There are many interference components in Fourier amplitude spectrum of a contaminated fault signal, and thus the segment obtained based on the spectrum can lead to serious over-decomposition of empirical wavelet transform (EWT). Aiming to resolve the above problems, a novel method named improved empirical wavelet transform (IEWT) is proposed. Because the power spectrum is less sensitive to the contaminated interference and manifests the presence of fault feature information, IEWT replaces the Fourier amplitude spectrum of EWT with power spectrum in segment acquirement, and threshold processing is also introduced to eliminate the bad influence on the acquirement, and thus the best decomposition result of IEWT can be obtained based on feature energy ratio (FER). The loose slipper fault signal of hydraulic pump is tested and verified. The result demonstrates that the proposed method is superior and can extract the fault feature information accurately.

This study proposes a parallel six-axis force/torque sensor decoupling method based on an independent component analysis (ICA), and the experimental platform is built for experimental exploration. First of all, the structural model and mathematical model of the parallel six-axis force/torque sensor are introduced, which is composed of single-dimensional force sensors with central symmetry. Secondly, the test prototype was developed and a test platform was built to perform online static loading test on the sensor, and the test results were analyzed. Finally, the ICA-based dynamic decoupling study of the sensor is carried out, the effectiveness and rationality of the proposed algorithm are proved. The research results of this paper have certain reference values for the further study of the decoupling of parallel six-axis force/torque sensors.

This paper proposed a circular flexible spherical joint to reduce the processing difficulty and manufacturing cost, and design a six-dimensional force sensor based on it. This paper analyzes the influence of the structural parameters of the flexible spherical joints on the accuracy of the six-dimensional force sensor by comparing the force mapping matrix of flexible spherical and ideal spherical joints. As the force mapping matrix is related to the stiffness matrix, the stiffness matrix of a six-dimensional force sensor based on a circular flexible spherical joint and the force mapping matrix of the generalized external force acting on the sensor was derived. Finally, a set of optimal parameters was selected to create a prototype and was verified through finite elements and experiments. The results show that the six-dimensional force sensor with a circular flexible spherical joint has good accuracy and provides some guidance for the design and analysis of the sensor.

As robot haptic provider, six-dimensional force sensors are an indispensable component of industrial intelligence. To address the disadvantages of the existing six-dimensional force sensor, a novel circular flexible spherical joint is proposed, its stiffness model is derived by numerical analysis method and microelement, and the stiffness model of the circular flexible spherical joint is verified by finite element simulation. A parallel six-dimensional force sensor is designed based on a circular flexible spherical joint, and a mathematical model of the sensor was established. Based on the genetic algorithm, the structural optimization design of the circular flexible spherical joint was performed, and the structural parameters of the circular flexible spherical joints are obtained by analyzing isotropy. Finally, according to the optimization results, a sensor was developed and calibration experiments were carried out. The experimental results show that the maximum error of the sensor is 1.85%, which verifies the effectiveness of the mathematical model of the sensor, which has high measurement accuracy and can accurately measure the six-dimensional external force and moment applied to the sensor.

The collision detection algorithm of the robot body previously needed to rely on the surface geometry information of the colliding object and no deformation was allowed during the collision process. To solve this problem, a new robot body collision detection algorithm that uses the force information of the six-axis force/torque sensor at the base to self-constrain is proposed which does not rely on the geometric information of the colliding object surface, and the deformation also allows deformation during the collision. In terms of sensor data preprocessing, a gravity and dynamic force compensation algorithm for the six-axis force/torque sensor at the base is proposed to ensure that the reading of the six-axis force/torque sensor at the base always maintains the value of 0 when the robot is working. Then, the robot is considered to have collided with the outside world when the sensor reading exceeds the set threshold. And a precision factor is proposed to analyze the influence of force and collision distance on the accuracy of the algorithm. Finally, the new algorithm proposed in this paper is compared with the traditional algorithm that relies on the geometric information of the colliding body surface. The experimental results indicate that the accuracy of the collision point detection algorithm proposed in this paper is close to that of the traditional method, but it does not need to rely on the geometric information of the collision body surface, and there is no requirement for whether there is deformation during the contact process. It can be concluded that the collision distance is the most important factor affecting the accuracy of the algorithm, followed by the conclusion of the magnitude of the collision force through the calculation of the precision factor. The results show that this method can effectively detect the collision point of the machine body, and the maximum error at the farthest point of the robot is 8.712%, which lays a certain foundation for the subsequent research on human-machine collaboration in small collaborative robots.

In order to solve the external periodic disturbance and unknown dynamics influence in the passive rehabilitation process of a rope-driven lower limb rehabilitation robot, a control method with periodic repeated learning was designed. In this control method, the closed-loop dynamics are divided into a periodic disturbance term, an unknown dynamics term, and a basic term, and the shape function is designed by using the Stone–Weirstrass theorem. In the process of periodic operation, the estimated value of the shape function coefficient is repeatedly learned to obtain the periodic disturbance term approximation and to realize the compensation in advance. Through the design of the impedance learning rate, the unknown dynamic term is periodically learned, and the unknown dynamics approximation is obtained. By combining the two approximations with the basic terms which can be solved directly, the external periodic disturbance is compensated for in advance and the control precision is improved. The control algorithm was verified by simulation, and the error fluctuation of the system gradually decreases and reaches the ideal value within several cycles. The performance of the control system is stable, and the problem of limb impedance caused by different patients is well solved.