Admittance Control Research Articles

Design, testing and control of a smart haptic interface driven by 3D-printed soft pneumatic actuators for virtual reality-based hand rehabilitation

This work presents the main steps of design and testing of a novel haptic interface and adaptive admittance control scheme for optimal regulation of the human–machine interaction in hand rehabilitation mediated by a smart system in virtual–reality environment. The prototype development is the result of an integrated HW/SW design and, moreover, the advantages from additive manufacturing techniques and mechanical properties of soft materials are exploited for the realization steps. Indeed, to make the interface smart, a network of piezo-resistive force sensors is embedded into the user’s command interface and the acquired signals are used for the adaptive regulation of human–machine interaction. Another distinctive feature of the haptic interface, which enables to identify this latter as a smart system, is the interaction control based on the estimation of the user’s intention within a novel scheme of adaptive admittance control. The enhanced training process in rehabilitation assisted by the haptic interface and virtual environment has been experimentally validated during a series of goal-directed tasks. The improvement of the motor performance of the user under the assistance of the adaptive admittance control has been experimentally evaluated. Further results show that the rehabilitation system supports the quantitative assessment of the robustness of the motor learning performance of the hand under the generation of haptic disturbances.

Assessment and enhancement control for small‐signal voltage stability of VSC‐HVDC systems supplying passive industrial loads

AbstractThis paper presents a comprehensive assessment for the small‐signal voltage stability of voltage source converter (VSC)‐HVDC systems supplying industrial loads represented by the induction motors (IMs) and provides a stability enhancement control for the VSCs. First, the steady‐state voltage stability is analysed to seek the factors that affect the existence of the stable equilibrium point. Then, the small‐signal voltage stability analysis is performed based on the developed linear model. Eigenvalue analysis is carried out to capture the critical factors that induce instability phenomena correlative to the system voltage. It demonstrates that decreasing either the proportional gain K of the VSC's PI controller or the electrical distance between the VSC and IM is detrimental to voltage stability. Next, the enhancement control strategy is elaborated. It adopts virtual reactance and virtual admittance control for the VSC to improve the voltage control capability. Finally, the effectiveness and robustness of the proposed enhancement control are validated via time‐domain simulations in a multi‐IM system supplied by VSC‐HVDC.

Uneven Terrain Walking with Linear and Angular Momentum Allocation.

Uneven terrain walking is hard to achieve for most child-size humanoid robots, as they are unable to accurately detect ground conditions. In order to reduce the demand for ground detection accuracy, a walking control framework based on centroidal momentum allocation is studied in this paper, enabling a child-size humanoid robot to walk on uneven terrain without using ground flatness information. The control framework consists of three controllers: momentum decreasing controller, posture controller, admittance controller. First, the momentum decreasing controller is used to quickly stabilize the robot after disturbance. Then, the posture controller restores the robot posture to adapt to the unknown terrain. Finally, the admittance controller aims to decrease contact impact and adapt the robot to the terrain. Note that the robot uses a mems-based inertial measurement unit (IMU) and joint position encoders to calculate centroidal momentum and use force-sensitive resistors (FSR) on the robot foot to perform admittance control. None of these is a high-cost component. Experiments are conducted to test the proposed framework, including standing posture balancing, structured non-flat ground walking, and soft uneven terrain walking, with a speed of 2.8 s per step, showing the effectiveness of the momentum allocation method.

Adaptive fractional-order admittance control for force tracking in highly dynamic unknown environments

PurposeWith the increasing demands of industrial applications, it is imperative for robots to accomplish good contact-interaction with dynamic environments. Hence, the purpose of this research is to propose an adaptive fractional-order admittance control scheme to realize a robot–environment contact with high accuracy, small overshoot and fast response.Design/methodology/approachFractional calculus is introduced to reconstruct the classical admittance model in this control scheme, which can more accurately describe the complex physical relationship between position and force in the interaction process of the robot–environment. In this control scheme, the pre-PID controller and fuzzy controller are adopted to improve the system force tracking performance in highly dynamic unknown environments, and the fuzzy controller is used to improve the trajectory, transient and steady-state response by adjusting the pre-PID integration gain online. Furthermore, the stability and robustness of this control algorithm are theoretically and experimentally demonstrated.FindingsThe excellent force tracking performance of the proposed control algorithm is verified by constructing highly dynamic unstructured environments through simulations and experiments. In simulations and experiments, the proposed control algorithm shows satisfactory force tracking performance with the advantages of fast response speed, little overshoot and strong robustness.Practical implicationsThe control scheme is practical and simple in the actual industrial and medical scenarios, which requires accurate force control by the robot.Originality/valueA new fractional-order admittance controller is proposed and verified by experiments in this research, which achieves excellent force tracking performance in dynamic unknown environments.

