基于液态状态机不确定性估计器的时变时延遥操作系统自适应有限时间位置-力控制
Adaptive Finite-Time Position-Force Control of Teleoperation Systems With Time-Varying Delays Using a Liquid State Machine Uncertainty Estimator
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中文总结 AI 辅助
本文针对时变时延遥操作系统，首次将液态状态机（LSM）用于不确定性估计，提出带LSM的有限时间自适应位置-力控制器，仿真显示其跟踪性能优于RBFNN控制器且执行时间更短。
中文摘要 AI 辅助
遥操作系统正日益广泛应用于医疗、康复及远程操纵等场景，这类场景中精准的位置/力跟踪与稳定的交互至关重要。远程环境可能呈现粘弹性、摩擦记忆、接触转变等动态交互效应，致使系统响应不仅依赖当前状态，还与其过往演化相关。这种历史依赖性，加之通信时延与不确定非线性动力学，让精准的不确定性补偿颇具挑战性。传统前馈神经逼近器本身无法保留时间信息，而全循环架构可能引入额外计算与在线训练复杂度。为应对这一局限，本文首次将液态状态机（LSM）应用于双边遥操作控制。本文开发了一种有限时间自适应控制器，该控制器采用带速度和力滤波器的混合位置/力辅助误差系统；同时利用LSM固有的时间处理与衰退记忆能力，辅以简单的自适应机制来估计不确定动力学。通过Lyapunov-Krasovskii框架，确立了闭环稳定性与有限时间收敛性。在弹簧-阻尼及广义麦克斯韦粘弹性环境下的仿真表明，与基于RBFNN的控制器相比，所提控制器的位置与力跟踪性能更优，平均执行时间更低。
英文摘要
Teleoperation systems are increasingly used in medical, rehabilitation, and remote manipulation applications, where accurate position/force tracking and stable interaction are essential. In such applications, the remote environment may exhibit viscoelasticity, frictional memory, contact transitions, and other dynamic interaction effects, causing the system response to depend not only on the current state but also on its previous evolution. This history dependence, together with communication delays and uncertain nonlinear dynamics, makes accurate uncertainty compensation particularly challenging. Conventional feedforward neural approximators do not inherently retain temporal information, while fully recurrent architectures may introduce additional computational and online training complexity. To address this limitation, this article introduces the first application of a liquid state machine (LSM) to bilateral teleoperation control. A finite-time adaptive controller is developed using a hybrid position/force auxiliary error system with velocity and force filters, while the LSM is employed to estimate uncertain dynamics by exploiting its intrinsic temporal processing and fading-memory capabilities with a simple adaptation mechanism. Closed-loop stability and finite-time convergence are established through a Lyapunov--Krasovskii framework. Simulations in spring--damper and generalized Maxwell viscoelastic environments demonstrate improved position and force tracking and lower mean execution time compared with an RBFNN-based controller.
发表机构
- Department of Electrical Engineering, Iran University of Science and Technology (IUST)（伊朗科技大学电气工程系）
机构由 AI 辅助整理，请以论文原文为准。
