With the advantages of high resolution, structural simplicity, reliability, compact size, and high sensitivity, inductive sensors have been widely used in nanopositioning systems. However, the measuring range of traditional inductive sensors are usually limited to 0.2 mm. A novel analysis and design methodology of the miniaturized inductive sensor with large measuring range and nanoscale resolution is proposed. Firstly, an accurate leakage inductance model is established. Secondly, a design rule of armature size is proposed by considering the fringing effect. Then, the error terms introduced by the measurement circuit of differential inductive sensors are analyzed and the corresponding error suppression methods are illustrated. Moreover, A design rule of selecting the optimal excitation frequency is proposed to meet the requirements of high Q value and high bandwidth, and to minimize the impact of core loss resistance on the performance of the sensor. Validated by the experiments, the proposed analysis and design method can effectively guide the design of the miniaturized inductive sensor with nanoscale resolution in the measuring range of ±0.5 mm. The overall size of the fabricated sensor prototypes is less than 6 mm × 6 mm × 3 mm. Combined with large range, high resolution and ideal miniaturization, this inductive sensor can be well suitable for compact and large stroke nanopositioning systems.

This paper investigates the pre-pressure’s influence on the key performance of a traveling wave ultrasonic motor (TRUM) using simulations and experimental tests. An analytical model accompanied with power dissipation is built, and an electric cylinder is first adopted in regulating the pre-pressure rapidly, flexibly and accurately. Both results provide several new features for exploring the function of pre-pressure. It turns out that the proportion of driving zone within the contact region declines as the pre-pressure increases, while a lower power dissipation and slower temperature rise can be achieved when the driving zones and the braking zones are in balance. Moreover, the shrinking speed fluctuations with the increasing pre-pressures are verified by the periodic-varying axial pressure. Finally, stalling torque, maximum efficiency, temperature rise and speed variance are all integrated to form a novel optimization criterion, which achieves a slower temperature rise and lower stationary error between 260 and 320 N. The practical speed control errors demonstrate that the proportion of residual error declines from 2.88% to 0.75% when the pre-pressure is changed from 150 to 300 N, which serves as one of the pieces of evidence of the criterion’s effectiveness.

This paper develops an electromechanical TWUSM (traveling-wave ultrasonic motor) model combining the driving circuit with the motor itself. An equivalent circuit model substituted for the piezoelectric ceramics is designed in the driving circuit model to obtain accurate input currents and powers. Then teeth discretization is implemented in the stator–rotor contact model, which can calculate the interaction forces more accurately. After building the complete model of TWUSM, a typical startup–stopping process is divided into five stages by evaluating the changes in contact status and driving forces. Finally, the fitness of transient responses of the rotor speed increasing from 55 % to 89 % shows that the proposed model fits better than the one without teeth discretization, and the experimental tests under various driving parameters verify the effectiveness of the model.

The synchronization control performance of the Fieldbus control system (FCS) is an important guarantee for the completion of multi-axis collaborative machining tasks, and its synchronization control accuracy is one of the decisive factors for the machining quality. To improve the synchronization control accuracy of FCS, this paper first makes a comprehensive analysis of the factors affecting synchronization in FCS. Secondly, by analyzing the communication model of linear Ethernet, a distributed clock compensation method based on timestamps is proposed to solve the asynchronous problem of communication data transmission in the linear ethernet bus topology. Then, based on the CANopen application layer protocol, the FCS communication and device control task collaboration method is proposed to ensure the synchronous control of multiple devices by FCS. Finally, an experimental platform is built for functional verification and performance testing of the proposed synchronization method. The results show that the proposed synchronization method can achieve a communication synchronization accuracy of 50 ns and a device control synchronization accuracy of 150 ns.

In order to improve the state monitoring and adaptive control capability of inertial stabilization platforms (ISPs) with unknown loads, it is necessary to estimate the dynamic parameters comprehensively online. However, most current online estimation methods regard the system as a linear dual-inertia model which neglects the backlash and nonlinear friction torque. It reduces the accuracy of the model and leads to incomplete and low accuracy of the estimated parameters. The purpose of this research is to achieve a comprehensive and accurate online estimation of multiple parameters of ISPs and lay a foundation for state monitoring and adaptive control of ISPs. First, a dual-inertia model containing backlash and nonlinear friction torque of the motor and load is established. Then, the auto-regressive moving average (ARMA) model of the motor and load is established by the forward Euler method, which clearly expresses the online identification formula of the parameters. On this basis, the adaptive identification method based on the recursive extended least squares (RELS) algorithm is used to realize the online estimation of multiple parameters. The simulation and experimental results show that the proposed adaptive multi-parameter estimation method can realize the simultaneous online identification of the moment of inertia of the load, the damping coefficient of motor and load, the transmission stiffness, the Coulomb friction torque of motor and load, and the backlash, and the steady-state error is less than 10%. Compared with the traditional linear dual-inertia model, the similarity between the model based on the proposed adaptive parameter estimation algorithm and the actual system is increased by 65.3%.