Robust variable admittance control for human–robot co-manipulation of objects with unknown load

Over the last years, physical Human–Robot Interaction (pHRI) has become a particularly interesting topic for industrial tasks. An important issue is allowing people and robots to collaborate in an useful, simple and safe manner. In this work, we propose a new framework that allows the person to collaborate with a robot manipulator, while the robot has its own predefined task. To allow the robot to smoothly switch from its own task to be a compliant collaborator for the person, a variable admittance control is developed. Furthermore, in general the task to accomplish requires the robot to carry variable, unknown, loads at the end-effector. To include this feature in our framework, a robust control is also included to preserve the performance of the robot despite uncertainties coming from the unknown load. To validate our approach, experiments were carried out with a Kuka LBR iiwa 14 R820, first to validate both parts of the controller, and finally, to study a use-case scenario similar to an industrial production line. Results show the efficiency of this approach to allow the person to collaborate at any moment while the robot is capable of performing another task. This flexible framework for object co-manipulation also allows unknown loads up to 2 kg to be handled without making the task more difficult for the person.

Evaluation of the FLEXotendon glove-III through a human subject case study.

Cervical spinal cord injury (SCI) can significantly impair an individual's hand functionality due to the disruption of nerve signals from the brain to the upper extremity. Robotic assistive hand exoskeletons have been proposed as a potential technology to facilitate improved patient rehabilitation outcomes, but few exoskeleton studies utilize standardized hand function tests and questionnaires to produce quantitative data regarding exoskeleton performance. This work presents the human subject case study evaluation of the FLEXotendon Glove-III, a 5 degree-of-freedom voice-controlled, tendon-driven soft robotic assistive hand exoskeleton for individuals with SCI. The exoskeleton system was evaluated in a case study with two individuals with SCI through two standardized hand function tests namely, the Jebsen-Taylor Hand Function Test and the Toronto Rehabilitation Institute Hand Function Test and three questionnaires (Capabilities of Upper Extremities Questionnaire, Orthotics Prosthetics Users Survey, Quebec User Evaluation of Satisfaction with Assistive Technology). Minor design changes were made to the exoskeleton: integrated fingertip force sensors to sense excessive grasp force, a quick connect system to expedite the exoskeleton glove swapping process between users, compact tendon tension sensors to measure tendon force for admittance control, and a redesigned smartphone app to encompass all aspects of exoskeleton use.

Experimental Study of Robotic Polishing Process for Complex Violin Surface

This paper presents a robotic polishing process for complex violin surfaces to increase efficiency and minimize the cost and consumed time caused by using labor and traditional polishing machines. The polishing process is implemented based on modeling a smooth path, controlled contact force embedded with gravity compensation and material removal depth. A cubic Non-Uniform Rational Bases-Spline (NURBS) interpolation curve combined with an S-curve trajectory model is used to generate a smooth polishing path on a complex violin surface to achieve stable motion during the polishing process. An online admittance controller added to the fast gravity compensation algorithm maintains an accurate polishing force for equal removal depth on all polished surface areas. Then, based on Pythagorean theory, the removal depth model is calculated for the violin’s complex surface before and after polishing to estimate the accuracy of the polishing process. Experimental studies were conducted by polishing a wooden surface using the 6DOF robot manipulator to validate this methodology. The experimental results demonstrated that the robot had accurate polishing force based on the online admittance controller with gravity compensation. It also showed a precise proportional uniformity of removal depths at the different normal forces of 10, 15, and 20 N. The final results indicated that the proposed experimental polishing approach is accurate and polishes complex surfaces effectively.

Robot Manipulation Skills Transfer for Sim-to-Real in Unstructured Environments

Robot force control that needs to be customized for the robot structure in unstructured environments with difficult-to-tune parameters guarantees robots’ compliance and safe human–robot interaction in an increasingly expanding work environment. Although reinforcement learning provides a new idea for the adaptive adjustment of these parameters, the policy often needs to be trained from scratch when used in new robotics, even in the same task. This paper proposes the episodic Natural Actor-Critic algorithm with action limits to improve robot admittance control and transfer motor skills between robots. The motion skills learned by simple simulated robots can be applied to complex real robots, reducing the difficulty of training and time consumption. The admittance control ensures the realizability and mobility of the robot’s compliance in all directions. At the same time, the reinforcement learning algorithm builds up the environment model and realizes the adaptive adjustment of the impedance parameters during the robot’s movement. In typical robot contact tasks, motor skills are trained in a robot with a simple structure in simulation and used for a robot with a complex structure in reality to perform the same task. The real robot’s performance in each task is similar to the simulated robot’s in the same environment, which verifies the method’s effectiveness.

