Impedance Control Algorithm Research Articles

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.

Research on robot tracking force control algorithm based on neural networks

Purpose This study aims to propose a force control algorithm based on neural networks, which enables a robot to follow a changing reference force trajectory when in contact with human skin while maintaining a stable tracking force. Design/methodology/approach Aiming at the challenge of robots having difficulty tracking changing force trajectories in skin contact scenarios, a single neuron algorithm adaptive proportional – integral – derivative online compensation is used based on traditional impedance control. At the same time, to better adapt to changes in the skin contact environment, a gated recurrent unit (GRU) network is used to model and predict skin elasticity coefficients, thus adjusting to the uncertainty of skin environments. Findings In two robot–skin interaction experiments, compared with the traditional impedance control and robot force control algorithm based on the radial basis function model and iterative algorithm, the maximum absolute force error, the average absolute force error and the standard deviation of the force error are all decreased. Research limitations/implications As the training process of the GRU network is currently conducted offline, the focus in the subsequent phase is to refine the network to facilitate real-time computation of the algorithm. Practical implications This algorithm can be applied to robot massage, robot B-ultrasound and other robot-assisted treatment scenarios. Originality/value As the proposed approach obtains effective force tracking during robot–skin contact and is verified by the experiment, this approach can be used in robot–skin contact scenarios to enhance the accuracy of force application by a robot.

Pose detection and docking control for autonomous dynamic docking mechanism with non-cooperative targets

PurposeThis study aims to address the issue of random movement and non coordination between docking mechanisms and locking mechanisms, and proposes a comprehensive dynamic docking control architecture that integrates perception, planning, and motion control.Design/methodology/approachFirstly, the proposed dynamic docking control architecture uses laser sensors and a charge-coupled device camera to perceive the pose of the target. The sensor data are mapped to a high-dimensional potential field space and fused to reduce interference caused by detection noise. Next, a new potential function based on multi-dimensional space is developed for docking path planning, which enables the docking mechanism based on Stewart platform to rapidly converge to the target axis of the locking mechanism, which improves the adaptability and terminal docking accuracy of the docking state. Finally, to achieve precise tracking and flexible docking in the final stage, the system combines a self-impedance controller and an impedance control algorithm based on the planned trajectory.FindingsExtensive simulations and experiments have been conducted to validate the effectiveness of the dynamic docking system and its control architecture. The results indicate that even if the target moves randomly, the system can successfully achieve accurate, stable and flexible dynamic docking.Originality/valueThis research can provide technical guidance and reference for docking task of unmanned vehicles under the ground conditions. It can also provide ideas for space docking missions, such as space simulator docking.

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.

Development of a low-cost upper-body rehabilitation robot for home use.

The inability to use one's hands or arms greatly restricts the ability to perform daily activities. After a developmental or acquired injury, the intensity and frequency of rehabilitation exercises are essential. To alleviate the burden on the healthcare system, robotic systems have been developed to support clinicians' interventions. However, these systems are often bulky and expensive, limiting their use to specific clinical settings and making them impractical for home use. This paper presents the development of an affordable and easy to install 2-DOF five-bar linkage robot designed to be used at home. This work aims to reduce the cost of the robot through actuation optimization, mechanical optimization and 3D printing. The architecture and links length are chosen to optimize the robot's performance in the required workspace. Using sensor feedback, impedance control algorithms and multiple types of exercise such as virtual walls guidance are implemented. Finally, a user interface was programmed to facilitate the robot's use.

Adaptive Variable-Damping Impedance Control for Unknown Interaction Environment

Aiming at the force-tracking error phenomenon of impedance control in an unknown surface environment, an adaptive variable-damping impedance control algorithm is proposed, and the stability and convergence of the algorithm are deduced. An adaptive-law selection rule is proposed to aim at the phenomenon that the adaptive parameters are too large to cause the system oscillation and overshoot and too small to cause the adaptive line variation in the curved surface environment. Finally, experiments conclude that the impedance control based on the adaptive variable-damping algorithm has a better force-tracking effect than the ordinary impedance control in the curved surface environment where the contact surface between the end-effector of the manipulator and the atmosphere is unknown.

