Modeling of Asynchronous Mode-Dependent Delays in Stochastic Markovian Jumping Modes Based on Static Neural Networks for Robotic Manipulators

作者：Summera Shamrooz, Muhammad Shamrooz Aslam, Houguang Liu, Hazrat Bilal, Athanasios V. Vasilakos · 发表于：IEEE Transactions on Automation Science and Engineering · 年份：2025 · DOI：10.1109/tase.2025.3552645 · 被引用次数：54 · 研究领域：Fault Detection and Control Systems

Over the past 20 years, many specialists in the business have become interested in managing flexible joint robotics. Considering flexibility in joints in the control scheme of inflexible robots is challenging because it eliminates certain structural properties that facilitate control, such as full activation, distinct control for every socket, and passivity of the torque generated by the motor for coupling velocity. In this research, the authors analyze asymptotic stability for neural models of fixed stochastic process that have time-varying delays, which depend on jumping modes; their derivatives no longer need to be smaller than one. Secondly, by applying a stochastic setup, the researchers explore fixed neutral models with Markovian jump variables based upon mode-dependent time-varying delays for modeling flexible joint robots. Thirdly, this research develops various stability circumstances with delay-dependent conditions based on linear matrix inequalities (LMIs) by combining the novel Lyapunov theory with the convex polyhedron technique, concluding with two numerical examples illustrating their use. As a result, an optimization problem can be addressed to calculate a control law created for implementing the stochastically stable platform. As a final step, the proposed method is verified using a quadruple-tank model and for modeling flexible joint robots. Note to Practitioners—Neural network controllers are frequently employed in the medical field, automation industry, ro...