Embodied Communication: How Robots and People Communicate Through Physical Interaction

Early research on physical human–robot interaction (pHRI) has necessarily focused on device design—the creation of compliant and sensorized hardware, such as exoskeletons, prostheses, and robot arms, that enables people to safely come in contact with robotic systems and to communicate about their collaborative intent. As hardware capabilities have become sufficient for many applications, and as computing has become more powerful, algorithms that support fluent and expressive use of pHRI systems have begun to play a prominent role in determining the systems’ usefulness. In this review, we describe a selection of representative algorithmic approaches that regulate and interpret pHRI, describing the progression from algorithms based on physical analogies, such as admittance control, to computational methods based on higher-level reasoning, which take advantage of multimodal communication channels. Existing algorithmic approaches largely enable task-specific pHRI, but they do not generalize to versatile human–robot collaboration. Throughout the review and in our discussion of next steps, we therefore argue that emergent embodied dialogue—bidirectional, multimodal communication that can be learned through continuous interaction—is one of the next frontiers of pHRI.

Adaptive Admittance Control for Safety-Critical Physical Human Robot Collaboration

Physical human-robot collaboration requires strict safety guarantees, due to the fact that robots and humans work in a shared workspace. This paper presents a novel control framework to handle safety-critical position-based constraints for human-robot physical interaction. The proposed methodology is based on admittance control, exponential control barrier functions (ECBFs), and quadratic program (QP) to achieve compliance during the force interaction between human and robot, while simultaneously guaranteeing safety constraints. In particular, the formulation of admittance control is formulated as a second-order nonlinear control system, and the interaction forces between humans and robots are regarded as the control input. A virtual force feedback for admittance control is provided in real-time by using the ECBFs-QP framework as a compensator of the external human forces. A safe trajectory is therefore derived from the proposed adaptive admittance control scheme for a low-level controller to track. The main innovation of the proposed approach is the ability to enable the robot to naturally comply with human forces without violating any safety constraints, even when external human forces incidentally force the robot to do so. The effectiveness of our approach is demonstrated in simulation studies on a two-link planar robot manipulator.

Position-and-Torque-Sensorless Admittance Control with State Estimation Considering dq-axis Cross-Coupling Factors

This paper presents a position estimation compensation method considering dq-axis cross-coupling factors of Interior Permanent Magnet Synchronous Motors (IPMSMs) inductance for position-and-torque-sensorless admittance control. The cross-coupling factors are from dq-axis mutual inductance and deteriorate control and position estimation performance in control methods without the model. Since dq-axis cross-coupling factors of inductance vary with the rotor position and the current, measurement and estimation of the values are difficult. The proposed method estimates the cross-coupling factors and position estimation errors using dq-axis current variation from the injected voltage. In addition, the proposed method compensates the estimated position based on a voltage equation, including the dq-axis cross-coupling factors, by adding the estimated error. As a result, the vibration of current, velocity, and estimated reaction torque were reduced, and the performance of a position-and-torque-sensorless admittance control system was improved. The experimental results show the proposed method's validity and response by the sensorless admittance control.

Robust Admittance Control With Complementary Passivity

This paper studies a robust admittance control problem with a passivity requirement for stable and unstable linear time-invariant systems, motivated by control issues originated from physical human-robot interaction. A complementary admittance control structure is proposed and analyzed, revealing that the nominal performance (admittance tracking and passivity) is decoupled from robustness. Simulations on the admittance control for human arm strength augmentation with a passivity requirement validate the proposed controller design.

Variable admittance control based on iterative learning scheme with adaptive gradient methods for human-robot collaborative manipulation

Variable admittance control based on iterative learning scheme with adaptive gradient methods for human-robot collaborative manipulation

A Method of Human-Like Compliant Assembly Based on Variable Admittance Control for Space Maintenance.

On-orbit assembly has become a crucial aspect of space operations, where the manipulator frequently and directly interacts with objects in a complex assembly process. The traditional manipulator control has limitations in adapting to diverse assembly tasks and is vulnerable to vibration, leading to assembly failure. To address this issue, we propose a human-like variable admittance control method based on the variable damping characteristics of the human arm. By collecting the velocity and contact force of human arm operations in assembly, we analyze the damping change of human arm and establish the active compliance model based on S-type damping variation rule in assembly. Furthermore, 3 passive contact models are proposed between the end of the human arm and the environment: one-sided bevel contact, both sides bevel contact, and pin-hole contact. On the basis of these active and passive models, a typical space assembly task for a robot is designed, and a human-like variable admittance controller is established and simulated. Finally, we build a ground verification platform and complete different assembly tasks, thereby successfully verifying the safety, robustness, and adaptability of the human-like variable admittance control method.

