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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 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.

This study aims to solve the problem that under light-load conditions, the output voltage regulation capability is lost due to the fact that the voltage gain of the LLC resonant converter does not decrease with the increase of the switching frequency.

In this paper, the impedance model considering the parasitic parameters of the primary and secondary sides is calculated under light-load conditions, the limitations of the previous method are explained and a new circuit improvement is proposed.

In this paper, an improved circuit is proposed, and the impedance Bode plot is used to verify that the circuit can effectively improve the voltage gain problem under light-load conditions. Finally, the experimental results verify the effectiveness of the proposed circuit through comparison with traditional solutions and circuits.

In this paper, the impedance model considering the parasitic parameters of the primary and secondary sides is calculated, the limitations of the previous method are explained and a new circuit improvement is proposed. When compared with the previous method, the proposed circuit improvement can suppress the voltage gain increase that occurs when the switching frequency increases to a certain level.

Teaching by demonstration (TbD) is a promising way for robot learning skills in human and robot collaborative hybrid manufacturing lines. Traditionally, TbD systems have only concentrated on how to enable robots to learn movement skills from humans. This paper aims to develop an extended TbD system which can also enable learning stiffness regulation strategies from humans.

Here, the authors propose an extended dynamical motor primitives (DMP) framework to achieve this goal. In addition to the advantages of the traditional ones, the authors’ framework can enable robots to simultaneously learn stiffness and the movement from human demonstrations. Additionally, Gaussian mixture model (GMM) is used to capture the features of movement and of stiffness from multiple demonstrations of the same skill. Human limb surface electromyography (sEMG) signals are estimated to obtain the reference stiffness profiles.

The authors have experimentally demonstrated the effectiveness of the proposed framework. It shows that the authors approach could allow the robot to execute tasks in a variable impedance control mode with the learned movement trajectories and stiffness profiles.

In robot skill acquisition, DMP is widely used to encode robotic behaviors. So far, however, these DMP modes do not provide the ability to properly represent and generalize stiffness profiles. The authors argue that both movement trajectories and stiffness profiles should be considered equally in robot skill learning. The authors’ approach has great potential of applications in the future hybrid manufacturing lines.

Interaction plays a significant role in robotics and it is considered in all levels of hardware and software control design. Several models have been introduced and developed for controlling robotic interaction. This study aims to address and analyze the state-of-the-art on robotic interaction control by which it is revealed that both practical and theoretical issues have to be faced when designing a controller.

In this review, a critical analysis of the control algorithms developed for robotic interaction tasks is presented. A hierarchical classification of distributed control levels from general aspects to specific control algorithms is also illustrated. Hence, two main control paradigms are discussed together with control approaches and architectures. The challenges of each control approach are discussed and the relevant solutions are presented.

This review presents an evolvement trend of interaction control theories and technologies over time. In addition, it highlights the pros and cons of each control approaches with addressing how the flaws of one control approach were compensated by emerging another control methods.

This review provides the robotic controller designers to select the right architecture and accordingly design the appropriate control algorithm for any given interactive task and with respect to the technology implemented in robotic manipulator.

The purpose of this paper is to design a smart handheld device with force regulating function, which demonstrates the concept of patient-specialized tools.

This handheld device integrates an electrical bioimpedance (EBI) sensor for tissue measurement and a constant force regulation mechanism for ensuring stable tool–tissue contact. Particular focuses in this study are on the design of the constant force regulation mechanism whose design process is through genetic algorithm optimization and finite element simulation. In addition, the output force can be changed to the desired value by adjusting the cross-sectional area of the generated spring.

The following two specific applications based on ex vivo tissues are used for evaluating the designed device. One is in terms of safety of interaction with delicate tissue while the other is for compensating involuntary tissue motion. The results of both examples show that the handheld device is able to provide an output force with a small standard deviation.

In this paper, a handheld device with force regulation mechanism is designed for specific patients based on the genetic algorithm optimization and finite element simulation. The device can maintain a steady and safe interaction force during the EBI measurement on fragile tissues or moving tissues, to improve the sensing accuracy and to avoid tissue damage. Such functions of the proposed device are evaluated through a series of experiments and the device is demonstrated to be effective.

To promote the intuitiveness of collaborative tasks, the negotiation ability of humans with each other has inspired a large amount of studies aimed at reproducing the capacity in physical human-robot interaction (pHRI). This paper aims to promote mutual adaptation in negotiation when both parties possess incomplete information.

