Admittance Control Research Articles

A compliance control strategy of lower‐limb exoskeletons for energy‐efficient stance‐squat transitions with a payload

AbstractThis paper proposes an admittance control algorithm for lower‐limb exoskeletons to assist able‐bodied wearers in performing energy‐efficient stance‐squat transitions while carrying a payload of up to 20 kg. A two‐degree‐of‐freedom (DOF) model dedicated to the stance‐squat transition is derived, which takes into account the wearer's torso inclination angle and the vertical displacement of the hip joint. The torso inclination angle is obtained from an inertial measurement unit (IMU) located at the backpack of the exoskeleton. In addition, the moment caused by the torso plus the payload with respect to the hip joint is estimated. Then a model‐based disturbance observer (DOB) is used to estimate the wearer's joint torque as an indication of the motion intention, and the exoskeleton complies with the intention by following the desired joint angular velocity generated from the estimated wearer's joint torque through an admittance function. Experiments with different admittance parameters are conducted, and the energy consumption of the wearer is evaluated in terms of the normalized energy consumption index (NECI) and the physiological cost index (PCI). Furthermore, experimental comparisons of energy consumption are made for a subject carrying a payload of up to 20 kg and performing repeated stance‐squat transitions with and without the exoskeleton. The results show that a 50% reduction of energy consumption (in terms of PCI) is achieved by the exoskeleton with the proposed admittance control strategy.

Gait Prediction and Variable Admittance Control for Lower Limb Exoskeleton With Measurement Delay and Extended-State-Observer.

The measurement delay of the feedback control system is a universal problem in industrial engineering, which will degrade output performance, especially causing undesirable chatter responses. In this study, a deep-Gaussian-process (DGP)-based method for operator's gait prediction is proposed to estimate the real-time motion intention and to compensate for the measurement delay of the inertial measurement unit (IMU). On the basis of these gait prediction uncertainties quantified by the DGP method, a variable admittance controller is designed to reduce real-time human-exoskeleton interaction torque. The reference trajectory is generated by the admittance controller, which is smoothed by the two-order Bessel interpolation. Meanwhile, the admittance parameters are self-regulated based on the defined uncertainty index of gait prediction. The extend-state observer (ESO) with backstepping iteration is adopted to compensate unmeasured system state, model uncertainties, and unmodeled dynamics of lower limb exoskeleton. The effectiveness of the proposed gait prediction and control scheme is verified by both the comparative simulations and experimental results of the human-exoskeleton cooperative motion.

Recursive Secondary Controller for Voltage Profile Improvement Based on Primary Virtual Admittance Control

This paper proposes a recursive, virtual admittance based, secondary controller for DG units that improves the voltage profile in distribution networks. First, the adaptation of the virtual admittance concept for the goal of voltage regulation is explained. Then, a recursive secondary controller is developed to periodically update the virtual admittance gains. The controller is formulated as an optimization problem with current and stability limitations as constraints. Measurements across the grid, transmitted through low-bandwidth communications, are used to simplify the calculations, resulting in a recursive algorithm. Weight vectors are included in the objective function to allow participation flexibility of each converter. Results show that the primary virtual admittance controller is able to mitigate over- and under-voltages in steady state and under transient conditions. Subsequently, the secondary controller is shown to further improve the voltage profiles across the grid. Experimental results obtained from a laboratory environment, comprising three DG units and a grid emulator, validate the functionality of the complete control structure.