Sensorless Variable Admittance Control for Human–Robot Interaction of a Dual-Arm Social Robot

Sensorless Variable Admittance Control for Human–Robot Interaction of a Dual-Arm Social Robot

Admittance control design and system testing of industrial robot polishing operation

The control strategy is established with position control as the inner loop and admittance control the outer loop to solve the smooth contact force problem in faucet polishing operation. To avoid the excessive polishing, the parameter sensitivity is also introduced. Firstly, the system model and the controller are presented, the sensitivity of the parameter is calculated, and the effect of the admittance parameters on the output of the system is analyzed. Secondly, through the simulation of position control and admittance control, the change of the contact force between the robot end actuator and the external environment is analyzed, which shows that the proposed control strategy improves the accuracy of position control by 3.0% and guarantees that the contact force between the manipulator and the environment is close to the expected value. Finally, the polishing experiment is carried out on an industrial robot. The experimental results show that the control strategy can well control the contact force in the polishing process of the faucet.

Hybrid impedance and admittance control of robot manipulator with unknown environment

Hybrid impedance and admittance control of robot manipulator with unknown environment

Variable admittance time-delay control of an upper limb rehabilitation robot based on human stiffness estimation

This paper proposes a new variable admittance time-delay control strategy based on human stiffness estimation for improving the effectiveness of robot-assisted cooperative rehabilitation training. This control strategy is developed and implemented on a planar upper limb rehabilitation robot. Given the minimum-jerk-based desired trajectories of human hand position, in the developed control strategy, a time-delay approximator is utilized to estimate the external disturbances and modeling errors without exact knowledge of dynamics parameters, a sliding mode admittance controller is applied to obtained objective admittance characteristics, and an iterative optimization algorithm is used to estimate human arm stiffness and adjust human-robot interaction compliance. The closed-loop stability of the proposed control method is demonstrated via Lyapunov function theory. Experimental investigations involving ten subjects are conducted to validate the feasibility of the proposed control scheme. The experimental results show that the interaction compliance during cooperative rehabilitation training can be accurately adjusted based on selected admittance parameters and human arm stiffness, and it contributes to satisfying the specific training requirements of patients with different weakness levels and promoting the effectiveness of the robot-assisted training.

Continuous mode adaptation for cable-driven rehabilitation robot using reinforcement learning.

Continuous mode adaptation is very important and useful to satisfy the different user rehabilitation needs and improve human-robot interaction (HRI) performance for rehabilitation robots. Hence, we propose a reinforcement-learning-based optimal admittance control (RLOAC) strategy for a cable-driven rehabilitation robot (CDRR), which can realize continuous mode adaptation between passive and active working mode. To obviate the requirement of the knowledge of human and robot dynamics model, a reinforcement learning algorithm was employed to obtain the optimal admittance parameters by minimizing a cost function composed of trajectory error and human voluntary force. Secondly, the contribution weights of the cost function were modulated according to the human voluntary force, which enabled the CDRR to achieve continuous mode adaptation between passive and active working mode. Finally, simulation and experiments were conducted with 10 subjects to investigate the feasibility and effectiveness of the RLOAC strategy. The experimental results indicated that the desired performances could be obtained; further, the tracking error and energy per unit distance of the RLOAC strategy were notably lower than those of the traditional admittance control method. The RLOAC strategy is effective in improving the tracking accuracy and robot compliance. Based on its performance, we believe that the proposed RLOAC strategy has potential for use in rehabilitation robots.

6-DOF Bilateral Teleoperation Hybrid Control System for Power Distribution Live-Line Operation Robot

In the master-slave heterogeneous teleoperation, the workspace of the slave manipulator is usually much larger than that of the master manipulator. This paper proposes a 6-DOF bilateral hybrid teleoperation control strategy to map the workspace of the manipulators without changing the operation accuracy. The hybrid control includes the admittance and force control based on the feedback of the force sensor at the end of the manipulator. The two control strategies switched autonomously through the positioning of the Sigma.7 handle in the workspace. Compared with the classic bilateral teleoperation control, it overcomes the limitation of pre-matching the workspace of the master and slave. When the tool contacts a rigid environment, the robot can make adaptive compensation through the admittance controller even if the operator has not responded. We conduct extensive experiments to evaluate the changes in displacement and velocity before and after the switching process and under different admittance controller parameters. Finally, teleoperation is applied to live-line operation in distribution networks. The experiment proved that the control strategy is more consistent with human operation habits and can improve assembly success rate and efficiency.