Robot Joint Research Articles

Tracking control of robot manipulators actuated by brushless DC motors: Elimination of joint velocity measurements with a robust observer

AbstractThis work introduces an innovative robust partial state feedback controller designed for robotic manipulators actuated by brushless DC motors. The proposed controller/observer structure relies solely on actuator current and robot joint position measurements, while effectively compensating for dynamic uncertainties in both the electrical (actuator dynamics) and mechanical (robot's dynamical terms) subsystems. Specifically, a model–based robust observer, eliminating the need of joint velocity measurements, is combined with a backstepping‐type controller design. As opposed to the previous model based observers that depend on the actual model parameters, the proposed observer structure utilizes the best guest estimates of the system parameters supported with a robust compensation term. This approach eliminates the need for precise knowledge of system parameters of the observer design which is a significant improvement over most of the previous results. This approach ensures the semi‐global, uniformly ultimately bounded joint tracking error signal. The overall stability of the closed‐loop system is affirmed through Lyapunov‐based arguments. Experimental studies conducted on an in‐house‐built two‐link robotic manipulator, actuated by brushless DC motors, are presented to demonstrate the effectiveness and feasibility of the proposed method.

Cornerstone for MRI-compatible robots: A continuous pneumatic actuator with easy-to-connect prismatic/revolute and bidirectional encoder modules

The exceptional imaging capability of magnetic resonance imaging (MRI) enables the applications of robot-assisted surgery under MRI guidance. Essential to these robotic systems are the MRI-compatible robotic joint actuators. Among various actuation methods using MRI-compatible materials, pneumatic actuators are considered the most suitable for this application scenario due to their sterility, safety, and reliability. Yet, the current pneumatic actuators are either bulky, inefficient, or cannot achieve accurate bidirectional continuous motion. As a step forward, a novel high-performance continuous MRI-compatible pneumatic motor is proposed, along with quick-connect revolute/prismatic motion output modules and high accurate bidirectional encoder modules. The high performance of the pneumatic rotary and linear actuators are evaluated through experimental testing, where the rotary actuator can provide up to 2 Nm of output torque and over 28 W of output power, while the linear variant can deliver over 50 N of thrust, which surpass the SOTA competitors. Corresponding dynamic models are established and verified via experiments, with a maximum error of only 2.01%. Closed-loop control experiments demonstrate the high control precision and fast response frequency of the proposed pneumatic actuators, promoting the performance of joint actuators of various MRI-compatible surgical robots.

Machine Learning-Based Multiagent Control for a Bunch of Flexible Robots

In this paper, two novel methodologies of employing machine learning (here, the type-2 fuzzy system) are presented to control a multiagent system in which the agents are flexible joint robots. In the previous methods, the static mode controller has been investigated, which has little flexibility and cannot measure all the states of the system, but in the method presented in this paper, we can eliminate these disadvantages. The control signal is consisting of feedback from the output and the estimated states of the system. In the first method, the control signal coefficients are calculated from the linear matrix inequality (LMI), followed by a type-2 fuzzy system that adds the compensation signal to the control signal. In the second method, the type-2 fuzzy system is directly used to estimate the control signal coefficients which do not employ LMI. Both methods have their disadvantages and benefits, so in general, one of these two methods cannot be considered superior. To prove the effectiveness of the two proposed methods, a topology with four robots has been considered. Both proposed methods have been evaluated for controlling the angle and speed of the robot link. Also, another simulation was made without using the fuzzy system to verify the importance of our methods. Simulation results indicate the proper efficiency of proposed methods, especially in presence of uncertainty in the system.

Research on decoupling control of single leg joints of hydraulic quadruped robot

PurposeThis study proposes a predictive neural network model reference decoupling control method for the coupling problem between the leg joints of hydraulic quadruped robots, and verifies its decoupling effect..Design/methodology/approachThe machine–hydraulic cross-linking coupling is studied as the coupling behavior of the hydraulically driven quadruped robot, and the mechanical dynamics coupling force of the robot system is controlled as the disturbance force of the hydraulic system through the Jacobian matrix transformation. According to the principle of multivariable decoupling, a prediction-based neural network model reference decoupling control method is proposed; each module of the control algorithm is designed one by one, and the stability of the system is analyzed by the Lyapunov stability theorem.FindingsThe simulation and experimental research on the robot joint decoupling control method is carried out, and the prediction-based neural network model reference decoupling control method is compared with the decoupling control method without any decoupling control method. The results show that taking the coupling effect experiment between the hip joint and knee joint as an example, after using the predictive neural network model reference decoupling control method, the phase lag of the hip joint response line was reduced from 20.3° to 14.8°, the amplitude attenuation was reduced from 1.82% to 0.21%, the maximum error of the knee joint coupling line was reduced from 0.67 mm to 0.16 mm and the coupling effect between the hip joint and knee joint was reduced from 1.9% to 0.48%, achieving good decoupling.Originality/valueThe prediction-based neural network model reference decoupling control method proposed in this paper can use the neural network model to predict the next output of the system according to the input and output. Finally, the weights of the neural network are corrected online according to the predicted output and the given reference output, so that the optimization index of the neural network decoupling controller is extremely small, and the purpose of decoupling control is achieved.

