Adaptive Impedance Control Research Articles

Robot skill acquisition for precision assembly of flexible flat cable with force control

Abstract The flexible flat cable (FFC) assembly task is a prime challenge in electronic manufacturing. Its characteristics of being prone to deformation under external force, tiny assembly tolerance, and fragility impede the application of robotic assembly in this field. To achieve reliable and stable robotic automation assembly of FFC, an efficient assembly skill acquisition strategy is presented by combining a parallel robot skill learning algorithm with adaptive impedance control. The parallel robot skill learning algorithm is proposed to enhance the efficiency of FFC assembly skill acquisition, which reduces the risk of damaging FFC and tackles the uncertain influence resulting from deformation during the assembly process. Moreover, FFC assembly is also a complex contact-rich manipulation task. An adaptive impedance controller is designed to implement force tracking during the assembly process without precise environment information, and the stability is also analyzed based on the Lyapunov function. Experiments of FFC assembly are conducted to illustrate the efficiency of the proposed method. The experimental results demonstrate that the proposed method is robust and efficient.

Distributed Neural Adaptive Impedance Control for Cooperative Manipulation With Unknown Objects.

Existing cooperative manipulation methods for multiple manipulator systems usually assume that the grasp matrix and the desired trajectory of each manipulator are known in advance. In this work, distributed neural adaptive impedance control (AIC) strategies integrating fully distributed observers are proposed to remove both limitations. Specifically, two fully distributed finite-time observers are designed to estimate the actual and ideal states of the reference point without using global information. The estimates of the grasp matrix and the desired trajectory of each end-effector (EE) are then obtained by kinematic constraints and the estimates of the reference point's states. At the controller development, a distributed adaptive impedance model is established to achieve an adaptive trade-off between tracking performance and compliance. Then, distributed neural network (NN)-based tracking control strategies are developed to asymptotically realize the desired adaptive impedance dynamics in the presence of uncertainties. Additionally, a virtual energy tank (EK) is employed to interact with the impedance system to correct the adaptive impedance laws for system passivity. A simulation for four mobile manipulators tightly cooperative transport an unknown object is carried out to demonstrate the established results.

Robust adaptive impedance control of coordinated electrically driven robots: An engineering application for the (p,q)-analogue of Bernstein-Stancu operators

Function approximation techniques have emerged as influential mathematical tools for designing compliant motion controllers capable of managing objects using multiple electrical manipulators without the need for exact models. However, their effectiveness often relies on having access to velocity signals, which may not always be practical in real-world situations. This paper introduces an observer-based robust adaptive impedance control strategy that employs the (p,q)-analogue of Bernstein-Stancu operators as uncertainty estimators to tackle uncertainties in collaborative multiple electrical manipulators. This approach leverages visual task-space information and doesn’t depend on velocity feedback, enhancing cost-effectiveness and applicability in practical robotic systems. The lumped uncertainty is first modeled by this operator. The adaptation laws, derived from stability analysis, are then employed to tune its coefficients, which are not presented in the previous literature. By applying the Lyapunov lemma, the paper ensures that error signals in the controlled system are uniformly ultimately bounded (UUB). The suggested controller is evaluated in a system featuring two arms managing a rigid load. Simulation results showcase the effectiveness and versatility of the proposed approach. The outcomes are also compared with two advanced approximation techniques to demonstrate the precision and effectiveness of the suggested controller design.

Adaptive impedance control method for manipulator based on radial basis function

Purpose In view of the unknown environmental parameters and uncertain interference during gripping by the manipulator, it is difficult to obtain an effective gripping force with the traditional impedance control method. To avoid this dilemma, the purpose of this study is to propose an adaptive control strategy based on an adaptive neural network and a PID search optimization algorithm for unknown environments. Design/methodology/approach The method is based on a variable impedance model, and a new impedance model is established using a radial basis function (RBF) neural network to estimate unknown parameters of the impedance model. The approximation errors of the adaptive neural network and the uncertain disturbance are effectively suppressed by designing the adaptive rate. In the meantime, auxiliary variables are constructed for Lyapunov stability analysis and adaptive controller design, and PSA is used to ensure the stability of the adaptive impedance control system. Based on the Lyapunov stability criterion, the adaptive im-pedance control system is proved to have progressive tracking convergence property. Findings Through comparative simulations and experiments, the superiority of the proposed adaptive control strategy in position and force tracking has been verified. For objects with low flexibility and light-weight (such as a coke, a banana and a nectarine), this control method demonstrates errors of less than 10%. Originality/value This paper uses RBF neural networks to estimate unknown parameters of the impedance model in real-time, enhancing system adaptability. Neural network weights are updated online to suppress errors and disturbances. Auxiliary variables are designed for Lyapunov stability analysis. The PSA algorithm is used to adjust controller parameters in real-time. Additionally, comparative simulations and experi-ments are designed to analyze and validate the performance of controller.

