ref id="cit14"><label>14</label><citation-alternatives><mixed-citation xml:lang="ru">Zu-Ren Feng, Rui-Zhi Sha, Zhi-Gang Ren. A Chattering-Reduction Sliding Mode Control Algorithm for Affine Systems with Input Matrix Uncertainty. IEEE Access. 2022;10:58982–58996. https://doi.org/10.1109/ACCESS.2022.3179580</mixed-citation><mixed-citation xml:lang="en">Zu-Ren Feng, Rui-Zhi Sha, Zhi-Gang Ren. A Chattering-Reduction Sliding Mode Control Algorithm for Affine Systems with Input Matrix Uncertainty. IEEE Access. 2022;10:58982–58996. https://doi.org/10.1109/ACCESS.2022.3179580</mixed-citation></citation-alternatives></ref><ref id="cit15"><label>15</label><citation-alternatives><mixed-citation xml:lang="ru">Zeinali M. Adaptive Chattering-Free Sliding Mode Control Design Using Fuzzy Model of the System and estimated uncertainties and its application to robot manipulators. In: Proc. International Workshop on Recent Advances in Sliding Modes (RASM). New York City: IEEE; 2015. P. 1–6. https://doi.org/10.1109/RASM.2015.7154652</mixed-citation><mixed-citation xml:lang="en">Zeinali M. Adaptive Chattering-Free Sliding Mode Control Design Using Fuzzy Model of the System and estimated uncertainties and its application to robot manipulators. In: Proc. International Workshop on Recent Advances in Sliding Modes (RASM). New York City: IEEE; 2015. P. 1–6. https://doi.org/10.1109/RASM.2015.7154652</mixed-citation></citation-alternatives></ref><ref id="cit16"><label>16</label><citation-alternatives><mixed-citation xml:lang="ru">Boiko I. Chattering in Mechanical Systems under Sliding-Mode Control. In book: Oliveira TR, Fridman L, Hsu L (eds). Sliding-Mode Control and Variable-Structure Systems. Studies in Systems, Decision and Control. Cham: Springer; 2023. P. 337–356. https://doi.org/10.1007/978-3-031-37089-2_13</mixed-citation><mixed-citation xml:lang="en">Boiko I. Chattering in Mechanical Systems under Sliding-Mode Control. In book: Oliveira TR, Fridman L, Hsu L (eds). Sliding-Mode Control and Variable-Structure Systems. Studies in Systems, Decision and Control. Cham: Springer; 2023. P. 337–356. https://doi.org/10.1007/978-3-031-37089-2_13</mixed-citation></citation-alternatives></ref><ref id="cit17"><label>17</label><citation-alternatives><mixed-citation xml:lang="ru">Minghao Liu, Qirong Tang, Yinghao Li, Changhui Liu, Min Yu. A Chattering-Suppression Sliding Mode Controller for an Underwater Manipulator Using Time Delay Estimation. Journal of Marine Science and Engineering. 2023;11(9):1742. https://doi.org/10.3390/jmse11091742</mixed-citation><mixed-citation xml:lang="en">Minghao Liu, Qirong Tang, Yinghao Li, Changhui Liu, Min Yu. A Chattering-Suppression Sliding Mode Controller for an Underwater Manipulator Using Time Delay Estimation. Journal of Marine Science and Engineering. 2023;11(9):1742. https://doi.org/10.3390/jmse11091742</mixed-citation></citation-alternatives></ref><ref id="cit18"><label>18</label><citation-alternatives><mixed-citation xml:lang="ru">Rehman FU, Mufti MR, Din SU, Afzal H, Qureshi MI, Khan M. Adaptive Smooth Super-Twisting Sliding Mode Control of Nonlinear Systems with Unmatched Uncertainty. IEEE Access. 2020;8:177932–177940. https://doi.org/10.1109/ACCESS.2020.3027194</mixed-citation><mixed-citation xml:lang="en">Rehman FU, Mufti MR, Din SU, Afzal H, Qureshi MI, Khan M. Adaptive Smooth Super-Twisting Sliding Mode Control of Nonlinear Systems with Unmatched Uncertainty. IEEE Access. 2020;8:177932–177940. https://doi.org/10.1109/ACCESS.2020.3027194</mixed-citation></citation-alternatives></ref><ref id="cit19"><label>19</label><citation-alternatives><mixed-citation xml:lang="ru">Mondal S, Mahanta Ch. Adaptive Integral Higher Order Sliding Mode Controller for Uncertain Systems. Journal of Control Theory and Applications. 