Towards Robust Physical Human Robot Interaction by an Adaptive Admittance Controller

Towards Robust Physical Human Robot Interaction by an Adaptive Admittance Controller

Assembly of low-stiffness parts through admittance control with adaptive stiffness

Existing studies on robot assembly mostly focus on high-stiffness metal parts, which are unsuitable for assembling low-stiffness parts. Additionally, when assembly is achieved by the conventional admittance control method, the number of parameters to be adjusted is significant, and unstable contact force may occur. This study proposes two algorithms, namely admittance control with variable stiffness (ACVS) and admittance control with adaptive stiffness (ACAS), which are suitable for robotic assembly of low-stiffness parts. The proposed ACVS is achieved without a position trajectory, which reduces the number of parameters that need to be adjusted by the user. Furthermore, overshoot is suppressed by calculating the stiffness parameter in real time. The ACAS scheme adjusts the stiffness value in ACVS through a multilayer feedforward neural network; therefore, its overshoot is lower than that of ACVS, and the contact force stability is enhanced. The ACAS scheme thus characterized by a slight overshoot and stable contact force enables stable assembly of low-stiffness parts via the maximum force generated by the robot.

An Admittance Control Method Based on Parameters Fuzzification for Humanoid Steering Wheel Manipulation.

Developing a human bionic manipulator to achieve certain humanoid behavioral skills is a rising problem. Enabling robots to operate the steering wheel to drive the vehicle is a challenging task. To address the problem, this work designs a novel 7-DOF (degree of freedom) humanoid manipulator based on the arm structure of human bionics. The 3-2-2 structural arrangement of the motors and the structural modifications at the wrist allow the manipulator to act more similar to a man. Meanwhile, to manipulate the steering wheel stably and compliantly, an admittance control approach is firstly applied for this case. Considering that the system parameters vary in configuration, we further introduce parameter fuzzification for admittance control. Designed simulations were carried out in Coppeliasim to verify the proposed control approach. As the result shows, the improved method could realize compliant force control under extreme configurations. It demonstrates that the humanoid manipulator can twist the steering wheel stably even in extreme configurations. It is the first exploration to operate a steering wheel similar to a human with a manipulator by using admittance control.

Factors affecting trust in the transparency index for stable and intuitive physical human–robot cooperation

Safety, stability, and intuitive control are the three most basic requirements for the design of a physical human–robot cooperation (HRC) system. High transparency is one of the main design objectives for dynamically coupled HRC systems. However, determinants of system transparency and its influence on system performance are unachievable with the current methods available. This paper presents an overview of admittance control as a method of physical interaction control between robots and human operators, in particular on the analysis of the influencing factors to the computational transparency index. The influence of frequency, sampling point number, and cutoff frequency of the weighting function on the transparency of the admittance controller were analyzed and experimentally verified. The controller was implemented on a four degree-of-freedom interactive manipulator to verify system transparency and stability. The experimental results validate the proposed framework.

Interpolating across the impedance/admittance spectrum with Unified Interaction Control

Impedance Control (IC) and Admittance control (AC) are two control methods for robot-environment interaction which have opposing performance and stability characteristics. Previous research has proposed that the two controllers define a spectrum of controllers. This paper quantifies the IC/AC spectrum as a trade-off between the suppression of force sensor error and modelling error. Unified Interaction Control (UIC) is introduced, which can interpolate across this spectrum of controllers by using a periodic state-reset and an inner-loop gain weighting parameter. The UIC is verified through simulation, experiment, and an eigenvalue analysis. Interpolating across the spectrum allows one to choose an ideal controller given the nature of the robot and environment. This is demonstrated in two case studies: adapting the level of interpolation to optimize performance with a changing environment, and using a static level of interpolation to mitigate the worst-case effects in both IC and AC.

Variable Admittance Control in Sliding Mode for Robust Physical Human–Robot Interaction

Intuitive and comfortable physical human–robot interaction (pHRI) can be realized by changing impedance/admittance parameters corresponding to human interaction. However, this dynamic adjustment may result in drastically changed system dynamics, which usually give rise to system instability. We introduce a power envelope regulation strategy designed to constrain the variability of admittance parameters, thereby ensuring system passivity and mitigating the risk of instability. Then, sliding mode control (SMC) is employed to yield stable and robust performance. A new sliding surface is proposed based on feedback linearization, which shows improved tracking performance and stability compared to a conventional sliding surface. The effectiveness of the proposed sliding surface and associated control is theoretically validated. Notably, our modified sliding surface works universally, regardless of the order of the desired admittance equation. The trade-off between system chattering and robustness is effectively managed using a variable–boundary approach, which dynamically adjusts system constraints to optimize performance. In addition, a control algorithm combining acceleration feedback and sliding mode is proposed, showing improved robustness and tracking accuracy performance compared with applying the proposed SMC algorithm exclusively. The efficacy of these methodologies is substantiated through numerical simulations and empirical experiments.