Event-Triggered Adaptive Neural Impedance Control of Robotic Systems.

This article presents an event-triggered adaptive neural impedance control (ETANIC) scheme for robotic systems, where the combination of impedance control (IC) and event-triggered mechanism can significantly reduce the computational burden and the communication cost under the premise of ensuring the stability and tracking performances of the robotic systems. The IC is used to achieve the compliant behavior of the robotic systems in response to the environment. The uncertainties of the robotic systems are estimated by the radial basis function neural network (RBFNN), and the update laws for RBFNN are derived from the designed Lyapunov function. The stability of the whole closed-loop control system is analyzed by the Lyapunov theory, and the event-triggered conditions are designed to avoid the Zeno behavior. The numerical simulation and experimental tests demonstrate that the proposed ETANIC scheme can achieve better efficiency for controlling the robotic systems to perform the interaction tasks with the environment in comparison to the adaptive neural IC (ANIC).

Fuzzy adaptive impedance control for the two-layered vertical cable-driven parallel robot

This study unveils a novel two-layered vertical octahedron cable-driven parallel robot (TLVO CDPR), distinctively engineered for effective force interactions with vertical surfaces while preventing collision with cables. It pioneers an innovative control strategy integrating a position-based fuzzy adaptive impedance controller with a fuzzy Proportional – Integral – Derivative (PID) controller, adeptly managing both the pose and contact force of the robot. While dual control application is often found in rigid-link robots, it remains a largely unexplored frontier in the realm of CDPRs, despite its critical importance in sectors like manufacturing and assembly. The fuzzy adaptive mechanism significantly boosts impedance control efficacy in the face of unpredictable, non-uniform working surfaces, ensuring algorithmic stability and convergence. Concurrently, fuzzy logic is harnessed to optimize PID controller performance. The forward kinematics challenge is efficiently tackled using a least squares method coupled with an Inertial Measurement Unit (IMU), ensuring swift and precise solutions. The robustness and adaptability of the robot and its control systems are thoroughly validated through extensive experimental trials, involving diverse trajectories and varying uncertainties on vertical working surfaces.

Toward Impedance Control in Human-Machine Interfaces for Upper-Limb Prostheses.

Adaptation of upper-limb impedance (stiffness, damping, inertia) is crucial for humans to physically interact with the external environment during grasping and manipulation tasks. Here, we present a novel framework for Adaptive Impedance Control of Upper-limb Prosthesis (AIC-UP) based on surface electromyography (sEMG) signals. AIC-UP uses muscle-tendon models driven by sEMG signals from agonist-antagonist muscle groups to estimate the human motor intent as joint kinematics, stiffness and damping. These estimates are used to implement a variable impedance controller on a simulated robot. Designed for use by amputees, joint torque or stiffness measurements are not used for model calibration. AIC-UP was evaluated with eight able-bodied subjects and a transradial amputee performing target-reaching tasks in simulation through wrist flexion-extension. The control performance was tested in free space and in the presence of unexpected perturbations. We show that AIC-UP outperformed a neural network that regresses the desired kinematics from sEMG signals, in terms of robustness to muscle coactivations needed to complete the task. These results were in agreement with the qualitative feedback from the participants. Additionally, we observed that AIC-UP enables the user to adapt the stiffness and damping to the tasks at hand.

Research on adaptive impedance control strategy for humanoid walking in unstructured dynamic environment

Research on adaptive impedance control strategy for humanoid walking in unstructured dynamic environment

Active impedance control based adaptive locomotion for a bionic hexapod robot

AbstractIn recent years, with the continuous development of human exploration of the natural world, there has been a growing demand across various fields for robots capable of free movement in diverse environments. In this study, we address the issue of compliant control for a hexapod robot in diverse environments and propose a novel control method based on an adaptive impedance model for position control. Our approach enables the hexapod robot to stabilize foot force on complex terrains while preserving balance and body height. Specifically, we analyze the algorithm's parameters and stability by establishing the hexapod robot's structural and impedance control models. To tackle this challenge, we introduce an adaptive impedance control algorithm that estimates environmental parameters using Lyapunov's asymptotic stability theorem and achieves tracking of actual foot‐end forces to desired foot forces. Furthermore, to ensure body stability and height, we incorporate attitude feedback and body feedback. Experimental results from foot force control experiments conducted on a multilegged robot demonstrate that our proposed algorithm enhances the adaptability and robustness of the robot. This research holds significant implications for the stable control of hexapod robots in complex environments and has promising practical applications.