A Novel Compact High-Sensitivity Fiber Bragg Grating Sensor for Microvibration Measurement of Robot Joints

A Novel Compact High-Sensitivity Fiber Bragg Grating Sensor for Microvibration Measurement of Robot Joints

Port-Hamiltonian modeling and jumping trajectory tracking control for a bio-inspired quadruped robot

Applying jumping locomotion into autonomous mobile robot is an effective way for improving abilities to overcome barriers and pass through complex terrains. In this paper, based on the designed structural framework for bio-inspired quadruped jumping robot, dynamic model is established by utilizing port-Hamiltonian with dissipation (pHd) method, and a passivity-based control strategy for the robot joints trajectory tracking is presented. First, morphology and biomimetics knowledges of frogs (a kind of animals with excellent jumping skill) motivate us to complete a bio-inspired jumping robot framework based on frog’s motion mechanism and body structure with torsional springs as energy storage device. Then, combining system passivity and dissipation, port-Hamiltonian method is utilized to build a dynamic model for expressing the relationship of energy and force in the designed robot. Jumping process analysis of different stages is also designed for ensuring the robot to complete taking-off and landing stages successfully. Next, with the definition of extending feasible robotic joint trajectory by La Salle invariant set principle, interconnection and damping assignment passivity-based control (IDA-PBC) method is exerted to obtain a trajectory controller for realizing smoothly and stably trajectory tracking in joint space. At last, simulation results show the reasonableness of the designed framework. By comparing our method with state-feedback and sliding mode control, effectiveness of the dynamic model and trajectory controller is also verified.

Mechanical Behaviors of Hybrid Composites with Orthogonal Spiral Wire Mesh and Polyurethane Elastomer

This work aims to significantly improve the mechanical properties of conventional rigid lattice structures under repeatable large deformations. A novel hybrid material is proposed based on the concept of interpenetrating composite materials. The material utilizes a woven TC4 orthogonal spiral wire mesh as the skeleton and PU elastomer (OSWM‐PU) as the matrix. The uniaxial tensile tests demonstrate that OSWM‐PU possesses the excellent load‐bearing capacity, allowing for large deformations (≥60%) while maintaining partial integrity even after matrix fracture. Optical measurements and simulation analysis reveal that Poisson's ratio can be adjusted within a certain range by manipulating the microscopic parameters (p, d) of the longitudinal helical filaments. Cyclic tensile experiments further demonstrate that OSWM‐PU exhibits exceptional energy absorption performance, multiple energy dissipation modes, and a more pronounced Mullins effect. The stress relaxation experiment reveals the significant influence of the volume fraction of the skeleton on long‐term loading conditions. The orthogonal spiral wire skeleton exhibits a superior hooking effect without dividing the matrix, enabling OSWM‐PU to possess enhanced collaborative deformation capability and inherent designability in the orthogonal direction. These characteristics make it highly promising for applications in various robot joints and as flexible aircraft skin, offering excellent prospects for utilization.

A novel Bayesian optimization prediction framework for four-axis industrial robot joint motion state

Robot joints are the main structure for controlling the motion of the machine body, where the motion state of them directly affects the performance of the industrial robot. Due to the difficulty of obtaining the joint torque information of industrial robots, it is very hard to monitor the motion state of them. Based on the velocity and force driven by current of motors, we propose a novel Bayesian optimization framework to predict the joint motion state of industrial robot in this paper. Based on the temporal correlation of joint current and the correlation between the current and motion state of joint, we use the LSTM and BiLSTM to regressing prediction of the current and state of joint motor first. Then, the Bayesian optimization method is used to adjust the hyperparameters of our network, which realize the analysis of the joint motor current under different motion states and improve the accuracy of the prediction of joint motion states. Finally, we design the joint current acquisition platform of industrial robot based on Hall current sensors, which can collect joint currents without contact and generate experimental dataset. Comparing with the popular intelligent methods, the results show that our Bayesian optimization framework realizes a more accurate prediction of motion state for the four-axis industrial robot on the basis of contact-less current acquisition.

