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Our study presents a novel design for a cable-driven robotic arm, emphasizing low cost, low inertia movement, and long-term cable durability. The robotic arm shares similar specifications with the UR5 robotic arm, featuring a total of six degrees of freedom (DOF) distributed in a 1:1:1:3 ratio at the arm base, shoulder, elbow, and wrist, respectively. The three DOF at the wrist joints are driven by a cable system, with heavy motors relocated from the end-effector to the shoulder base. This repositioning results in a lighter cable-actuated wrist (weighing 0.8 kg), which enhances safety during human interaction and reduces the torque requirements for the elbow and shoulder motors. Consequently, the overall cost and weight of the robotic arm are reduced, achieving a payload-to-body weight ratio of 5:8.4 kg. To ensure good positional repeatability, the shoulder and elbow joints, which influence longer moment arms, are designed with a direct-drive structure. To evaluate the design’s performance, tests were conducted on loading capability, cable durability, position repeatability, and manipulation. The tests demonstrated that the arm could manipulate a 5 kg payload with a positional repeatability error of less than 0.1 mm. Additionally, a novel cable tightener design was introduced, which served dual functions: conveniently tightening the cable and reducing the high-stress concentration near the cable locking end to minimize cable loosening. When subjected to an initial cable tension of 100 kg, this design retained approximately 80% of the load after 10 years at a room temperature of 24 °C.

This paper proposes a data-driven method-based fault diagnosis method using the deep convolutional neural network (DCNN). The DCNN is used to deal with sensor and actuator faults of robot joints, such as gain error, offset error, and malfunction for both sensors and actuators, and different fault types are diagnosed using the trained neural network. In order to achieve the above goal, the fused data of sensors and actuators are used, where both types of fault are described in one formulation. Then, the deep convolutional neural network is applied to learn characteristic features from the merged data to try to find discriminative information for each kind of fault. After that, the fully connected layer does prediction work based on learned features. In order to verify the effectiveness of the proposed deep convolutional neural network model, different fault diagnosis methods including support vector machine (SVM), artificial neural network (ANN), conventional neural network (CNN) using the LeNet-5 method, and long-term memory network (LTMN) are investigated and compared with DCNN method. The results show that the DCNN fault diagnosis method can realize high fault recognition accuracy while needing less model training time.

In response to the high torque density performance requirements for the hip joints of quadruped robots, an analytical design method for permanent magnet synchronous motors with the goal of increasing motor torque density is proposed in this study. A high torque density motor for quadruped robot hip joints is designed using this method. Finite element simulation is employed to model the motor, and a proof-of-concept prototype is designed based on the proposed analytical design method. Simulation and experimental data indicate that the designed motor's torque density meets the practical application requirements for quadruped robot hip joints, validating the accuracy and effectiveness of the analytical design method presented in this study. 针对四足机器人髋关节对驱动电机高转矩密度的性能需求, 提出一种以提高电机转矩密度为目标的永磁同步电机设计方法。设计出一款四足机器人髋关节用高转矩密度电机, 并通过有限元仿真对电机进行建模, 基于所提解析设计方法设计了原理样机。仿真与实验数据表明, 所设计电机的扭矩密度能够满足四足机器人髋关节的实际应用需求, 验证了所提解析设计方法的准确性和有效性。

In this paper, a fixed-time prescribed performance fault-tolerant control scheme is presented for the n-link flexible joint robot with actuator failures. Firstly, a modified prescribed performance control method is proposed to enhance the robustness of the system against input perturbations and to ensure that the tracking error converges in a predetermined time, and the constrained system is transferred into an unconstrained system. Secondly, an adaptive-based passive fault-tolerant controller is constructed to counteract the actuator failures in the system. Then, the uncertainty problem in the flexible-joint robot system is solved by incorporating the radial basis function neural networks and adaptive techniques into the fixed-time backstepping framework. After that, the “complexity explosion” issue is well handled by creating the fixed-time second-order filter, in which the filtering errors are eliminated by the devised compensation mechanism. The stability analysis proves that the closed-loop system is fixed-time stable, and the tracking error is limited to the predefined range. Finally, simulations have been performed on a two-link FJR and a three-link flexible joint robot, respectively. Via the three conditions: actuators operating normally, actuators losing 50% of effectiveness instantaneously, and actuators losing 50% of effectiveness gradually, the results show that the tracking error of each joint of the system is less than 0.2 rad, and the tracking time is limited to the specified time (0.8 s), which proves the effectiveness of the proposed control scheme.

This paper contributes by presenting a parameter identification procedure for n-degrees-of-freedom flexible joint robot manipulators. An advantage of the given procedure is the obtaining of robot parameters in a single experiment. Guidelines are provided for the computing of the joint position filtering and velocity estimation. The method relies in the filtered robot model, for which no acceleration measurements are required. The filtered model is expressed in regressor form, which allows applying a parameter identification procedure based on the least squares algorithm. In order to assess the performance of the proposed parameter identification scheme, an implementation of a least squares with forgetting factor (LSFF) parameter identification method is carried out. In order to assess the reliability of the tested identification schemes, a model-based trajectory tracking controller has been implemented twice in different conditions: one control experiment using the estimated parameters provided by the proposed scheme, and another experiment using the parameters given by the LSFF method. These real-time control experiments are compared with respect to numerical simulations using the estimated parameters for each identification method. For the proposed scheme, the comparison between experiments and numerical simulations indicates better accuracy in the torque and position prediction.