Adaptive variable impedance force/position hybrid control for large surface polishing

PurposeThis paper aims to propose a hybrid force/position controller based on the adaptive variable impedance.Design/methodology/approachFirst, the working space is divided into a force control subspace and a position subspace, the force control subspace adopts the position impedance control strategy. At the same time, the contact force model between the robot and the surface is analyzed in this space. Second, based on the traditional position impedance, the model reference adaptive control is introduced to provide an accurate reference position for the impedance controller. Then, the BP neural network is used to adjust the impedance parameters online.FindingsThe experimental results show that compared with the traditional PI control method, the proposed method has a higher flexibility, the dynamic response accommodation time is reduced by 7.688 s and the steady-state error is reduced by 30.531%. The overshoot of the contact force between the end of robot and the workpiece is reduced by 34.325% comparing with the fixed impedance control method.Practical implicationsThe proposed control method compares with a hybrid force/position based on PI control method and a position fixed impedance control method by simulation and experiment.Originality/valueThe adaptive variable impedance control method improves accuracy of force tracking and solves the problem of the large surfaces with robot grinding often over-polished at the protrusion and under-polished at the concave.

Adaptive Impedance Control for Teleoperation with Event-Triggered Controller

Uncertainty in the master–slave model is one of the primary factors affecting the transparency of teleoperation systems, and congestion in the master–slave communication network also greatly influences the performance of the teleoperation system. This paper proposes a combined framework of adaptive and impedance control to address the uncertainty in the master–slave model and achieve smooth operation at the slave end. Building upon this linear model, an event-triggered mechanism is designed using Lyapunov functions, with dynamic online adjustment of the triggering threshold parameters. Following the completion of the aforementioned research, control objectives are established to validate the performance of the teleoperation control system proposed in this paper. Finally, simulation verification is conducted in the Matlab/Simulink environment.

Adaptive low voltage crossing strategy for symmetrical faults of networked inverters

Abstract Due to the inability of grid-following control methods to compensate for the comprehensive reduction in system inertia, strength, and short-circuit capacity caused by the decreased proportion of synchronous machines, the grid exhibits insufficient resilience to disturbances, leading to severe stability risks in terms of rotor angle, frequency, and voltage. Grid-forming inverters have emerged to address this issue. During symmetrical faults, the low-voltage ride-through of grid-forming inverters often involves freezing the reactive power loop, resulting in a direct output of higher voltage and reactive current. However, an increase in reactive power command leads to a sharp rise in the internal potential magnitude of grid-forming inverters. Upon clearance of the grid fault, the excessively high internal potential may cause a rapid, short-term increase in terminal voltage, potentially resulting in overvoltage, which is detrimental to the operation of grid-connected equipment. In response to this issue, this paper proposes an adaptive low-voltage ride-through control strategy for grid-forming inverters under symmetrical faults controlled by a virtual synchronous machine control method. Initially, a virtual synchronous machine control model is established. Limiting current is achieved through the construction of a fixed virtual impedance loop. The issues associated with fixed virtual impedance current limiting are analyzed, leading to the proposal of an adaptive virtual impedance control strategy. Finally, the feasibility of the proposed method is verified through simulation experiments.

Research on force-controlled polishing system for robotic arm based on compliance control

This thesis enhances robotic arm control in intricate polishing environments by using impedance control for consistent force regulation between the arm and workpiece. An adaptive impedance control algorithm accommodates contact force variations, boosting system robustness. The PSO-BP variable impedance controller integrates particle swarm optimization and BP neural network for real-time optimization, overcoming limitations of fixed-parameter impedance control on surfaces with curvature changes. Experimental results show a significant reduction in polishing force fluctuations and improved force tracking. These findings support efficient robotic arm utilization in polishing tasks.

An Investigation of Real-Time Robotic Polishing Motion Planning Using a Dynamical System

When addressing the technical challenges of achieving precise force tracking during the local polishing process of polishing robots, controlling the contact state between the robot and the workpiece surface is essential. To this end, a contact motion-planning strategy based on dynamic systems is designed to generate trajectory routes during local polishing. The trajectory simulation of the local modulation dynamic system is achieved through the employment of the support vector regression (SVR) algorithm with a Gaussian kernel, facilitating the learning process. The feasibility and stability of planning local paths are validated using the local modulation dynamic system. To maintain a constant contact force between the end-effector polishing robot and the workpiece, an integral adaptive impedance control strategy is utilized, enabling the robot’s compliant control. Subsequently, an experimental system for the polishing robot is constructed in order to verify the effectiveness of the motion-planning system. The experimental results demonstrate that the proposed motion-planning approach is applicable in practical polishing processes, ensuring smooth contact and maintaining the desired contact force when scanning nonlinear surfaces, and thus showcasing stability and practicality.