Dynamic compliance of energy-saving legged elastic parallel joints for quadruped robots: design and realization

Dynamic compliance of energy-saving legged elastic parallel joints for quadruped robots: design and realization

Detecting contamination events during robotic total joint arthroplasty

Robot-assisted total joint arthroplasty (robotic-TJA) has become more widespread over the last 20 years due to higher patient satisfaction and reduced complications. However, robotic TJA may have longer operative times and increased operating room traffic, which are known risk factors for contamination events. Contamination of surgical instruments may be contact- or airborne-related with documented scalpel blade contamination rates up to 9%. The robot arm is a novel instrument that comes in and out of the surgical field, so our objective was to assess whether the robot arm is a source of contamination when used in robotic TJA compared to other surgical instruments. This was a prospective, single-institution, single-surgeon pilot study involving 103 robotic TJAs. The robot arm was swabbed prior to incision and after closure. Pre- and postoperative control swabs were also collected from the suction tip and scalpel blade. Swabs were incubated for 24hours on tryptic soy agar followed by inspection for growth of any contaminating bacteria. A contamination event was detected in 10 cases (10%). The scalpel blade was the most common site of contamination (8%) followed by the robot arm (2%) and suction tip (0%). Robotic TJA is contaminated with bacteria at a rate around 10%. Although the robot arm is an additional source of potential contamination, the robot arm accrues bacterial contamination infrequently compared to the scalpel blade. Contamination of the robot arm during robotic TJA is minimal when compared to contamination of the scalpel blade.

Barrier Function-Based Integral Sliding Mode Controller Design for a Single-Link Rotary Flexible Joint Robot

Abstract This paper proposes and evaluates a novel control approach for trajectory tracking, stability enhancement, and vibration reduction of a flexible joint robot (FJR). The FJR is a 2-degree-of-freedom underactuated nonlinear system that is challenging to control due to vibration, underactuation, uncertainties, and external disturbances. The control objectives are high trajectory tracking performance together with suppressing vibration. The proposed control approach uses integral sliding mode control (ISMC) combined with a barrier function based on back-stepping. This ensures robust and smooth performance by eliminating the reaching phase where sliding mode control (SMC) is typically not robust. Robustness is thus guaranteed from the start. The FJR is modeled as a 4th-order system using Lagrangian mechanics and decomposed into two 2nd-order subsystems for control design. Sliding variables are defined for each subsystem. Using these variables, the proposed control achieves robust trajectory tracking, stability, and vibration reduction. Numerical simulations in MATLAB validate the superior performance of the proposed ISMC-barrier function control compared to conventional ISMC for the FJR. This novel control approach addresses the challenges of controlling this FJR.

Design and Kinematics of a Novel Continuum Robot Connected by Unique Offset Cross Revolute Joints

Abstract Continuum robots have continuous structures and inherent compliance, which can be used for accessing unstructured and confined space in many fields, such as minimally invasive surgery and aero-engine in-situ inspection. A novel cable-driven continuum robot connected by unique offset cross revolute joints is proposed in this paper, which has excellent bending capacity and appropriate torsional stiffness compared with continuum robots connected by revolute joints and spherical joints, respectively. Furthermore, the kinematic modeling and analysis are carried out. The mappings among robot's actuator space, joint space and task space are established step by step. Particularly, an improved inverse kinematics algorithm is proposed by combining the constant curvature method with the numerical iterative method. This combined inverse kinematics algorithm can effectively reduce the error of approximate solution derived by the traditional constant curvature method. Numerical simulations are conducted to verify the proposed algorithm and analyze workspace of the continuum robot. Finally, experimental prototype of the robot is built to verify its excellent bending capacity and the correctness of the proposed kinematic model.

Bipedal Robot Gait Generation Using Bessel Interpolation.