2013;11:61–68. https://doi.org/10.1007/s11768-013-1180-5</mixed-citation><mixed-citation xml:lang="en">Mondal S, Mahanta Ch. Adaptive Integral Higher Order Sliding Mode Controller for Uncertain Systems. Journal of Control Theory and Applications. 2013;11:61–68. https://doi.org/10.1007/s11768-013-1180-5</mixed-citation></citation-alternatives></ref><ref id="cit20"><label>20</label><citation-alternatives><mixed-citation xml:lang="ru">Mirzaei MJ, Hamida MA, Plestan F, Taleb M. Super-Twisting Sliding Mode Controller with Self-Tuning Adaptive Gains. European Journal of Control. 2022;68:100690. https://doi.org/10.1016/j.ejcon.2022.100690</mixed-citation><mixed-citation xml:lang="en">Mirzaei MJ, Hamida MA, Plestan F, Taleb M. Super-Twisting Sliding Mode Controller with Self-Tuning Adaptive Gains. European Journal of Control. 2022;68:100690. https://doi.org/10.1016/j.ejcon.2022.100690</mixed-citation></citation-alternatives></ref><ref id="cit21"><label>21</label><citation-alternatives><mixed-citation xml:lang="ru">Shtessel YuB, Moreno JA, Plestan F, Fridman LM, Poznyak AS. Super-Twisting Adaptive Sliding Mode Control: A Lyapunov Design. In: Proc. 49th IEEE Conference on Decision and Control (CDC). New York City: IEEE; 2010. P. 5109–5113. https://doi.org/10.1109/CDC.2010.5717908</mixed-citation><mixed-citation xml:lang="en">Shtessel YuB, Moreno JA, Plestan F, Fridman LM, Poznyak AS. Super-Twisting Adaptive Sliding Mode Control: A Lyapunov Design. In: Proc. 49th IEEE Conference on Decision and Control (CDC). New York City: IEEE; 2010. P. 5109–5113. https://doi.org/10.1109/CDC.2010.5717908</mixed-citation></citation-alternatives></ref><ref id="cit22"><label>22</label><citation-alternatives><mixed-citation xml:lang="ru">Jiabin Hu, Xue Zhang, Dan Zhang, Yun Chen, Hongjie Ni, Huageng Liang. Finite-Time Adaptive Super-Twisting Sliding Mode Control for Autonomous Rrobotic Manipulators with Actuator Faults. ISA Transactions. 2024;144:342–351. https://doi.org/10.1016/j.isatra.2023.10.028</mixed-citation><mixed-citation xml:lang="en">Jiabin Hu, Xue Zhang, Dan Zhang, Yun Chen, Hongjie Ni, Huageng Liang. Finite-Time Adaptive Super-Twisting Sliding Mode Control for Autonomous Rrobotic Manipulators with Actuator Faults. ISA Transactions. 2024;144:342–351. https://doi.org/10.1016/j.isatra.2023.10.028</mixed-citation></citation-alternatives></ref><ref id="cit23"><label>23</label><citation-alternatives><mixed-citation xml:lang="ru">Xinyue Hu, Ban Wang, Yanyan Shen, Yifang Fu, Ni Li. Disturbance Observer-Enhanced Adaptive Fault-Tolerant Control of a Quadrotor UAV against Actuator Faults and Disturbances. Drones. 2023;7(8)541. https://doi.org/10.3390/drones7080541</mixed-citation><mixed-citation xml:lang="en">Xinyue Hu, Ban Wang, Yanyan Shen, Yifang Fu, Ni Li. Disturbance Observer-Enhanced Adaptive Fault-Tolerant Control of a Quadrotor UAV against Actuator Faults and Disturbances. Drones. 2023;7(8)541. https://doi.org/10.3390/drones7080541</mixed-citation></citation-alternatives></ref><ref id="cit24"><label>24</label><citation-alternatives><mixed-citation xml:lang="ru">Jiqing Chen, Qingsong Tang, Chaoyang Zhao, Haiyan Zhang. Adaptive Sliding Mode Control for Robotic Manipulators with Backlash. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science. 2023;237(24):5842–5852. https://doi.org/10.1177/09544062231167555</mixed-citation><mixed-citation xml:lang="en">Jiqing Chen, Qingsong Tang, Chaoyang Zhao, Haiyan Zhang. Adaptive Sliding Mode Control for Robotic Manipulators with Backlash. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science. 2023;237(24):5842–5852. https://doi.org/10.1177/09544062231167555</mixed-citation></citation-alternatives></ref><ref id="cit25"><label>25</label><citation-alternatives><mixed-citation xml:lang="ru">Jiqian Xu, Lijin Fang, Huaizhen Wang, Qiankun Zhao, Yingcai Wan, Yue Gao. Observer-Based Finite-Time Prescribed Performance Sliding Mode Control of Dual-Motor Joints-Driven Robotic Manipulators with Uncertainties and Disturbances. Actuators. 