Search results

This paper aims to propose a hybrid force/position control algorithm based on the stiffness estimation of the unknown environment. A frequency-division control scheme is developed to improve the applicability and reliability of the robot in welding, polishing and assembly.

The stiffness estimation algorithm with time-varying forgetting factors is used to improve the speed and accuracy of the unknown environmental estimation. The sensor force control and robot position control are adopted in different frequencies to improve system stability and communication compatibility. In the low frequency of sensor force control, the Kalman state observer is used to estimate the robot’s joints information, whereas the polynomial interpolation is used to ensure the smoothness of the high frequency of robot position control.

Accurate force control, as well as the system stability, is attained by using this control algorithm.

The entire algorithm is applied to a six-degrees-of-freedom industrial robot, and experiments are performed to confirm its applicability.

The frequency-division control strategy guarantees the control stability and improves the smoothness of the robot movement.

The purpose of this paper is to present the control methods of the exoskeleton robotic arm for stroke rehabilitation.

The robotic arm is driven by the pneumatic muscle actuators. The control system provides independent control for the robot. The joint axes of the robotic arm are arranged to mimic the natural upper limb workspace.

Findings are the classification of training modes and control methods of rehabilitation training, and the characters of both the instant spasm and the sustaining one.

This paper is a preliminary step in the control system and the kinematical characteristics should be analyzed to achieve high precision of movement.

Based on a hierarchical structure, the control system allows the execution of sequence of switching control methods: position, force, force/position and impedance. Patient‐active‐robot‐passive and patient‐passive‐robot‐active (PPRA) training modes are also presented in this paper. In PPRA mode, the robotic arm can provide pre‐specified resistances on the patient's arm. Both instant and sustaining spasms are taken into account for safety.

This paper aims to develope a novel fractional hybrid impedance control (FHIC) approach for high-sensitive contact stress force tracking control of the series elastic muscle-tendon actuator (SEM-TA) in uncertain environments.

In three different cases, the fractional parameters of the FHIC were optimized with the particle swarm optimization algorithm. Its adaptability to the pressure of the sole of the foot on real environments such as grass (soft), carpet (medium) and solid floors (hard) is far superior to traditional impedance control. The main aim of this paper is to derive the dynamic simulation models of the SEM-TA, to develop a control architecture allowing for high-sensitive contact stress force control in three cases and to verify the simulation models and the proposed controller with experimental results. The performance of the optimized controllers was evaluated according to these parameters, namely, maximum overshoot, steady-state error, settling time and root mean squared errors of the positions. Moreover, the frequency robustness analysis of the controllers was made in three cases.

Different simulations and experimental results were conducted to verify the control performance of the controllers. According to the comparative results of the performance, the responses of the proposed controller in simulation and experimental works are very similar.

Origin approach and origin experiment.

This paper aims to propose a dynamic trajectory-tracking control method for robotic transcranial magnetic stimulation (TMS), based on force sensors, which follows the dynamic movement of the patient’s head during treatment.

First, end-effector gravity compensation methods based on kinematics and back-propagation (BP) neural networks are presented and compared. Second, a dynamic trajectory-tracking method is tested using force/position hybrid control. Finally, an adaptive proportional-derivative (PD) controller is adopted to make pose corrections. All the methods are designed for robotic TMS systems.

The gravity compensation method, based on BP neural networks for end-effectors, is proposed due to the different zero drifts in different sensors’ postures, modeling errors in the kinematics and the effects of other uncertain factors on the accuracy of gravity compensation. Results indicate that accuracy is improved using this method and the computing load is significantly reduced. The pose correction of the robotic manipulator can be achieved using an adaptive PD hybrid force/position controller.

A BP neural network-based gravity compensation method is developed and compared with traditional kinematic methods. The adaptive PD control strategy is designed to make the necessary pose corrections more effectively. The proposed methods are verified on a robotic TMS system. Experimental results indicate that the system is effective and flexible for the dynamic trajectory-tracking control of manipulator applications.

The purpose of this paper is to present a novel and accurate coordination control method of dual‐arm modular robot based on position feedback using 3D motion measurement system – Optotrak3020. The end‐position accuracy of dual‐arm modular robot can be improved obviously.