This paper introduces a novel approach to bipedal robot gait generation by proposing a higher-order form through the parameter equation of first-order Bessel interpolation. The trajectory planning for the bipedal robot, specifically for stepping up or down stairs, is established based on a three-dimensional interpolation equation. The experimental prototype, Roban, is utilized for the study, and the structural sketch of a single leg is presented. The inverse kinematics expression for the leg is derived using kinematic methods. Employing a position control method, the angle information is transmitted to the robot's joints, enabling the completion of both downstairs simulation experiments and physical experiments with the Roban prototype. The analysis of the experimental process reveals a noticeable phenomenon of hip and ankle joint tilting in the robot. This observation suggests that low-cost bipedal robots driven by servo motors exhibit low stiffness characteristics in their joints.

Optimization of stiffness performance of six-axis industrial robots based on posture stiffness evaluation index

Aiming at the problem that the stiffness performance of industrial robots depends on the posture, a method to improve the stiffness performance by optimizing the robot posture is proposed based on the robot posture stiffness evaluation index. The robot joint stiffness identification model is established by using the principle of virtual work and Jacobi matrix, and the stiffness values of six joints are obtained by least-squares. On the basis of revealing the mapping relationship between robot joints and end, the robot end flexibility ellipsoid is obtained, and the volume of the end flexibility ellipsoid is utilized as the global stiffness coefficient of the robot, so as to obtain the robot posture stiffness evaluation index. According to the posture stiffness evaluation index, the robot posture optimization model is established, and the optimized posture is obtained by using genetic algorithm. Taking the QJR6-1 robot as the experimental object, under the maximum load, the deformation of the robot end center is reduced from 0.6238 to 0.3984 mm, which is 36.13%, and the deformation of the end is greatly reduced compared with that before the optimization, which indicates that the robot’s stiffness is improved after the posture optimization, and it verifies the feasibility of the optimization model, and provides a reference to improve the stiffness performance of industrial robots further.

Force Compensation Control for Electro-Hydraulic Servo System with Pump–Valve Compound Drive via QFT–DTOC

Each joint of a hydraulic-driven legged robot adopts a highly integrated hydraulic drive unit (HDU), which features a high power–weight ratio. However, most HDUs are throttling-valve-controlled cylinder systems, which exhibit high energy losses. By contrast, pump control systems offer a high efficiency. Nevertheless, their response ability is unsatisfactory. To fully utilize the advantages of pump and valve control systems, in this study, a new type of pump–valve compound drive system (PCDS) is designed, which can not only effectively reduce the energy loss, but can also ensure the response speed and response accuracy of the HDUs in robot joints to satisfy the performance requirements of robots. Herein, considering the force control requirements of energy conservation, high precision, and fast response of the robot joint HDU, a nonlinear mathematical model of the PCDS force control system is first introduced. In addition, pressure–flow nonlinearity, friction nonlinearity, load complexity and variability, and other factors affecting the system are considered, and a novel force control method based on quantitative feedback theory (QFT) and a disturbance torque observer (DTO) is designed, which is denoted as QFT–DTOC herein. This method improves the control accuracy and robustness of the force control system, reduces the effect of the disturbance torque on the control performance of the servo motor, and improves the overall force control performance of the system. Finally, experimental verification is performed using the PCDS performance test platform. The experimental results and quantitative data show that the QFT–DTOC proposed herein can significantly improve the force control performance of the PCDS. The relevant force control method can be used as a bottom-control method for the hydraulic servo system to provide a foundation for implementing the top-level trajectory planning of the robot.

Hysteresis identification of joint with harmonic drive transmission based on Monte Carlo method

In this study, we analyze the hysteresis behavior of robotic joints equipped with harmonic drivers under various speeds and loads, focusing on a range of hysteresis models including the Bouc–Wen (BW), Fractional Order Bouc–Wen (FOBW), and their integration with the Krasnoselskii–Pokrovskii (KP) model. This integration, which forms the KP-FOBW model, is a novel aspect of our research, offering an enhanced understanding of non-linear and hysteresis behaviors in robotic joints. We employ the Monte Carlo method, particularly focusing on the Stochastic Gradient Hamiltonian Monte Carlo (SGHMC), for precise parameter identification. Our results demonstrate that the FOBW model, especially when combined with the KP model, provides a more accurate representation of the hysteresis curves under varying operating conditions. The KP-FOBW combination emerges as a powerful tool to predict the output torque in robotic joints. This study contributes to a deeper understanding of hysteresis in joints with harmonic drivers, showcasing its potential for complex dynamic modeling in robotics.