2024;13(9):325. https://doi.org/10.3390/act13090325</mixed-citation><mixed-citation xml:lang="en">Jiqian Xu, Lijin Fang, Huaizhen Wang, Qiankun Zhao, Yingcai Wan, Yue Gao. Observer-Based Finite-Time Prescribed Performance Sliding Mode Control of Dual-Motor Joints-Driven Robotic Manipulators with Uncertainties and Disturbances. Actuators. 2024;13(9):325. https://doi.org/10.3390/act13090325</mixed-citation></citation-alternatives></ref><ref id="cit26"><label>26</label><citation-alternatives><mixed-citation xml:lang="ru">Shanchao Yi, Junyong Zhai. Adaptive Second-Order Fast Nonsingular Terminal Sliding Mode Control for Robotic Manipulators. ISA Transactions. 2019;90:41–51. https://doi.org/10.1016/j.isatra.2018.12.046</mixed-citation><mixed-citation xml:lang="en">Shanchao Yi, Junyong Zhai. Adaptive Second-Order Fast Nonsingular Terminal Sliding Mode Control for Robotic Manipulators. ISA Transactions. 2019;90:41–51. https://doi.org/10.1016/j.isatra.2018.12.046</mixed-citation></citation-alternatives></ref><ref id="cit27"><label>27</label><citation-alternatives><mixed-citation xml:lang="ru">Tinoco V, Silva MF, Santos FN, Morais R, Magalhães SA, Moura P. A Review of Advanced Controller Methodologies for Robotic Manipulators. International Journal of Dynamics and Control. 2025;13:36. https://doi.org/10.1007/s40435-024-01533-1</mixed-citation><mixed-citation xml:lang="en">Tinoco V, Silva MF, Santos FN, Morais R, Magalhães SA, Moura P. A Review of Advanced Controller Methodologies for Robotic Manipulators. International Journal of Dynamics and Control. 2025;13:36. https://doi.org/10.1007/s40435-024-01533-1</mixed-citation></citation-alternatives></ref><ref id="cit28"><label>28</label><citation-alternatives><mixed-citation xml:lang="ru">Romero S, Valero J, Garcia AV, Rodriguez CF, Montes AM, Marin C, et al. Trajectory Planning for Robotic Manipulators in Automated Palletizing: A Comprehensive Review. Robotics. 2025;14(5):55. https://doi.org/10.3390/robotics14050055</mixed-citation><mixed-citation xml:lang="en">Romero S, Valero J, Garcia AV, Rodriguez CF, Montes AM, Marin C, et al. Trajectory Planning for Robotic Manipulators in Automated Palletizing: A Comprehensive Review. Robotics. 2025;14(5):55. https://doi.org/10.3390/robotics14050055</mixed-citation></citation-alternatives></ref><ref id="cit29"><label>29</label><citation-alternatives><mixed-citation xml:lang="ru">Kharrat M, Alhazmi H. Fixed-Time Adaptive Control for Nonstrict-Feedback Nonlinear Systems with Input Delay and Unknown Backlash-Like Hysteresis. Neural Processing Letters. 2025;57:52. https://doi.org/10.1007/s11063-025-11749-7</mixed-citation><mixed-citation xml:lang="en">Kharrat M, Alhazmi H. Fixed-Time Adaptive Control for Nonstrict-Feedback Nonlinear Systems with Input Delay and Unknown Backlash-Like Hysteresis. Neural Processing Letters. 2025;57:52. https://doi.org/10.1007/s11063-025-11749-7</mixed-citation></citation-alternatives></ref><ref id="cit30"><label>30</label><citation-alternatives><mixed-citation xml:lang="ru">Huanqing Wang, Zhu Meng. Fixed-Time Adaptive Neural Tracking Control for High-Order Nonlinear Switched Systems with Input Saturation and Dead-Zone. Applied Mathematics and Computation. 2024;480:128904. https://doi.org/10.1016/j.amc.2024.128904</mixed-citation><mixed-citation xml:lang="en">Huanqing Wang, Zhu Meng. Fixed-Time Adaptive Neural Tracking Control for High-Order Nonlinear Switched Systems with Input Saturation and Dead-Zone. Applied Mathematics and Computation. 2024;480:128904. https://doi.org/10.1016/j.amc.2024.128904</mixed-citation></citation-alternatives></ref><ref id="cit31"><label>31</label><citation-alternatives><mixed-citation xml:lang="ru">Bhat SP, Bernstein DS. Finite-Time Stability of Continuous Autonomous Systems. SIAM Journal of Control and Optimization. 