Non negative linear combination grasp force optimization

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.

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.

In this paper, an image-based impedance control strategy for force tracking of an unmanned aerial manipulator (UAM) is presented. Firstly, image features with nice decoupling characteristics are designed and the relationship between the camera motion and the image features is derived. Then, a two-stage strategy is proposed to achieve force tracking of the UAM on a planar object in an arbitrary pose. The first stage drives the end-effector perpendicular to the object’s planer surface by pure visual servoing. To achieve force tracking under the visual guidance, an adaptive visual impedance control method which adjusts the target stiffness according to the force tracking error and the visual feature error is proposed in the second stage. The closed-loop system is proved asymptotically stable by means of Lyapunov analysis. Further, the stability in free flight phase of the stage two is also analyzed and ensured. Finally, experiments were carried out including a whiteboard cleaning task in different poses. The experimental results illustrate the validity and effectiveness of the proposed approach. Note to Practitioners—This work is motivated by the contact force tracking problem of an unmanned aerial manipulator (UAM) without the position measurement. In recent years, impedance control is widely used in force tracking problems. However, position measurement is needed both for the robot and the object in most studies. To generate desired force under visual guidance, image-based visual servoing is combined with a novel impedance controller with variable stiffness. The idea is intuitive. For a human in contact with a wall, the contact force is controlled by adjusting his arm stiffness, which means the stiffness is adapted to the difference between the desired and actual contact force. Furthermore, the stiffness is also adapted to the visual tracking error, removing the assumption that no tracking error is in the inner loop of the impedance controller in the previous works. The proposed strategy is a promising solution for real applications and is validated by a board cleaning experiment in this paper.

Affected by artificial intelligence and automation technology, robotic arms have become increasingly widespread. However, traditional impedance control algorithms have been unable to adapt robotic arms to increasingly complex environmental changes. Therefore, an adaptive impedance control algorithm based on variable stiffness, neural networks, and motion trajectories is developed. Then, it is used for intelligent control of robotic arms. This method employs an impedance relationship model and dynamically optimizes inertia, damping, and stiffness parameters through PD algorithm. The physical quantities such as joint angular acceleration and end displacement are input into the neural network, which outputs key stiffness and interference to estimate the unknown environment in real-time. An adaptive trajectory generation module is designed and combined with contact force feedback to achieve collaborative optimization. The experimental results showed that in terms of contact force with the environment, the error rate between the contact force controlled by this algorithm and the expected value was about 2%. In terms of joint position, the error between the selected joint position in the experiment and the expected value was kept within 0.5%. When running under the motion trajectory generated by the algorithm, the error of each parameter value was less than 1%. The adaptive impedance control algorithm can significantly improve the stability, accuracy, and safety of robotic arm control. The research results have potential applications in improving the environmental adaptability of robotic arms.

Reference trajectory learning based adaptive iterative impedance control for a lower limb rehabilitation exoskeleton with actuator saturation

The interaction force control problem for the object manipulated by the dual-arm manipulator with the unknown environment is addressed in this paper. An adaptive dual-loop impedance control scheme is designed to meet the three control targets, including the trajectory tracking control of the object, the internal force control of the object with the two arms, and the interaction force control of the object with the unknown environment. Firstly, an adaptive variable impedance control method is proposed in the outer loop to copy with the unknown stiffness and position of the environment. Secondly, an impedance control scheme based on the internal force is designed in the inner loop to plan the internal force and the modified position supplied with the outer-loop control. Thirdly, to realize the reference trajectory supplied with the inner-loop control, the virtual decomposition control (VDC) method is incorporated, which is a recursive adaptive control progress, so that the unknown dynamics of the manipulators is acceptable and the computation complexity is also very low to satisfy the real-time control requirement. The stability verifications of the inner-loop impedance control and the VDC method are given based on the Lyapunov method, and the stability analysis of the outer-loop impedance control is obtained using the Routh criterion. Finally, a plane dual-arm robotic manipulator with three degrees of freedom is used to verify the effectiveness of the proposed control scheme.

In this paper, adaptive impedance control is developed for an ${n}$ -link robotic manipulator with input saturation by employing neural networks. Both uncertainties and input saturation are considered in the tracking control design. In order to approximate the system uncertainties, we introduce a radial basis function neural network controller, and the input saturation is handled by designing an auxiliary system. By using Lyapunov’s method, we design adaptive neural impedance controllers. Both state and output feedbacks are constructed. To verify the proposed control, extensive simulations are conducted.