The control of an aerial flexible joint robot (FJR) manipulator system with underactuation is a difficult task due to unavoidable factors, including, coupling, underactuation, nonlinearities, unmodeled uncertainties, and unpredictable external disturbances. To mitigate those issues, a new robust fixed-time sliding mode control (FxTSMC) is proposed by using a fixed-time sliding mode observer (FxTSMO) for the trajectory tracking problem of the FJR attached to the drones system. First, the underactuated FJR is comprehensively modeled and converted to a canonical model by employing two state transformations for ease of the control design. Then, based on the availability of the measured states, a cascaded FxTSMO (CFxTSMO) is constructed to estimate the unmeasurable variables and lumped disturbances simultaneously in fixed-time, and to effectively reduce the estimation noise. Finally, the FxTSMC scheme for a high-order underactuated FJR system is designed to guarantee that the system tracking error approaches to zero within a fixed-time that is independent of the initial conditions. The fixed-time stability of the closed-loop system of the FJR dynamics is mathematically proven by the Lyapunov theorem. Simulation investigations and hardware tests are performed to demonstrate the efficiency of the proposed controller scheme. Furthermore, the control technique developed in this research could be implemented to the various underactuated mechanical systems (UMSs), like drones, in a promising way.

This study presents a dual-channel disturbance rejection controller for achieving high-precision position control of flexible-joint robots (FJRs) with prescribed performance constraint. The dynamics of the FJR are decomposed into a quasi-steady-state (QSS) model and a boundary-layer model via singular perturbation theory. To effectively address unknown disturbances in the QSS model, a dual-channel disturbance compensator is proposed, which contains a high-frequency disturbance compensator (HFDC) and a generalized proportional integral observer (GPIO). The dual-channel disturbance compensator enables separate estimation of high-frequency and low-frequency components of the lumped disturbance by HFDC and GPIO, respectively. Additionally, the controller for the QSS model is constructed by combining the barrier Lyapunov function and the sliding mode surface, ensuring the exponential stability of the QSS model. The practical exponential stability of the entire system is proven utilizing the extended Tikhonov’s theorem. Numerical simulation and comparative experiments are performed to validate the high-precision tracking performance achieved by the designed approach. To the best of our knowledge, this is the first approach to effectively distinguish and estimate distinct components of the lumped disturbance to achieve precise control of FJRs, particularly in the presence of prescribed performance constraint.

The problem of finite-time tracking control for n-link flexible-joint robot manipulators is addressed. An adaptive fuzzy finite-time command-filtered backstepping control scheme is presented to solve the following problems: “explosion of terms” problem, finite-time stabilization of the closed-loop system, and the reduction of computational cost. To this end, new virtual adaptive control signals and new finite-time error compensation mechanism are constructed using inherent properties of robot manipulator systems. Based on the Lyapunov theory, the finite-time stabilization of the closed-loop system is proved. Simulation studies show the effectiveness of the proposed method.

Passivity-based controllers are widely used to facilitate physical interaction between humans and elastic joint robots, as they enhance the stability of the interaction system. However, the joint position tracking performance can be limited by the structures of these controllers when the system is faced with uncertainties and rough high-order system state measurements (such as joint accelerations and jerks). This study presents a variable structure passivity (VSP) control method for joint position tracking of elastic joint robots, which combines the advantages of passive control and variable structure control. This method ensures the tracking error converges in a finite time, even when the system faces uncertainties. The method also preserves the passivity of the system. Moreover, a cascaded observer, called CHOSSO, is also proposed to accurately estimate high-order system states, relying only on position and velocity signals. This observer allows independent implementation of disturbance compensation in the acceleration and jerk estimation channels. In particular, the observer has an enhanced ability to handle fast time-varying disturbances in physical human-robot interaction. The effectiveness of the proposed method is verified through simulations and experiments on a lower limb rehabilitation robot equipped with elastic joints.

The torsional stiffness of rehabilitation robot joints is a critical performance determinant, significantly affecting motion accuracy, stability, and user comfort. This paper introduces an innovative traction drive mechanism that transmits torque through friction forces, overcoming mechanical impact issues of traditional gear transmissions, though accurately modeling surface roughness effects remains challenging. Based on fractal theory, this study presents a comprehensive torsional stiffness analysis for advanced traction drive joints. Surface topography is characterized using the Weierstrass–Mandelbrot function, and a contact mechanics model accounting for elastic–plastic deformation of micro-asperities is developed to derive the tangential stiffness of individual contact pairs. Static force analysis determines load distribution, and overall joint torsional stiffness is calculated through the integration of individual contact contributions. Parametric analyses reveal that contact stiffness increases with normal load, contact length, and radius, while decreasing with the tangential load and roughness parameter. Stiffness exhibits a non-monotonic relationship with fractal dimension, reaching a maximum at intermediate values. Overall system stiffness demonstrates similar parameter dependencies, with a slight decrease under increasing output load when sufficient preload is applied. This fractal-based model enables more accurate stiffness prediction and offers valuable theoretical guidance for design optimization and performance improvement in rehabilitation robot joints.