Dual Neural Network Control of a Hybrid Functional Electrical Stimulation Cycling System

Abstract Hybrid functional electrical stimulation (FES) cycling is a method to rehabilitate people with neurological conditions when they are not in and of themselves capable of fully controlling their extremities. To ensure smooth cycling and adequate stimulation to accomplish the rehabilitation task, admittance control is applied between the human and the robotic cycle. The cycle motor is actuated by a dual neural network control structure with an additional robust element tracking the admittance trajectory, while muscles are stimulated with a simple saturated robust controller. The dual neural network structure allows adaptation to separable functions of the dynamic system, in addition to shared adaptation through the admittance filter. A Lyapunov analysis shows that the admittance tracking controller is globally exponentially stable. A passivity analysis shows that the admittance system and cadence tracking error are output strictly passive. A combined analysis shows that the total system is passive. Experiments are performed on eight participants without neurological conditions, on 12 differing protocols including a robust controller for comparison, the addition of noise, and the addition or lack of stimulation. One participant with a neurological condition was evaluated on three different protocols, including a robust controller, a neural network controller, and a game-like mode where the participant was asked to track the trajectory as it appeared on a screen. Statistical analysis of the experiments show that the standard deviation of the tracking error is significantly improved with the adaptive dual neural network control addition when compared to the robust controller, in some instances reducing the magnitude by half.

A robust electromyography signals-based interaction interface for human-robot collaboration in 3D operation scenarios

At present, the serious challenge of human-robot collaboration is how to establish an interaction interface with multi-directional interactivity and robustness. This paper applies surface electromyography (sEMG) signals as the interface medium, and proposes a robust 3D arm strength estimation model based on hybrid deep learning networks (R3DNet) to build a stable interaction interface and achieve reliable human-robot collaboration performance in 3D operation scenarios. R3DNet adopts the modular design idea and includes a feature extraction module, a regression estimation module, and a robust enhancement module. In the feature extraction module, an autoencoder structure-based network and a new loss function are proposed to extract the fusion features of multi-channel sEMG signals. In the regression estimation module, an arm strength model based on transfer learning algorithms is proposed. This model considers the similarity of arm strength estimation in each direction and realizes the arm strength estimation from one to three dimensions through the fine-tune strategy. The robust enhancement module adopts a residual structure and aims to address the problem of poor model robustness due to non-ideal conditions such as electrode displacement, muscle fatigue, muscle time-varying characteristics and differences in muscle characteristics among subjects. Further, an improved adaptive admittance control method is proposed to help the robot to adjust collaborative behavior according to the changing arm strength of the human tutor. The results of comparison experiments and multi-point flexible assembly experiments verify the validity and engineering realizability of R3DNet for human-robot collaboration.

Improved precise guidewire delivery of a cardiovascular interventional surgery robot based on admittance control.

The development of cardiovascular interventional surgery robots can realize master-slave interventional operations, which will effectively solve the problem of surgeons being injured by X-ray radiation. The delivery accuracy and safety of interventional instruments such as guidewire are the most important issues in the development of robotic systems. Most of the current control methods are position control or force feedback control, which cannot take into account delivery accuracy and safety. A cardiovascular interventional surgery robotic system integrated force sensors is developed. A novel force/position controller, which includes a radial basis function neural networks-based inner loop position controller and a force-based admittance outer loop controller, is proposed. Furthermore, a series of simulations and vascular model experiments are carried out to demonstrate the feasibility and accuracy of the proposed controller. The designed cardiovascular interventional robot is flexible to enter the target vessel branch. Experimental results indicate that the proposed controller can effectively improve the delivery accuracy of the guidewire and reduce the contact force with the vessel wall. The proposed controller based on radial basis function neural network and admittance control is effective in improving delivery accuracy and reducing contact force. The algorithm needs to be further validated in vivo experiments.