Event-triggered critic learning impedance control of lower limb exoskeleton robots in interactive environments

In this paper, we present an event-triggered critic learning impedance control algorithm for a lower limb rehabilitation exoskeleton robot in an interactive environment, where the control objective is specified by a desired impedance model. In comparison to many other traditional impedance controller design algorithms, in this paper, the impedance control problem is transformed into an optimal control problem. Firstly, the interactive environment accounts for the interaction between the exoskeleton, the human, and the environment, and is modeled by a linear time-invariant exogenous system. Secondly, in contrast to time-triggered control design mechanisms, the event-triggered controller is updated only when the system states deviate from prescribed threshold values. To obtain the event-triggered optimal controller, a critic neural network is developed through the framework of reinforcement learning. A modified gradient descent method is introduced to update the weights of the critic network with an additional stable term employed to eliminate the need for an initial admissible control. Meanwhile, with the simultaneous application of historical and transient state data to the critic neural network, the persistent excitation conditions are relaxed. The Lyapunov method is used to rigorously demonstrate the stability of the overall system. Finally, the effectiveness of the proposed algorithm is demonstrated via simulation.

Adaptive Impedance Control for Force Tracking in Manipulators Based on Fractional-Order PID

Force tracking control in robot arms has been widely used in many industrial applications, particularly in tasks involving end effectors and environmental contact, such as grinding, polishing, and other similar operations. However, these environments are not always precisely known. In order to address the force tracking control problem in unknown environments, this paper proposes a fractional-order PID adaptive impedance control strategy based on traditional impedance control. The unknown environmental information is estimated online using the adaptive impedance control algorithm, and the estimated parameters are used to generate reference trajectories to reduce force tracking errors. Fractional-order PID control is then introduced into the system to improve the control performance of the system model, and the theoretical proof of strategy stability is conducted. Finally, a comparison of four strategies was conducted through simulations: traditional impedance control, adaptive hybrid impedance control, adaptive variable impedance control, and the fractional-order PID impedance control proposed in this paper. The simulation results demonstrate that the strategy proposed in this paper exhibits robustness, virtually eliminates overshoot, and enhances response speed. In contrast, both adaptive hybrid impedance control and adaptive variable impedance control exhibit approximately 30% to 45% overshoot during interactions with the environment. Furthermore, in terms of force tracking error, the proposed strategy in this paper outperforms the above two strategies by approximately 29% to 60%, achieving excellent force tracking control performance.

Human muscle fatigue monitoring method and its application for exoskeleton interactive control

Aiming at the human-computer interaction problem during the movement of the rehabilitation exoskeleton robot, this paper proposes an adaptive human-computer interaction control method based on real-time monitoring of human muscle state. Considering the efficiency of patient health monitoring and rehabilitation training, a new fatigue assessment algorithm was proposed. The method fully combined the human neuromuscular model, and used the relationship between the model parameter changes and the muscle state to achieve the classification of muscle fatigue state on the premise of ensuring the accuracy of the fatigue trend. In order to ensure the safety of human-computer interaction, a variable impedance control algorithm with this algorithm as the supervision link was proposed. On the basis of not adding redundant sensors, the evaluation algorithm was used as the perceptual decision-making link of the control system to monitor the muscle state in real time and carry out the robot control of fault-tolerant mechanism decision-making, so as to achieve the purpose of improving wearing comfort and improving the efficiency of rehabilitation training. Experiments show that the proposed human-computer interaction control method is effective and universal, and has broad application prospects.