This paper introduces virtual fixtures into the traditional negotiation mechanism, locally regulating tracking trajectory and impedance parameters in the negotiating phase until the final plan integrates bilateral intentions well. In the strategy, robots convey its task information to humans and offer groups of guide plans for them to choose, on the premise of maximizing the robot’s own profits.

Compared with traditional negotiation strategies, humans adapt to robots easily and show lower cognitive load in the method, while the satisfied plan shows better performance for the whole human-robot system.

In this study, this paper proposes a novel negotiation strategy to facilitate the mutual adaptation of humans and robots in complicated shared tasks, especially when both parties possess incomplete information of tasks.

In recent years it has been illustrated that the Unified Power Flow Controller (UPFC) installation location plays an important role in effecting nonlinearly its steady state performance. A Pulse Width Modulation (PWM) based UPFC used as a voltage regulator is modeled and analyzed to investigate its optimal position in the transmission line. From the simulation results it is demonstration that by varying the modulation index of the device it can control the distribution of the active and reactive power flows. In addition, this paper deals with the definition and simulation of the control strategy of the closed‐loop UPFC with a series compensation block when it operates as a terminal voltage regulator using Electromagnetic Transients Program (EMTP). The design and simulation of two types of digital controller strategies for the study system in this paper have been carried out. The dynamic performance in terms of speed stability, accuracy, robustness and simplicity of a PI controller with gain scheduling and a fuzzy logic controller have been tested and compared.

Unified power flow controller (UPFC) and advanced static VAR compensator (ASVC) devices are now recognized as the most important flexible AC transmission systems (FACTS) devices. This paper aims to focus on this.

The effects of the location of such installation FACTS devices are examined.

The UPFC as a voltage regulator and ASVC devices applied to a non‐linear load are modeled and analyzed. It was found that the optimum installation position for a UPFC device is at the sending end bus where wide range of receiver terminal line voltage and active power can be controlled. However, it was also found that the optimum installation position for an ASVC device is at the receiving end bus where a wide range of receiver terminal line voltage and active power can be controlled. In both cases, it was found that a wider range of reactive power could be controlled when the devices are installed closer to the receiving end bus.

Shows that the mid‐point of a transmission line is the optimal location for some FACTS devices or reactive power support. The proof is based on a fixed receiving end voltage magnitude, which is practically not valid.

The idea is to exploit the natural stability and performance of the human arm during movement, execution and manipulation. The purpose of this paper is to remotely control a handling robot with a low cost but effective solution.

The developed approach is based on three different techniques to be able to ensure movement and pattern recognition of the operator’s arm as well as an effective control of the object manipulation task. In the first, the methodology works on the kinect-based gesture recognition of the operator’s arm. However, using only the vision-based approach for hand posture recognition cannot be the suitable solution mainly when the hand is occluded in such situations. The proposed approach supports the vision-based system by an electromyography (EMG)-based biofeedback system for posture recognition. Moreover, the novel approach appends to the vision system-based gesture control and the EMG-based posture recognition a force feedback to inform operator of the real grasping state.

The main finding is to have a robust method able to gesture-based control a robot manipulator during movement, manipulation and grasp. The proposed approach uses a real-time gesture control technique based on a kinect camera that can provide the exact position of each joint of the operator’s arm. The developed solution integrates also an EMG biofeedback and a force feedback in its control loop. In addition, the authors propose a high-friendly human-machine-interface (HMI) which allows user to control in real time a robotic arm. Robust trajectory tracking challenge has been solved by the implementation of the sliding mode controller. A fuzzy logic controller has been implemented to manage the grasping task based on the EMG signal. Experimental results have shown a high efficiency of the proposed approach.

There are some constraints when applying the proposed method, such as the sensibility of the desired trajectory generated by the human arm even in case of random and unwanted movements. This can damage the manipulated object during the teleoperation process. In this case, such operator skills are highly required.

The developed control approach can be used in all applications, which require real-time human robot cooperation.

The main advantage of the developed approach is that it benefits at the same time of three various techniques: EMG biofeedback, vision-based system and haptic feedback. In such situation, using only vision-based approaches mainly for the hand postures recognition is not effective. Therefore, the recognition should be based on the biofeedback naturally generated by the muscles responsible of each posture. Moreover, the use of force sensor in closed-loop control scheme without operator intervention is ineffective in the special cases in which the manipulated objects vary in a wide range with different metallic characteristics. Therefore, the use of human-in-the-loop technique can imitate the natural human postures in the grasping task.