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Component positioning is an important part of aircraft assembly, aiming at the problem that it is difficult to accurately fall into the corresponding ball socket for the ball head connected with aircraft component. This study aims to propose a ball head adaptive positioning method based on impedance control.

First, a target impedance model for ball head positioning is constructed, and a reference positioning trajectory is generated online based on the contact force between the ball head and the ball socket. Second, the target impedance parameters were optimized based on the artificial fish swarm algorithm. Third, to improve the robustness of the impedance controller in unknown environments, a controller is designed based on model reference adaptive control (MRAC) theory and an adaptive impedance control model is built in the Simulink environment. Finally, a series of ball head positioning experiments are carried out.

During the positioning of the ball head, the contact force between the ball head and the ball socket is maintained at a low level. After the positioning, the horizontal contact force between the ball head and the socket is less than 2 N. When the position of the contact environment has the same change during ball head positioning, the contact force between the ball head and the ball socket under standard impedance control will increase to 44 N, while the contact force of the ball head and the ball socket under adaptive impedance control will only increase to 19 N.

In this paper, impedance control is used to decouple the force-position relationship of the ball head during positioning, which makes the entire process of ball head positioning complete under low stress conditions. At the same time, by constructing an adaptive impedance controller based on MRAC, the robustness of the positioning system under changes in the contact environment position is greatly improved.

This paper aims to better design the resonant tank parameters for LLC resonant converter. And, it is found that under heavy load, the voltage gain is affected by junction capacitors of the primary side switching and the parasitic parameters of the secondary side diodes converted to the primary side, which will cause the voltage gain decreased when the switching frequency decreased.

This paper proposes an optimization parameters design method to solve this problem, which was based on impedance model considering the parasitic parameters of switching devices and diodes.

The effectiveness of the proposed method is verified by impedance Bode plots and experimental results.

From the perspective of impedance modeling, this paper finds the reasons for the insufficient voltage regulation capability of LLC resonant converters under heavy load and finds solutions through analysis.

The purpose of this paper is to transfer the impedance regulation of manual belt grinding to robot belt grinding control.

This paper presents a novel methodology for transmitting human impedance regulation skills to robot control in robot belt grinding. First, according to the human grinding experimental data, the skilled worker’s arm impedance regulation is calculated. Next, the human skills are encapsulated as the statistical learning model where the kernel parameters are learned from the demonstration data by Gaussian process regression (GPR) algorithms. The desired profiles of robot are generated by the task planner based on the learned skill knowledge model. Lastly, the learned skill knowledge model is integrated with an adaptive hybrid position-force controller over the trajectory and force of end-effector in robot belt grinding task.

Manual grinding skills are represented and transferred to robot belt grinding for higher grinding quality of the workpiece.

The impedance of the manual grinding is estimated by k-means++ algorithm at different grinding phases. Manual grinding skills (e.g. trajectory, impedance regulation) are represented and modeled by GMM and GPR algorithms. The desired trajectory, force and impedance of robot are generated by the planner based on the learned skills knowledge model. An adaptive hybrid position-force controller is designed based on learned skill knowledge model. This paper proposes a torque-tracking controller to suppress the vibration in robot belt grinding process.

This paper aims to present an impedance control method with mixed H₂/H_∞ synthesis and relaxed passivity for a cable-driven series elastic actuator to be applied for physical human–robot interaction.

To shape the system’s impedance to match a desired dynamic model, the impedance control problem was reformulated into an impedance matching structure. The desired competing performance requirements as well as constraints from the physical system can be characterized with weighting functions for respective signals. Considering the frequency properties of human movements, the passivity constraint for stable human–robot interaction, which is required on the entire frequency spectrum and may bring conservative solutions, has been relaxed in such a way that it only restrains the low frequency band. Thus, impedance control became a mixed H₂/H_∞ synthesis problem, and a dynamic output feedback controller can be obtained.

The proposed impedance control strategy has been tested for various desired impedance with both simulation and experiments on the cable-driven series elastic actuator platform. The actual interaction torque tracked well the desired torque within the desired norm bounds, and the control input was regulated below the motor velocity limit. The closed loop system can guarantee relaxed passivity at low frequency. Both simulation and experimental results have validated the feasibility and efficacy of the proposed method.

This impedance control strategy with mixed H₂/H_∞ synthesis and relaxed passivity provides a novel, effective and less conservative method for physical human–robot interaction control.

To improve the position tracking efficiency of the upper-limb rehabilitation robot for stroke hemiplegia patients, the optimization Learning rate of the membership function based on the fuzzy impedance controller of the rehabilitation robot is propose.

First, the impaired limb’s damping and stiffness parameters for evaluating its physical recovery condition are online estimated by using weighted least squares method based on recursive algorithm. Second, the fuzzy impedance control with the rule has been designed with the optimal impedance parameters. Finally, the membership function learning rate online optimization strategy based on Takagi-Sugeno (TS) fuzzy impedance model was proposed to improve the position tracking speed of fuzzy impedance control.