This paper describes an adaptive scheme for controlling the end-effector impedance of robot manipulators. The proposed control system consists of three subsystems: a simple "filter" which characterizes the desired dynamic relationship between the end-effector position error and the end-effector/environment contact force, an adaptive controller which produces the Cartesian-space control input required to provide this desired dynamic relationship, and an algorithm for mapping the Cartesian-space control input to a physically realizable joint-space control torque. The controller does not require knowledge of either the structure or the parameter values of the robot dynamics, and is implemented without calculation of the robot inverse kinematic transformation. As a result, the scheme represents a very general and computationally efficient approach to controlling the impedance of both nonredundant and redundant manipulators. Computer simulation results are given for a planar four degree-of-freedom redundant robot under adaptive impedance control. These results demonstrate that accurate end-effector impedance control and effective redundancy utilization can be achieved simultaneously by using the proposed controller.

In order to complete the robot axle hole assembly task in an unstructured environment, an adaptive impedance control algorithm based on joint space force and position double feedback is proposed, and the assembly process adjustment strategy is designed to complete the assembly task. By installing a torque sensor on the joint of the six-axis cooperative robot, the control model of joint force position with double feedback is formed. By combining the model reference adaptive control (MRAC), operational force planning, and operational space motion planning, an assembly system based on joint space flexibility control is finally designed. According to the task of axle hole assembly, the geometric constraint analysis and force analysis are carried out in the axle hole assembly process, and the corresponding adjustment strategy is designed. The assembly experiments were carried out for holes with different gaps, and the pose parameters, contact force, and control adaptability parameters during assembly were analyzed experimentally. The experimental results show that the flexible assembly system can complete the shaft hole assembly well, and the assembly torque is controlled in a small range, which meets the assembly requirements.

Robotic force control is a core research direction in robotics technology. For tasks such as polishing and assembly, it requires not only precise motion control but also compliant force control by adjusting output force and torque. Traditional impedance control can ensure accuracy in a single environment, but it is limited by the difficulty in accurately acquiring parameters in complex environments; although variable impedance control can adapt to the environment, it faces challenges in control law design. This paper integrates the extended Twin Delayed Deep Deterministic Policy Gradient (ex-TD3) algorithm with impedance control, proposing an ex-TD3-based intelligent impedance control optimization algorithm. A Python-MATLAB/SimMechanics co-simulation platform is established to verify its effectiveness. The experiment adopts a scenario where the robot moves linearly along the z-axis, with fixed environmental stiffness and the desired tracking force changing in stages over time. The model training is iterated for 300 episodes, after which the reward tends to stabilize and the control strategy converges. Experimental results show that, compared with the traditional adaptive impedance control algorithm, the proposed algorithm in this paper exhibits faster force response, smaller force fluctuations during force step changes, smoother force output throughout the process: the maximum overshoot of force control decreases from 6.21N to 1.33N (a reduction rate of 77.63%), and the maximum position tracking error is reduced by 69.5%. With the above optimized design, this study significantly improves the energy efficiency and long-term operational sustainability of mechatronic systems; its precise control of force overshoot and position tracking error effectively reduces mechanical losses and unnecessary energy consumption during the system’s dynamic interaction with the environment.

Assist-as-needed (AAN) algorithms for the control of lower extremity rehabilitation robots can promote active participation of patients during training while adapting to their individual performances and impairments. The implementation of such controllers requires the adaptation of a control parameter (often the robot impedance) based on a performance (or error) metric. The choice of how an adaptive impedance controller is formulated implies different challenges and possibilities for controlling the patient's leg movement. In this paper, we analyze the characteristics and limitations of controllers defined in two commonly used formulations: joint and end-point space, exploring especially the implementation of an AAN algorithm. We propose then, as a proof-of-concept, an AAN impedance controller that combines the strengths of working in both spaces: a hybrid joint/end-point impedance controller. This approach gives the possibility to adapt the end-point stiffness in magnitude and direction in order to provide a support that targets the kinematic deviations of the end-point with the appropriate force vector. This controller was implemented on a two-link rehabilitation robot for gait training—the Lokomat®Pro V5 (Hocoma AG, Switzerland) and tested on 5 able-bodied subjects and 1 subject with Spinal Cord Injury. Our experiments show that the hybrid controller is a feasible approach for exoskeleton devices and that it could exploit the benefits of the end-point controller in shaping a desired end-point stiffness and those of the joint controller to promote the correct angular changes in the trajectories of the joints. The adaptation algorithm is able to adapt the end-point stiffness based on the subject's performance in different gait phases, i.e., the robot can render a higher stiffness selectively in the direction and gait phases where the subjects perform with larger kinematic errors. The proposed approach can potentially be generalized to other robotic applications for rehabilitation or assistive purposes.