Impedance control and test of an automatic rotational orthosis for walking with arm swing

Neurological damage after stroke and spinal cord injury often results in walking impairments. The theory of interlimb neural coupling implies that synchronized arm swing should be included during gait training to improve rehabilitation outcomes. We previously developed an automatic rotational orthosis for walking with arm swing (aROWAS), which produced coordinated interlimb movement when running in passive mode. The current case-series study had three aims: to develop impedance control algorithms for generating flexible movement in the aROWAS system, to validate its technical feasibility, and to investigate interlimb muscle activity when using it. A force-free controller was developed to compensate for gravity and friction, and an impedance controller was developed to produce a flexible movement pattern. Experiments were performed on three able-bodied volunteers to evaluate the feasibility of the flexible aROWAS system and muscle activity in their upper and lower limbs was recorded. In force-free mode, the leg rig was static but easily moved by small external forces, and the subjects reported very little resistance when attempting to walk synchronously in the aROWAS system. In impedance mode, the leg rig performed the pre-defined gait pattern, but the joint trajectories were adaptable to external forces. All participants produced earlier hip extension and greater knee flexion during active walking than during passive walking. Furthermore, the arm and lower limb muscles simultaneously produced higher electromyography activity. The control algorithms enabled the aROWAS system to produce walking-like coordinated joint performance in the upper and lower limbs, and also allowed for some degree of adjustment in response to voluntary input from the users. Stronger interlimb muscle activity was produced when participants walked actively in the system. This aROWAS system has the technical potential to serve as an effective tool for investigating interlimb neural coupling and as a novel testbed for walking rehabilitation with synchronized arm swing.

Identification of Exoskeleton Dynamics Parameters and Control Based on Series Elastic Actuator

The lower limb exoskeleton is used for rehabilitation, assistance and maturing. Taking an exoskeleton based on a serial elastic actuator as an object, a method of dynamic parameter identification is proposed. Firstly, the modular SEA structure is introduced, and the exoskeleton is modeled by using the Modified Denavit-Hartenberg (MDH) method. Then, the exoskeleton dynamic model is derived and linearized. Secondly, the excitation trajectory required for exoskeleton parameter identification is designed, which is optimized by using the genetic algorithm. Then, the exoskeleton is tested by using the excitation trajectory to decouple the dynamic parameters. Finally, the exoskeleton impedance control algorithm based on dynamic compensation is designed and compared with the algorithm without dynamic compensation. The results show that the parameters obtained from the identification algorithm and the control algorithm are effective.

Hybrid Force-Impedance Control for Fast End-Effector Motions

Controlling the contact force on various surfaces is essential in many robotic applications such as in service tasks or industrial use cases. Mostly, classical impedance and hybrid motion-force control approaches are employed for these kinds of physical interaction scenarios. In this work, an extended Cartesian impedance control algorithm is developed, which includes geometrical constraints and enables explicit force tracking in a hybrid manner. The unified framework features compliant behavior in the free (motion) task directions and explicit force tracking in the constrained directions. Advantageously, the involved force subspace in contact direction is fully dynamically decoupled from dynamics in the motion subspace. The experimental validation with a torque-controlled robotic manipulator on both flat and curved surfaces demonstrates the performance during highly dynamic desired trajectories and confirms the theoretical claims of the approach.

Research on coordinated control strategy of photovoltaic energy storage system

In this paper, the modular design is adopted to study the control strategy of photovoltaic system, energy storage system and flexible DC system, so as to achieve the design and control strategy research of the whole system of “photovoltaic + energy storage + DC + flexible DC”. This realizes the flexibility and diversity of networking. Due to space reasons, this article focuses on the detailed explanation of the photovoltaic energy storage system control strategy, including the maximum power tracking control strategy of photovoltaic power generation, photovoltaic power generation boost chopper circuit control strategy, photovoltaic power generation DC/AC converter control strategy, PCS device control strategy, PCS virtual impedance control algorithm, etc. Simulation experiments are carried out according to the specific case setting parameters. The simulation results prove that the proposed flexible DC system coordinated control strategy can ensure grid frequency stability and grid voltage stability, and improve the consumption capacity of distributed new energy.