By means of Optotrak3020, the actual end‐position of dual‐arm modular robot is acquired and then returned to the robotic controllers, so the corresponding position error compensation is implemented. Through a 3D simulation and experiment of dual‐arm modular robot for tracking a trajectory of plane right triangle, the feasibility and validity of this control strategy are verified.

The coordination control of dual‐arm modular robot based on position feedback can be accomplished by means of Optotrak3020. The dual‐arm modular robot can accurately accomplish the task of positioning or tracking a reference trajectory.

This real‐time position feedback control method with high control accuracy can be implemented on a PowerCube dual‐arm modular robot system. This method also can be applied to other dual‐arm robot systems, such as mobile robot with dual‐arm, humanoid robot.

The coordination control method of dual‐arm modular robot is presented based on end‐position feedback using Optotrak3020 motion measurement system. The platforms of simulation, communication and experiment are developed, respectively.

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.

The gait planning and control of quadruped crawling robot affect the stability of the robot walking on a slope. The control includes the position control in the swing phase, the force control in the support phase and the switching control in the force/position switching. To improve the passing ability of quadruped crawling robot on a slope, this paper aims to propose a soft control strategy.

The strategy adopts the statically stable crawling gait as the main gait. As the robot moves forward, the position/force section switching control is adopted. When the foot does not touch the ground, the joint position control based on the variable speed PID is performed. When the foot touches the ground, the position-based impedance control is performed, and a fuzzy multi-model switching control based on friction compensation is proposed to achieve smooth switching of force and position.

The proposed method offers a solution for stable passage in slope environment. The quadruped crawling robot can realize smooth switching of force/position, precise positioning in the swing process and soft control of force in the supporting phase. This fact is verified by simulation and test.

The method presented in this paper takes advantage of minimal tracking errors and minimal jitters. Simulations and tests were performed to evaluate the performance.

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.

This paper aims to present a pose correction method based on integrated virtual impedance control for avoiding collision and reducing impact.

The authors first constructed the artificial potential field (APF) considering the geometric characteristics of the end-effector. The characteristics of the proposed field were analyzed considering the position and orientation misalignment. Then, an integrated virtual impedance control was proposed by adding resultant virtual repulsive force into traditional impedance control. Finally, the authors modified a correction trajectory for avoiding collision and reducing impact with virtual force and contact force.

The APF the authors constructed can get rid of a local minimum. Comparing with linear correction, this method is able to avoid collision effectively. When the capturing target has intrinsic estimation error, the pose correction can ensure smooth transitions among different stages.

This method can be implemented on a manipulator with inner position control. It can be applied to an industrial robot with applications on robotic assembly for achieving a softer and smoother process. The method can also be expanded to the kind of claw-shaped end-effectors for capturing target.

As the authors know, it is the first time that the characteristics of the end-effector are considered for avoiding collision in capturing application. The proposed integrated virtual impedance control can provide smooth transitions among different stages without switching different force/position controllers.

Flexible tooling for adjusting the posture of large components of aircraft (LCA) is composed of several numerical control locators (NCLs). Because of the manufacture and installation errors of NCL, the traditional control method of NCL may cause great interaction force between NCLs and form the internal force of LCA during the process of posture adjustment. Aiming at this problem, the purpose of this paper is to propose a control method for posture adjustment system based on hybrid force-position control (HFPC) to reduce the internal force of posture adjustment.

First of all, the causes of internal force of posture adjustment were analyzed by using homogeneous transformation matrix and inverse kinematics. Then, axles of NCLs were divided into position control axle and force control axle based on the screw theory, and the dynamic characteristics of each axle were simulated by MATLAB. Finally, a simulated posture adjustment system was built in the laboratory to carry out HFPC experiment and was compared with the other two traditional control methods for posture adjustment.

The experiment results show that HFPC method for redundant actuated parallel mechanism (RAPM) can significantly reduce the interaction force between NCLs.

In this paper, HFPC is applied to the control of the posture adjustment system, which reduces the internal force of LCA and improves the assembly quality of aircraft parts.