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Time delays are unavoidable since they are ubiquitous and may have a great impact on the performance of neural networks. Resources efficiency is a common concern in many networked systems with limited resources. This article investigates quasisynchronization of the heterogeneous neural networks with time-varying delays via event-triggered impulsive controls which combine the impulsive control and the event-triggered technique. The centralized and distributed event-triggered impulsive controls are, respectively, presented. The suitable Lyapunov functions are constructed, and the triggering functions are derived, which guarantee that not only are the synchronization errors less than a non-negative bound but also the Zeno behaviors can be eliminated. It is suggested that the distributed one has great superiority in taking up fewer resources compared with the time-triggered impulsive control. Numerical examples are proposed to verify the validity of the centralized and distributed control methods. [ABSTRACT FROM AUTHOR]

This paper is concerned with the finite-time synchronization (FTS) of memristor-based recurrent neural networks (MRNNs) combined with inertial items and mixed delays, where both the discrete delays and bounded distributed delays are included. First, MRNNs with inertial items are of second-order state derivatives, thereby differing from the classical first-order MRNNs and bringing difficulties to study the dynamics of such systems. By using the order-reduction method, such kind of second-order MRNNs is transferred into conventional first-order differential systems. Then, under two kinds of designed feedback controllers, several sufficient conditions are derived ensuring the FTS of MRNNs with inertial items and mixed delays. Finally, numerical simulations are provided to show the effectiveness of the results and one application is also presented in pseudorandom number generation. [ABSTRACT FROM AUTHOR]

For a bimanual robot teleoperation system, the internal forces affecting the common deformable manipulation object are determined by both robot arms’ actions. F/T sensorless, uncertain dynamics, and varying time delays increase dramatically the control difficulty for system stability and transparency. To address these problems, an internal force control method based on relative impedance is proposed in this paper. First, we deduce the desired positions to decrease internal force tracking errors and abstract the internal force from the product of the relative distance and impedance. Second, two strategies are adopted for reducing internal force tracking errors and motion synchronization. One is designing an adaptive factor to switch authorities for position control and internal force control. The other is proposing a force estimating method to the coupled item of uncertain dynamics, disturbance, and internal forces. Different parts of the coupled item are divided and solved separately by nonlinear characters. An integral sliding surface based on the relative impedance item is designed to minimize the force tracking errors. Finally, the effectiveness of the method is verified by linear matrix inequalities based on Lyapunov–Krasovskii functional synthesis, and an experiment based on a physical robot system is implemented to validate the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, a novel fuzzy-observer-based hybrid force/position control method is investigated for a bimanual teleoperation system in the presence of dynamics uncertainties, random network-induced time delays, and multiple sampling rates of remote control signals and local measured data. The system structure consists of two pairs of position observers and contact force/torque estimators. The position observers are designed based on Takagi–Sugeno fuzzy inference rules to estimate the delayed remote state with low sampling rates. The force/torque estimators are designed for estimating the coupled item of uncertain dynamics and contact forces without acceleration information. By adding a compensatory item based on an auxiliary model, the force estimation and motion-tracking errors caused by varying dynamics uncertainties decrease, which is certified by two comparative force estimation techniques. The stability condition for the closed-loop system is also proved by the linear matrix inequality method based on the Lyapunov function. Finally, two simulations verify the effectiveness of the proposed method. The results indicate that the proposed method enables a better motion synchronization effect in soft-handling environment. [ABSTRACT FROM AUTHOR]

Robot teleoperation techniques are applied in industrial automation, space, and surgery. Considering the high cost of force–torque (F/T) sensors, an “indirect estimation” of the external force is preferable to “direct sensing” of the force. This study provides a predictive control method for a bimanual teleoperation system without F/T and acceleration sensors. Two types of mirror predictors are built into the control architecture. One of them is a position predictor estimating the position states of the remote side under stochastic network-induced delays. The force predictor estimates the contact forces and dynamic uncertainties utilizing the local time-delayed information. By accurately estimating the local force, the position predictors/observers are used to estimate the internal dynamics of the master and slave simultaneously, and thereby, avoid using the delayed transmitted information. An adaptive fuzzy control strategy based on linear matrix inequalities (LMIs) is proposed to evaluate and suppress the uncertainties, thereby, ensuring that the synchronization errors of the system positions converge to zero and the estimated force approaches real values. The system stability of the closed-loop system is proven using LMIs based on a Lyapunov–Krasovskii functional synthesis. An experimental test involving a dual-arm robot, YuMi, holding and moving a yoga ball is performed based on a semiphysical platform. [ABSTRACT FROM PUBLISHER]