Adaptive-Constrained Impedance Control for Human-Robot Co-Transportation.

Human-robot co-transportation allows for a human and a robot to perform an object transportation task cooperatively on a shared environment. This range of applications raises a great number of theoretical and practical challenges arising mainly from the unknown human-robot interaction model as well as from the difficulty of accurately model the robot dynamics. In this article, an adaptive impedance controller for human-robot co-transportation is put forward in task space. Vision and force sensing are employed to obtain the human hand position, and to measure the interaction force between the human and the robot. Using the latest developments in nonlinear control theory, we propose a robot end-effector controller to track the motion of the human partner under actuators' input constraints, unknown initial conditions, and unknown robot dynamics. The proposed adaptive impedance control algorithm offers a safe interaction between the human and the robot and achieves a smooth control behavior along the different phases of the co-transportation task. Simulations and experiments are conducted to illustrate the performance of the proposed techniques in a co-transportation task.

Research on constant force polishing method of curved mold based on position adaptive impedance control

The quality and life of the injected product are affected by surface quality of curved mold, and reasonable polishing method is the key to obtain the high-quality surface of curved mold. Small polishing tool is controlled by constant displacement polishing in the conventional method of robot polishing. The polishing force is adjusted to affect the surface quality of mold during mold polishing. The shortcomings of traditional control method of robot are investigated by simulation, and the constant force control approach is proposed to maintain stable force, which is achieved by a position-based impedance control algorithm. The polishing experiments of curved mold are conducted by using two control methods with constant displacement control polishing (CDCP) and constant force control polishing (CFCP), respectively. The experimental results show that the CFCP method can maintain polishing force stability and the reduction of surface roughness in the three groups of experiments using CDCP method is much lower than that of CFCP method, respectively. The feasibility of constant force control polishing method (CFCP) is verified.

Coupled Force–Position Control for Dynamic Contact Force Tracking in Uncertain Environment

Both the position and force control of robots are needed in industrial manufacturing, such as in assembly and grinding, etc. In this paper, we concentrate on two issues. One is the system oscillation in traditional hybrid force–position control (HFPC) during switching between force and position control because the diagonal elements in the selection matrix are either 0 or 1. Another issue is the poor force-tracking performance of conventional impedance control, which depends on accurate environmental models. To address these issues, a coupled force–position control (CFPC) method is presented in this paper by combining the proposed adaptive impedance control method with a modified HFPC method. The selection matrix S of HFPC is replaced with a weighted matrix Sw. A weighted matrix regulator is designed to realize smooth switching between position and force control by adjusting the matrix weights in real time, and an adaptive impedance control algorithm is proposed to improve the force-tracking performance in complex environments. To verify the feasibility of the CFPC method proposed in this paper, simulations and physical experiments were conducted. The results show that the CFPC method has the advantages of a better force-tracking performance and a smoother switching between position and force control compared to the traditional HFPC method. A grinding experiment was conducted to further compare the performances of the HFPC and CFPC methods. The roughness values of the ground plates were 0.059 μm for the HFPC method and 0.031 μm for the proposed CFPC method, which demonstrates that the proposed CFPC method has a better performance.

An intelligent control system for robot massaging with uncertain skin characteristics

PurposeTo ensure accurate position and force control of massage robot working on human body with unknown skin characteristics, this study aims to propose a novel intelligent impedance control system.Design/methodology/approachFirst, a skin dynamic model (SDM) is introduced to describe force-deformation on the human body as feed-forward for force control. Then a particle swarm optimization (PSO) method combined with graph-based knowledge transfer learning (GKT) is studied, which will effectively identify personalized skin parameters. Finally, a self-tuning impedance control strategy is designed to accommodate uncertainty of skin dynamics, system delay and signal noise exist in practical applications.FindingsCompared with traditional least square method, genetic algorithm and other kinds of PSO methods, combination of PSO and GKT is validated using experimental data to improve the accuracy and convergence of identification results. The force control is effective, although there are contour errors, control delay and noise problems when the robot does massage on human body.Originality/valueIntegrating GKT into PSO identification algorithm, and designing an adaptive impedance control algorithm. As a result, the robot can understand textural and biological attributes of its surroundings and adapt its planning activities to carry out a stable and accurate force tracking control during dynamic contacts between a robot and a human.