2000;38(3):751–766. https://doi.org/10.1137/S0363012997321358</mixed-citation><mixed-citation xml:lang="en">Bhat SP, Bernstein DS. Finite-Time Stability of Continuous Autonomous Systems. SIAM Journal of Control and Optimization. 2000;38(3):751–766. https://doi.org/10.1137/S0363012997321358</mixed-citation></citation-alternatives></ref><ref id="cit32"><label>32</label><citation-alternatives><mixed-citation xml:lang="ru">Amato F, Ambrosino M, Ariola M, Consentino C, De Tommasi G. Finite-Time Stability and Control. New York, NY: Springer; 2013. 146 p. https://doi.org/10.1007/978-1-4471-5664-2</mixed-citation><mixed-citation xml:lang="en">Amato F, Ambrosino M, Ariola M, Consentino C, De Tommasi G. Finite-Time Stability and Control. New York, NY: Springer; 2013. 146 p. https://doi.org/10.1007/978-1-4471-5664-2</mixed-citation></citation-alternatives></ref><ref id="cit33"><label>33</label><citation-alternatives><mixed-citation xml:lang="ru">Honglei Xu. Finite-Time Stability Analysis: A Tutorial Survey. Complexity. 2020;9:1–12. https://doi.org/10.1155/2020/1941636</mixed-citation><mixed-citation xml:lang="en">Honglei Xu. Finite-Time Stability Analysis: A Tutorial Survey. Complexity. 2020;9:1–12. https://doi.org/10.1155/2020/1941636</mixed-citation></citation-alternatives></ref><ref id="cit34"><label>34</label><citation-alternatives><mixed-citation xml:lang="ru">Khalid K, Zaidi AA, Ayaz Y. Optimal Placement and Kinematic Design of 2-DoF Robotic Arm. In: Proc. International Bhurban Conference on Applied Sciences and Technologies (IBCAST). New York City: IEEE; 2021. P. 552–559. https://doi.org/10.1109/IBCAST51254.2021.9393255</mixed-citation><mixed-citation xml:lang="en">Khalid K, Zaidi AA, Ayaz Y. Optimal Placement and Kinematic Design of 2-DoF Robotic Arm. In: Proc. International Bhurban Conference on Applied Sciences and Technologies (IBCAST). New York City: IEEE; 2021. P. 552–559. https://doi.org/10.1109/IBCAST51254.2021.9393255</mixed-citation></citation-alternatives></ref><ref id="cit35"><label>35</label><citation-alternatives><mixed-citation xml:lang="ru">Hameed WN, Khawwaf JO. Robust Sliding Mode Control for 2-Dof Robot Manipulator Position Control System. In: Proc. 2nd International Conference on Emerging Trends and Applications in Artificial Intelligence. 2024;2024(34):282–288. https://doi.org/10.1049/icp.2025.0096</mixed-citation><mixed-citation xml:lang="en">Hameed WN, Khawwaf JO. Robust Sliding Mode Control for 2-Dof Robot Manipulator Position Control System. In: Proc. 2nd International Conference on Emerging Trends and Applications in Artificial Intelligence. 2024;2024(34):282–288. https://doi.org/10.1049/icp.2025.0096</mixed-citation></citation-alternatives></ref><ref id="cit36"><label>36</label><citation-alternatives><mixed-citation xml:lang="ru">Bouzid R, Gritli H, Narayan J. Optimized Inverse Kinematics of a 2-DoF Robotic Manipulator Using a Hybrid Approach Combining an ANN with a Metaheuristic Algorithm. In: Proc. IEEE International Conference on Artificial Intelligence &amp; Green Energy (ICAIGE). New York City: IEEE; 2024. P. 1–6. https://doi.org/10.1109/ICAIGE62696.2024.10776675.</mixed-citation><mixed-citation xml:lang="en">Bouzid R, Gritli H, Narayan J. Optimized Inverse Kinematics of a 2-DoF Robotic Manipulator Using a Hybrid Approach Combining an ANN with a Metaheuristic Algorithm. In: Proc. IEEE International Conference on Artificial Intelligence &amp; Green Energy (ICAIGE). New York City: IEEE; 2024. P. 1–6. https://doi.org/10.1109/ICAIGE62696.2024.10776675.</mixed-citation></citation-alternatives></ref></ref-list><fn-group><fn fn-type="conflict"><p>The authors declare that there are no conflicts of interest present.</p></fn></fn-group></back></article>
