In this study, dynamic characteristics of a robot six-axis wrist force/torque (F/T) sensor with crossbeam elastomer are analyzed by two methods of model identification, a method for simultaneous identification of order and parameters of the model (SIM) and a method based on the differential evolution (DE) algorithm. Firstly, by establishing the simplified mechanical model and finite element (FE) model, respectively, natural frequency of the six-axis wrist F/T sensor is calculated. Secondly, dynamic calibration experiment is conducted. Lastly, two dynamic models of the sensor are identified by SIM and DE methods and the dynamic characteristics of the sensor, such as natural frequency and working band, are further analyzed. Comparing experimental values with the theoretical values, the results show that this sensor has a wide dynamic range with the first natural frequency at more than 1600 Hz, working bands (±5%) are more than 400 Hz, and the step response oscillation is intense. This study can provide a reference for the application of the six-axis F/T sensor in the field of dynamic measurement.

In bilateral teleoperation systems, achieving fast position synchronization while maintaining high-communication efficiency represents an essential control objective. This article proposes a simplified control scheme with a finite-time proportional damping (FTP+d) injection method and an event-driven communication mechanism to address the challenge of position synchronization in time-delayed telerobotic systems. The system takes into account the asymmetric time-varying delays and external forces. First, a novel event-triggered strategy based on noninteger power is designed. With a designed error-velocity mixed variable, a new FTP+d controller is developed by employing a continuous nonsmooth function, a damping term, an adaptive term, and noninteger power. With the Lyapunov–Krasovskii (LK) method and the finite-time stability theory, the boundedness of the telerobot’s state is established, and the finite-time synchronization performance is analytically guaranteed. Furthermore, the relationships between controller parameters, time-varying delays, and system stability are also analyzed based on linear matrix inequality (LMI). Finally, both simulation and experimental investigations are conducted to evaluate the efficacy and performance of the proposed control approach.

Multi-dimensional force sensors are extensively utilized in industrial milling machining for measuring cutting force and in intelligent robot applications for measuring joint force. These sensors offer several advantages, including improved static characteristics, well-established static calibration techniques, and temperature compensation technology. However, with the increasing demand for measuring dynamic forces in various applications, force sensors need to possess enhanced dynamic characteristics. Unfortunately, strain force sensors typically exhibit low intrinsic frequency and damping ratio, resulting in slower dynamic response of the sensor.To address this issue and enhance the dynamic performance of multidimensional force sensors, this study proposes a dynamic compensation method based on an improved sparrow search algorithm.Chebyshev chaotic mapping was implemented to increase randomness and ergodicity in the initial population. An adaptive weight factor was incorporated to improve the position update formula of finders and the ratio of vigilantes. These changes enhanced the algorithm’s ability to conduct early global searches and late local depth mining. Subsequently, t -distribution changes and Chebyshev chaotic perturbations were introduced to expand the local search capability.The enhanced sparrow search algorithm improves the optimization capabilities of the original algorithm. By utilizing dynamic calibration experimental data from three-dimensional force sensors, the algorithm’s effectiveness was verified. The results indicate that the method successfully reduces the overshooting amount in each channel of the sensors and shortens the regulation time. Consequently, the dynamic performance of the three-dimensional force sensors is improved, and the algorithm proves to be effective, practical, and robust in compensating for the sensors’ dynamic behavior.

Based on the traditional control approach, the position-tracking performance of the teleoperation system with communication delay is generally asymptotically stable. In practical applications, the closed-loop system is expected to achieve stable and finite-time convergence performance. A novel finite-time bilateral control scheme for a telerobotics system with communication delay is presented in this paper. On the basis of the traditional proportional damping injection control, this paper proposes and designs a new finite-time control method by introducing the non-integer power to the position error, velocity, and the combined error with position error and velocity. In comparison to existing proportional damping injection and finite-time control structures, the proposed method not only achieves the finite-time convergence performance of position tracking, but it also has the advantages of a simple structure and fewer gain coefficients. The controller also incorporates the radial basis function (RBF) neural network and adaptive approach to compensate unknown dynamics and external forces, thus also avoiding the measurement of force signals. The Lyapunov–Krasovskii function is then defined, and it is demonstrated that the position tracking of closed-loop teleoperation system has bounded stability and finite-time control performance. The simulation experiment is also performed, and the results further illustrated the bounded stability of the system. Moreover, compared to the position tracking errors of other non-finite-time control methods, it is demonstrated that the proposed finite-time control scheme has a faster convergence rate and higher convergence precision.

Communication delay is an important factor affecting the stability and performance of telerobotic systems. In this paper, a new adaptive proportional damping controller is proposed to improve the stability and performance of the system in the presence of the cases such as asymmetric communication delay, unknown gravity torque, friction torque, and other disturbance torques. The proposed proportional damping control method combines the RBF neural network and adaptive control strategy to compensate for the unknown torque. The stability and robustness of the system are enhanced by adding error-damping items, operator force, and environmental force items. The Lyapunov–Krasovskii functional is employed to analyze and prove the exponential stability and signal boundedness of the closed-loop system. The simulation results verify the correctness of the proposed method, and the comparison with the results of other control methods shows the effectiveness of the designed control strategy.

This paper investigates the state prediction problems for uncertain Euler–Lagrange systems with large time delays during data transmissions. A set of sequential predictors is proposed to estimate the actual real-time states of the systems by using the delayed information of measurements. The arbitrarily large delays are handled by applying adequate numbers of serial sub-predictors. Meanwhile, the novel prediction structure of each subsystem is designed to deal with nonlinearities and unknown dynamics in the systems. Then, the predictor design is extended to the case without using delayed velocity measurements by updating the structure of the first sub-predictor. Sufficient conditions for the design of predictor gains, ensuring the boundness of prediction errors, are obtained through Lyapunov–Krasovskii functionals. The effectiveness and robustness of the uncertainties of the proposed method are verified by comparative results in simulations.