Variable Impedance Control Based on Target Position and Tracking Error for Rehabilitation Robots During a Reaching Task.

To obtain an anthropomorphic performance in physical human-robot interaction during a reaching task, a variable impedance control (vIC) algorithm with human-like characteristics is proposed in this article. The damping value of the proposed method is varied with the target position as well as through the tracking error. The proposed control algorithm is compared with the impedance control algorithm with constant parameters (IC) and another vIC algorithm, which is only changed with the tracking error (vIC-e). The different control algorithms are validated through the simulation study, and are experimentally implemented on a cable-driven rehabilitation robot. The results show that the proposed vIC can improve the tracking accuracy and trajectory smoothness, and reduce the interaction force at the same time.

Improving robotic impedance control performance employing a cascaded controller based on virtual dynamics model

We propose a cascaded impedance control algorithm based on a virtual dynamics model (VDM) to achieve robust and effective mechanical impedance for a robot interacting with unknown environments. This cascaded controller consists of an internal loop of virtual impedance control based on a VDM and an external loop of impedance reference control. The VDM-based virtual impedance control can achieve the same effect as the conventional admittance control; its intermediate output of force/torque serves as the input for the external loop reference impedance control. Therefore, this cascaded controller shows superior performance by combining the advantages of admittance control and impedance control. We evaluate the controller in multiple-contact experiments on a six-degrees of freedom (6-DOF) industrial robot manipulator. The result shows that under various contact situations such as soft and rigid surfaces and free space, the proposed method can rapidly track the target and effectively maintain stability. In the experiments conducted on the robot in contact with various environments, the proposed control method reduced the steady-state error by more than 20% compared with the conventional admittance control.

Mechanical Design and Control System Development of a Rehabilitation Robotic System for Walking With Arm Swing.

Background: Interlimb neural coupling implies that arm swing should be included during gait training to improve rehabilitation outcomes. We previously developed several systems for production of walking with arm swing, but the reaction forces on the foot sole during usage of the systems were not satisfactory and there was potential to improve control system performance. This work aimed to design and technically evaluate a novel system for producing walking with synchronised arm and leg movement and with dynamic force loading on the foot soles.Methods: The robotic system included a passive curved treadmill and a trunk frame, upon which the rigs for the upper and lower limbs were mounted. Ten actuators and servocontrollers with EtherCAT communication protocol controlled the bilateral shoulder, elbow, hip, knee and ankle joints. Impedance control algorithms were developed and ran in an industrial PC. Flexible pressure sensors recorded the plantar forces on the foot soles. The criteria of implementation and responsiveness were used to formally evaluate the technical feasibility of the system.Results: Using impedance algorithms, the system produced synchronous walking with arm swing on the curved treadmill, with mean RMS angular tracking error <2° in the 10 joint profiles. The foot trajectories relative to the hip presented similar shapes to those during normal gait, with mean RMS displacement error <1.5 cm. A force pattern that started at the heel and finished at the forefoot was observed during walking using the system, which was similar to the pattern from overground walking.Conclusion: The robotic system produced walking-like kinematics in the 10 joints and in the foot trajectories. Integrated with the curved treadmill, the system also produced walking-like force patterns on the foot soles. The system is considered feasible as far as implementation and responsiveness are concerned. Future work will focus on improvement of the mechanical system for future clinical application.