Admittance controller complemented with real-time singularity avoidance for rehabilitation parallel robots

Admittance controller complemented with real-time singularity avoidance for rehabilitation parallel robots

Path Recognition and Virtual Guides Design for Path Following Based on Human–Robot Collaboration

This paper presents a path recognition and virtual guides (VGs) design method for a task of path following based on human-robot collaboration (HRC). Firstly, a support vector machine (SVM) combined with least squares (LS) is developed to recognize the desired path based on human guidance. Secondly, VGs are designed based on the admittance control for human-robot collaborative path following, which combine the human's decision-making with the robot's accurate execution capability. The effectiveness and the robustness of the proposed method are verified on a 7-DOF Sawyer robot equipped with a force/torque sensor.

Task Location to Improve Human–Robot Cooperation: A Condition Number-Based Approach

This paper proposes and implements an approach to evaluate human–robot cooperation aimed at achieving high performance. Both the human arm and the manipulator are modeled as a closed kinematic chain. The proposed task performance criterion is based on the condition number of this closed kinematic chain. The robot end-effector is guided by the human operator via an admittance controller to complete a straight-line segment motion, which is the desired task. The best location of the selected task is determined by maximizing the minimum of the condition number along the path. The performance of the proposed approach is evaluated using a criterion related to ergonomics. The experiments are executed with several subjects using a KUKA LWR robot to repeat the specified motion to evaluate the introduced approach. A comparison is presented between the current proposed approach and our previously implemented approach where the task performance criterion was based on the manipulability index of the closed kinematic chain. The results reveal that the condition number-based approach improves the human–robot cooperation in terms of the achieved accuracy, stability, and human comfort, but at the expense of task speed and completion time. On the other hand, the manipulability-index-based approach improves the human–robot cooperation in terms of task speed and human comfort, but at the cost of the achieved accuracy.

Robust Admittance Control of Optimized Robot-Environment Interaction Using Reference Adaptation.

In this article, a robust control scheme is proposed for robots to achieve an optimal performance in the process of interacting with external forces from environments. The environmental dynamics are defined as a linear model, and the interaction performance is evaluated by a defined cost function, which is composed of trajectory errors and force regulation. Based on admittance control, the reference adaptation method is used to minimize the cost function and achieve the optimal interaction performance. To make the trajectory tracking controller robust to the unknown disturbance of internal system dynamics, an auxiliary system is defined and the approximation optimal controller is designed. Experiments on the Baxter robot are conducted to verify the effectiveness of the proposed method.

PID and compliance control-based anti-sticking of drill rod in underground mining

PurposeDuring drilling in coal mines, sticking of drill rod (referred to as SDR in this work) is a potential threat to underground safety. However, no practical measures to deter SDR have been developed yet. The purpose of this study is to develop an anti-SDR strategy using proportional-integral-derivative (PID) and compliance control (PIDC). The proposed strategy is compatible with the drilling process currently used in underground coal mines using drill rigs. Therefore, this study aims to contribute to the PIDC strategy for solving SDR.Design/methodology/approachA hydraulic circuit to reduce SDR was built based on a load-independent flow distribution system, a PID controller was designed to control the inlet hydraulic pressure of the rotation motor and a typical compliance control approach was adopted to control the feed force and displacement. Moreover, the weight and optimal combination of the alternative admittance control parameters for the feed cylinder were obtained by adopting the orthogonal experiment approach. Furthermore, a fuzzy admittance control approach was proposed to control the feed displacement. Experiments were conducted to test the effectiveness of the proposed method.FindingsThe experimental results indicated that the PIDC strategy was appropriate and effective for controlling the rotation motor and feed cylinder; thus, the proposed method significantly reduces the SDR during drilling operations in underground coal mines.Research limitations/implicationsAs the PIDC strategy solves the SDR problem in underground coal mines, it greatly improves the safety of coal mine operation and decreases the power cost. Consequently, it brings the considerable benefits of coal mine production and vast application prospects in other corresponding fields. Actual drilling conditions are difficult to accurately simulate in a laboratory; thus, for future work, drilling experiments can be conducted in actual underground coal mines.Originality/valueThe PIDC-based anti-SDR strategy proposed in this study satisfactorily controls the rotation motor and feed cylinder and facilitates the feed and rotation movements. Furthermore, the tangible novelty of this study results is that it improves the frequency response of the entire drilling system. The drilling process with PIDC decreased the occurrence of SDR by 50%; therefore, the anti-SDR strategy can significantly improve the safety and efficiency of underground coal mining.