Segment erector belongs to heavy load equipment, due to its rigidity and motion inertia, it will cause large inertial force impact during movement, which seriously affects the assembly accuracy, and service life of segment assembler. To address this issue, the present study employs the translation mechanism as a representative example and introduces a force-based impedance control strategy is proposed to facilitate compliant control of the segment erector within a pipe segment assembly machine. Firstly, the architecture of the segment assembler and the operational principles governing its hydraulic system are presented, the classical impedance control model for hydraulic system is created. And regarding the characteristics inherent to the segment assembly hydraulic system and the uncertainty of the field environment during the assembly, designed a force-based adaptive impedance controller and provided a proof of stability for the adaptive impedance model. Finally, through comparison simulations with traditional control methods, it was proven that this method can significantly improve compensate for the inertial force impact during the motion of the pipe segment assembly machine and has better force/position tracking performance.

This paper presented a safety approach for the interactive manipulator. At first, the basic compliance control of the manipulator is realized by using the Cartesian impedance control, which inter-related the external force and the end position. In this way, the manipulator could work as an external force sensor. A novel force-limited trajectory was then generated in a high dynamics interactive manner, keeping the interaction force within acceptable tolerance. The proposed approach also proved that the manipulator was able to contact the environment compliantly, and reduce the instantaneous impact when collision occurs. Furthermore, adaptive dynamics joint controller was extended to all the joints for complementing the biggish friction. Experiments were performed on a 5-DOF flexible joint manipulator. The experiment results of taping the obstacle, illustrate that the interactive robot could keep the desired path precisely in free space, and follow the demand force in good condition.

PurposeThe aim of this paper is to prove that the manipulator is able to contact the environment compliantly, and reduce the instantaneous collision impact.Design/methodology/approachCartesian impedance control law is introduced to interrelate the external force with the Cartesian position.FindingsWhen the estimated external force sensor feedback is the input of the on‐line trajectory regeneration, a novel online motion plan could be performed in a task‐consistent manner keeping the interaction force within the acceptable tolerance. The proposed approach also proves that the manipulator is able to contact the environment compliantly, and reduce the instantaneous collision impact. The virtual decomposition control, simplifying the Cartesian impedance control application of the manipulator and guaranteeing the asymptotical stability of the entire system, is implemented to actualize the approach. Furthermore, adaptive dynamics joint controller is extended to all the joints for complementing the biggish friction.Originality/valueWith the proposed adaptive Cartesian impedance control and the online path planner, the robot will be manipulation‐friendly in an unstructured environment.

To solve the problem of insufficient adjustment ability of traditional impedance control due to uncertainty of environmental parameters when the robot grinds castings, an adaptive variable impedance constant force control method for robot grinding with unknown environmental parameters is proposed. In the proposed method, first, self-impedance impedance control is used as inner loop control to realize fast tracking response of actual trajectory to reference trajectory and reduce overshoot of grinding force; secondly, fuzzy adaptive variable impedance control is used as outer loop control to realize real-time prediction of unknown environmental parameters and real-time adjustment of impedance parameters; finally, the constant force control of robot grinding in unknown environment is realized by combining inner and outer loop control. In order to verify the effectiveness of the proposed method, the simulation model of the algorithm is constructed based on the MATLAB-SIMULINK simulation platform for simulation analysis, and experimental verification is carried out on the casting surface defects robot grinding platform. The simulation and experimental results show that the algorithm can control the grinding force well when grinding unknown environmental parameters, the force control accuracy is within ±1N, the average grinding force error of casting fin grinding is about 7.59%, the average grinding force error of casting riser grinding is about 5.31%, and the contour accuracy of casting surface defects is good after grinding by force control, and the surface quality after grinding is improved.