arXiv subjects

Armin Attarzadeh

Publications and source records attributed to Armin Attarzadeh.

3 recordsLinked to original sources

arXiv2026

Adaptive Finite-Time Position-Force Control of Teleoperation Systems With Time-Varying Delays Using a Liquid State Machine Uncertainty Estimator

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.

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arXiv2026

Adaptive RBFNN Control of Uncertain Bilateral Teleoperation Systems with Delay-Dependent LMI Stability Conditions

Bilateral teleoperation requires stability despite uncertain master and slave dynamics and delayed communication channels. Existing radial basis function neural network (RBFNN) controllers mainly differ in uncertainty decomposition, while online adaptive parameters often increase with network size. This paper proposes a compact two-sided adaptive controller for a nonlinear bilateral teleoperator with constant forward and backward delays. Operator and environment impedances are incorporated into the manipulator dynamics, and each side uses only two scalar adaptive estimates: one for the ideal RBF weight norm and another for the combined effects of friction, approximation error, and disturbances. Both estimates are updated through sigma-modification, resulting in only two adaptive parameters regardless of the number of RBF nodes. A Lyapunov-Krasovskii functional integrating sliding-variable energy, estimation errors, and delay-dependent integral terms is developed. Free-weighting matrices based on sliding-surface identities provide delay-dependent matrix conditions that guarantee uniform ultimate boundedness of synchronization errors, sliding variables, and adaptive estimates. Simulations on two 2-degree-of-freedom (2-DOF) revolute manipulators with friction, external disturbance, and stepwise operator inputs demonstrate synchronization within approximately 2 s and reveal the trade-off between accuracy and control chattering.

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arXiv2026

Deep Reinforcement Learning for Adaptive Gain Tuning in Control of Teleoperation Manipulators with Joint Flexibility and Time-Varying Delays

Bilateral teleoperation systems that include joint flexibility better reflect real robotic systems used in surgery, space, and rehabilitation. However, joint flexibility together with time-varying communication delays makes it difficult to maintain stable and coordinated motion between the master and slave robots. To address this, we propose a hybrid control method that combines a stable Proportional-plus-Damping (P+d) controller with a model-free deep reinforcement learning agent based on the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. The P+d controller provides basic stability under bounded delays, while the learning agent adjusts and tunes the remote-side proportional and damping gains in real time to reduce vibrations and improve tracking. Stability is guaranteed for bounded time-varying delays using Lyapunov-Krasovskii analysis. The approach provides a practical solution for teleoperation systems facing both joint flexibility and uncertain network delays.

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