Task-Based Human-Robot Collaboration Control of Supernumerary Robotic Limbs for Overhead Tasks

Supernumerary robotic limbs (SRLs) are novel wearable robots that can be used to augment human operating ability in completing some difficult and complex tasks. In this work, a task-based human-SRLs collaboration control method for overhead tasks is developed. It is autonomous and safe without the need for active commands and previous data. A task model is proposed to model the human-SRLs collaboration process that features different task states and state transition conditions. Specifically, the overhead task process is modeled as a finite state machine (FSM) with four task states, three trigger events, and three SRLs actions. The real-time measured human motion data is utilized to trigger the task state transition and estimate the task parameters, which are the constraints for SRLs motion planning. The proposed admittance control with adjustable parameters allows SRLs to behave like spring-damping systems with different characteristics in different states and actions. The admittance control enhances the safety and reliability of the human-SRLs interaction. Finally, the effectiveness of the proposed control method for overhead tasks is further validated on a prototype of the human-SRLs system with two subjects under different installation heights. Trigger events and task parameters are successfully detected and estimated during the task process to trigger the coordination actions of SRLs. The results demonstrate that the task-based collaboration method is useful for overhead tasks with different task parameters.

Stability compensation of an admittance-controlled cartesian robot considering physical interaction with a human operator

In human-machine systems, admittance control is widely used for controlling robots. However, the problem with this method is that the stability can be impacted by the stiffness of the machine and the human operator. In order to minimize the oscillation issue that is caused by insufficient structure stiffness, this study used compensation methods, specifically feed-forward and acceleration feedback. Simulation results show that both compensation methods can expand the stability region of the system. Nevertheless, feedback compensation is more appropriate than feed-forward when taking into account uncertainties in the structure parameters of the system. Even when the system is not perfectly implemented, feedback compensation can keep the system stable, whereas feed-forward compensation causes a significantly reduced stability region. From the experiment, it is also confirmed that the feedback system has an advantage over the feed-forward system, and this simple feedback using an accelerometer can compensate for the insufficient stiffness of the robot structure and greatly enhance the stability of the human-machine system.

Compliant robotic behaviors for satellite servicing.

The demands of traditional industrial robotics differ significantly from those of space robotics. While industry requires robots that can perform repetitive tasks with precision and speed, the space environment needs robots to cope with uncertainties, dynamics, and communication delays or interruptions, similar to human astronauts. These demands make a well-suited application for compliant robotics and behavior-based programming. Pose Target Wrench Limiting (PTWL) is a compliant behavior paradigm developed specifically to meet these demands. PTWL controls a robot by moving a virtual attractor to a target pose. The attractor applies virtual forces, based on stiffness and damping presets, to an underlying admittance controller. Guided by virtual forces, the robot will follow the attractor until safety conditions are violated or success criteria are met. We tested PTWL on a variety of quasi-static tasks that may be useful for future space operations. Our results demonstrate that PTWL is an extremely powerful tool. It makes teleoperation easy and safe for a wide range of quasi-static tasks. It also facilitates the creation of semi-autonomous state machines that can reliably complete complex tasks with minimal human intervention.