Design of integral backstepping controller - sliding mode along with admittance controller of knee joint robot in the presence of noise

Robots are increasingly being used in rehabilitation to assist patients with physical disabilities, particularly the knee joint. Physiotherapists often practice the patient’s leg around the knee joint to strengthen the muscles. However, Continuous Passive Motion (CPM) devices face challenges such as lack of feedback and resistance. This thesis aims to address these issues by using admittance and impedance concepts to control the robot’s flexible behavior against individual foot forces. The research uses a combination of backstepping and admittance algorithms, Model Reference Adaptive Control (MRAC), Sliding-Backstepping and Admittance Control to create a soft interactive patient-robot interaction. The device is designed for both left and right legs.

Robot joint space grid error compensation based on three-dimensional discrete point space circular fitting

The poor absolute positioning accuracy of industrial robots has limited their application in fields such as aerospace manufacturing. To address this issue, the spatial grid compensation method has been proposed as an effective solution. In this paper, we propose a sampling method based on three-dimensional discrete point space circular fitting for grid points to significantly reduce the sampling workload and improve compensation accuracy compared to traditional joint space grid compensation methods. Additionally, we use the Kriging interpolation algorithm instead of the inverse distance weighting (IDW) algorithm for spatial interpolation prediction of pose error. Based on this, the proposed sampling and interpolation prediction method in this paper was verified on a Comau NJ500–2.7 manipulator equipped with a fiber-laying end effector. The experimental results demonstrate that using our proposed sampling method yields pose data of grid points that have only a small deviation from directly sampled results and are only slightly higher than the robot's repeat positioning accuracy. Moreover, our proposed method can significantly reduce the sampling workload by 60% under the experimental conditions of this study (sampling 10 groups of data within 90 degrees range), with increasing sampling range leading to more obvious efficiency improvements. Finally, we show that compared to the IDW algorithm, the Kriging interpolation algorithm yields better results and improves the mean absolute positioning accuracy of robots after compensation by 30%.

Continuous Gesture Control of a Robot Arm: Performance Is Robust to a Variety of Hand-to-Robot Maps.

Despite advances in human-machine-interface design, we lack the ability to give people precise and fast control over high degree of freedom (DOF) systems, like robotic limbs. Attempts to improve control often focus on the static map that links user input to device commands; hypothesizing that the user's skill acquisition can be improved by finding an intuitive map. Here we investigate what map features affect skill acquisition. Each of our 36 participants used one of three maps that translated their 19-dimensional finger movement into the 5 robot joints and used the robot to pick up and move objects. The maps were each constructed to maximize a different control principle to reveal what features are most critical for user performance. 1) Principal Components Analysis to maximize the linear capture of finger variance, 2) our novel Egalitarian Principal Components Analysis to maximize the equality of variance captured by each component and 3) a Nonlinear Autoencoder to achieve both high variance capture and less biased variance allocation across latent dimensions Results: Despite large differences in the mapping structures there were no significant differences in group performance. Participants' natural aptitude had a far greater effect on performance than the map. Robot-user interfaces are becoming increasingly common and require new designs to make them easier to operate. Here we show that optimizing the map may not be the appropriate target to improve operator skill. Therefore, further efforts should focus on other aspects of the robot-user-interface such as feedback or learning environment.

Low-frequency chatter suppression in robotic milling using Magnetorheological Joint Damper (MRJD)

Low-frequency structural vibrations caused by poor rigidity are one of the main obstacles limiting the machining efficiency of robotic milling. Existing vibration suppression strategies primarily focus on passive vibration absorption at the robotic end and feedback control at the joint motor. Although these strategies have a certain vibration suppression effect, the limitations of robotic flexibility and the extremely limited applicable speed range remain to be overcome. In this study, a Magnetorheological Joint Damper (MRJD) is developed. The joint-mounted feature ensures machining flexibility of the robot, and the millisecond response time of the Magnetorheological Fluid (MRF) ensures a large effective spindle speed range. More importantly, the evolution law of the damping performance of MRJD was revealed based on a low-frequency chatter mechanism, which guarantees the application of MRJD in robotic milling machining. To analyze the influence of the robotic joint angle on the suppression effect of the MRJD, the joint braking coefficient and end braking coefficient were proposed. Parallel coordinate plots were used to visualize the joint range with the optimal vibration suppression effect. Finally, a combination of different postures and cutting parameters was used to verify the vibration suppression effect and feasibility of the joint angle optimization. The experimental results show that the MRJD, which directly improves the joint vibration resistance, can effectively suppress the low-frequency vibration of robotic milling under a variety of cutting conditions.