Impedance control for force tracking of a dual-arm cooperative robot based on particle swarm optimization

PurposeThe purpose of this paper aims to investigate the adaptive impedance control and its optimized PSO algorithm for force tracking of a dual-arm cooperative robot. Because the dual-arm robot is directly in contact with external environment, controlling the mutual force between robot and external environment is of great importance. Besides, a high compliance of the robot should be guaranteed.Design/methodology/approachAn impedance control based on Particle Swarm Optimization (PSO) algorithm is designed to track the mutual force and achieve compliance control of the robot end.FindingsThe experimental results show that the impedance control coefficients can be automatically tuned converged by PSO algorithm.Originality/valueThe system can reach a steady state within 0.03 s with overshoot convergence, and the force fluctuation range at the steady state decreases to about ±0.08 N even under the force mutation condition.

Adaptive positioning method for ball head based on impedance control

PurposeComponent 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.Design/methodology/approachFirst, 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.FindingsDuring 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.Originality/valueIn 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.

Hybrid Adaptive Impedance and Admittance Control Based on the Sensorless Estimation of Interaction Joint Torque for Exoskeletons: A Case Study of an Upper Limb Rehabilitation Robot

Physiotherapy is the treatment to recover a patient’s mobility and limb function after an injury, illness, or disability. Rehabilitation robots can be used to replace human physiotherapists. To ensure safety during robot physical therapy, the patient’s limb needs to be controlled to track a desired joint trajectory, and the torque due to interaction force/torque needs to be measured and regulated. Therefore, hybrid impedance and admittance with position control (HIPC) is required to track the trajectory and simultaneously regulate the contact torque. The literature describes two structures of HIPC: (1) a switched framework between admittance and impedance control operating in parallel (HIPCSW); and (2) a series connection between admittance and impedance control without switching. In this study, a hybrid adaptive impedance and position-based admittance control (HAIPC) in series is developed, which consists of a proportional derivative-based admittance position controller with gravitational torque compensation and an adaptive impedance controller. An extended state observer is used to estimate the interaction joint torque due to human stiff contact with the exoskeleton without the use of force/torque sensor, which is then used in the adaptive algorithm to update the stiffness and damping gains of the adaptive impedance controller. Simulation results obtained using MATLAB show that the proposed HAIPC significantly reduces the mean absolute values of the actuation torques (control inputs) required for the shoulder and elbow joints in comparison with HIPC and HIPCSW.

An adaptive impedance control method for blade polishing based on the Kalman filter

An adaptive impedance control method for blade polishing based on the Kalman filter

The Adaptive Bilateral Control of Underwater Manipulator Teleoperation System with Uncertain Parameters and External Disturbance

A novel self-adaptive bilateral control strategy is introduced to manage uncertainties inherent in the teleoperation of an underwater manipulator system effectively. In response to uncertainties stemming from both the mathematical model and external disturbances, our approach offers innovative solutions. Firstly, to address uncertainties in the master model parameters, we propose a reference adaptive impedance control based on a nominal model. This control strategy dynamically adjusts the reference position of the desired model, leveraging adaptive control laws to compensate for model uncertainties. Additionally, to tackle uncertainties specific to the slave manipulator, we employ adaptive compensation using radial basis function (RBF) networks. Our unique combination of sliding mode variable structure controllers and robust adaptive controllers aims to mitigate approximation errors, ensuring precise tracking of the master manipulator’s position by the slave manipulator. By employing Lyapunov function analysis, we demonstrate the system’s superior tracking performance and global stability, with assured asymptotic convergence for force–position tracking. Through comprehensive experimentation, our results showcase the exceptional force–position tracking capabilities of the overall control system, even under challenging conditions of model uncertainties and external disturbances. Moreover, our system exhibits remarkable stability, reliability, and robustness, underscoring the effectiveness of our proposed adaptive control approach.

The nonlinear model reference adaptive impedance control for underwater manipulator operation objects in bilateral teleoperation system

The bilateral teleoperation system is susceptible to model parameter uncertainty and unknown disturbances in both the master and slave manipulators, resulting in instability and inaccuracies in the force and position tracking performance. To address these issues, a novel nonlinear model reference adaptive impedance controller has been designed to achieve coordinated force and position synchronization of dual manipulators. The adaptive control laws, based on sliding mode functions, have been designed to compensate for the uncertainty of the manipulator model. Furthermore, an adaptive estimation law has been employed to appraise the unknown upper bound of external disturbances. This ensures that the closed-loop model parameters of the dual manipulator converge to the reference impedance model respectively. Simultaneously, it enables the position error between the reference model response and the end-effector task space position to asymptotically converge to zero. To verify the effectiveness of the proposed controller, simulations have been conducted on the MATLAB platform and experiments on a single degree of freedom teleoperation system have been performed. The results demonstrate that the controller exhibits strong robustness and has the capability of force-position tracking ability.