This method provides a solution for improving the membership function learning rate of the fuzzy impedance controller of the upper limb rehabilitation robot. Compared with traditional TS fuzzy impedance controller in position control, the improved TS fuzzy impedance controller has reduced the overshoot stability time by 0.025 s, and the position error caused by simulating the thrust interference of the impaired limb has been reduced by 8.4%. This fact is verified by simulation and test.

The TS fuzzy impedance controller based on membership function online optimization learning strategy can effectively optimize control parameters and improve the position tracking speed of upper limb rehabilitation robots. This controller improves the auxiliary rehabilitation efficiency of the upper limb rehabilitation robot and ensures the stability of auxiliary rehabilitation training.

The track impedance is one of the most important parameters in designing the track circuit which is widely used in the railway signal control system to detect the presence of a train. This paper aims to calculate the ballastless track impedance by taking account of the influence of reinforcement bars.

This paper proposes a two-step decomposition approach to calculate the ballastless track impedance. The basic idea is evaluating the track impedance without the reinforcement bars by using two-dimensional (2D) finite element method (FEM), and the incremental impedance, because of the reinforcement bar, is calculated by the partial element equivalent circuit (PEEC) method.

The numerical examples show that the proposed approach can guarantee the accuracy and largely reduce the computing time, at least 20 times, compared with the direct three-dimensional (3D) FEM method.

The study provides a fast approach to calculate the ballastless track impedance. However, compared with the 3D FEM method, the results are less accurate because of the approximation and assumption adopted in the method. A future study should pay more attention to improve accuracy of the model.

A fast approach is proposed to calculate the ballastless track impedance taking account of the influence of the reinforcement bars. The computing time can be largely reduced by using the method. With the proposed approach, the influence of insulation of the reinforcement bars on track impedance can be analyzed.

The purpose of this paper is to propose a seamless active interaction control method integrating electromyography (EMG)-triggered assistance and the adaptive impedance control scheme for parallel robot-assisted lower limb rehabilitation and training.

An active interaction control strategy based on EMG motion recognition and adaptive impedance model is implemented on a six-degrees of freedom parallel robot for lower limb rehabilitation. The autoregressive coefficients of EMG signals integrating with a support vector machine classifier are utilized to predict the movement intention and trigger the robot assistance. An adaptive impedance controller is adopted to influence the robot velocity during the exercise, and in the meantime, the user’s muscle activity level is evaluated online and the robot impedance is adapted in accordance with the recovery conditions.

Experiments on healthy subjects demonstrated that the proposed method was able to drive the robot according to the user’s intention, and the robot impedance can be updated with the muscle conditions. Within the movement sessions, there was a distinct increase in the muscle activity levels for all subjects with the active mode in comparison to the EMG-triggered mode.

Both users’ movement intention and voluntary participation are considered, not only triggering the robot when people attempt to move but also changing the robot movement in accordance with user’s efforts. The impedance model here responds directly to velocity changes, and thus allows the exercise along a physiological trajectory. Moreover, the muscle activity level depends on both the normalized EMG signals and the weight coefficients of involved muscles.

The purpose of this paper is to create a model to calculate the high frequency (HF) complex impedance of common low‐voltage DC motors from construction parameters to predict their electromagnetic compatibility (EMC) emission behaviour and perform sensitivity analyses, and an optimization routine is developed to find combinations of construction parameters which best match a desired impedance curve.

The motor is divided into components. For each component, its electrical behaviour including parasitics is derived from material and geometry, and the electromagnetic interactions between components are defined. These results are then reproduced using inductances, capacitances, and resistors where applicable. Mathematical expressions are given to calculate their value from the material and geometrical parameters.

The complex impedance of DC motors can be accurately constructed from geometry and material parameters within a small range. The optimization routine successfully finds parameters to match a desired curve within specified parameter ranges. This can help finding a motor with lower conducted electromagnetic interference.

This analytically parameterized model constitutes a new way to describe electrical motors from an EMC perspective and define critical parameters.

The position/force control of the robot needs the parameters of the impedance model and generates the desired position from the contact force in the environment. When the environment is unknown, learning algorithms are needed to estimate both the desired force and the parameters of the impedance model.

In this paper, the authors use reinforcement learning to learn only the desired force, then they use proportional-integral-derivative admittance control to generate the desired position. The results of the experiment are presented to verify their approach.

The position error is minimized without knowing the environment or the impedance parameters. Another advantage of this simplified position/force control is that the transformation of the Cartesian space to the joint space by inverse kinematics is avoided by the feedback control mechanism. The stability of the closed-loop system is proven.

The position error is minimized without knowing the environment or the impedance parameters. The stability of the closed-loop system is proven.

As printed circuit board (PCB) track geometries shrink, sometimes it becomes necessary to look deeper than the material data sheet when modelling electrical properties of transmission lines; this study looks at how variation in dielectric constant should be handled in multiple and single dielectric PCB builds. This paper describes how even FR4 builds should in some cases be treated as multiple dielectric builds. Approaches for understanding the root cause of effective dielectric constant variation with structure, coupling and scale are considered. An outline of the implementation of the boundary element method is provided for those wishing to look a little deeper into the modelling process. Finally, a brief investigation covers the sources of measurement errors aimed at helping to put the best data back into the modelling process.