The measurement of dynamic forces within spacecraft sources is critical for safeguarding mission accuracy and stable operation. Two major challenges impede accurate measurement of dynamic forces from large sources: the severe nonlinearity of platforms; leading to inaccuracies in traditional methods; and the explosion of redundant sensor combinations, lowering optimization efficiency. To address these issues, this article proposes an intelligent framework that synergistically integrates the genetic algorithm with adaptive-learning-rate-based back propagation (GAARBP) and Bayesian optimization (BO). The GAARBP module addresses the nonlinear input-output mapping problem of the platform, and enhances the efficiency and accuracy of model training through adaptive learning rate adjustment. The BO module employs a blockwise Matérn kernel function tailored for sensors on horizontal and vertical planes to construct a Gaussian process surrogate model, alongside an expected improvement-based acquisition function for efficient optimization. These two modules operate in a closed loop, wherein GAARBP provides accurate fitting errors to guide BO’s selection, and BO feeds back optimal sensors to enhance GAARBP’s model training. Experimental results show that the framework achieves higher measurement accuracy (error: 2.28%) and superior decoupling performance (error: 1.13%), sufficient to satisfy the measurement requirements of large vibration sources in spacecraft. Moreover, compared with conventional methods (exhaustive search, D-optimization, particle swarm optimization, etc.), the proposed framework selects the best sensor combination more effectively for the optimization problem of six sensors selected from 24, with outcomes that are virtually globally optimal. This article offers a novel, effective approach to synergistically solving nonlinear dynamic force measurement and sensor optimization in complex environments. It thereby promotes the intelligent advancement of testing technologies in these fields.

This paper proposes an innovative virtual chain-based kinematic calibration for the 4PPa-2PaR parallel manipulators with subchain architectures. Conventional calibration methods for such architectures suffer from inherent limitations due to coupled parameter constraints and restricted solution spaces caused by joint displacement and structural parameter dependencies. The presented methodology introduces three fundamental advancements: (1) a novel parameter assignment strategy enabling independent joint/link parameter definition across different kinematic chains, (2) systematic transformation of constrained optimization into an unconstrained one, and (3) significant expansion of error parameter solution space through virtual chain modeling. Comparative experiment on the physical prototype demonstrate improvements in both orientation and position accuracy compared to existing methods.

Flexible joints are the integral drive and control components for manipulators in interaction applications. Ensuring optimal performance while implementing torque control is crucial to maintaining stability during interactions. However, the system is susceptible to numerous disturbances, such as motor inaccuracies, nonlinearity caused by friction, and hysteresis. These disturbances are distributed across the motor-side, reduction gear, and load-side, leading to limitations in system performance, including response speed and steady-state accuracy. This paper proposes a disturbance compensation method combined with dual torque sensors and an improved disturbance observer (DOB). Two torque sensors are installed on the fixed side and load-side, respectively. These sensors are utilized to measure the load-side disturbances without considering accuracy models. Furthermore, an improved DOB is designed based on one of the torque sensors to estimate the motor-side disturbances. The disturbance compensator and feedback controller are introduced as the control algorithms for the flexible joint to achieve high-precision torque control. The robust stability of the proposed method is analyzed. Finally, several comparative experiments are conducted under various conditions. The results demonstrate that the proposed method enhances torque control performances and backdrivability compared with the traditional methods. Note to Practitioners—Generally, lots of nonlinear disturbances in the flexible joint system are distributed across low-speed/high-torque and high-speed/low-torque ports. These disturbances will lead to limitations in torque control performance. To address the problem above, this paper focuses on integrating dual torque sensors with an improved DOB. Specifically, two torque sensors are installed on the fixed side of the reduction gear and load-side, respectively. This enables the real-time measurement and calculation of load-side disturbances. On the other hand, considering the torque sensor mounted at the fixed side and motor-side model, an improved DOB is designed to estimate the motor-side disturbances. Subsequently, the closed-loop control architecture is designed based on the torque sensor attached to the load. The complex modeling process can be reduced by installing and applying torque sensors. The model and parameter errors are not considered, and the torque control performance is improved. The research outcome of this paper provides an effective approach that can be used to achieve stable and rapid torque tracking, as well as effortless human-robot interaction. The proposed method still has good torque tracking performance in the case of a large load.

For conventional linear-in-parameter (LIP) dynamic model of industrial robots, redundant terms in multivariate polynomials (MVPs) are strongly coupled and more difficult to eliminate than the redundant parameters. In this work, by analyzing the mapping from linearized chain kinematics to Lagrange dynamics, the linear-in-multivariate-polynomial (LI-MVP) model is formed. Subsequently, a bilinearized dynamics is derived in both LI-MVP and LIP formulations, so that redundant MVPs and inertial parameters are concurrently eliminated. In addition, all the MVPs are encoded into a numeric matrix within a monoid, where a binary operation is defined to replace symbolic Kronecker products for efficient derivation of LI-MVP model. Eventually, by rearranging the encoded matrix with respect to total degrees of variate, the symbolic LIP model is directly restored with decoded MVPs in Horner forms, which can further reduce the number of multiplications during torque computation. Simulations and experiments on serial industrial robots in both model derivation and torque calculation demonstrate the effectiveness of proposed methods.

Feature selection is challenging in high-dimensional and small-sample data, particularly in industrial informatics with diverse noise sources. The information entropy of feature noise is included in mutual information of a label and noise-corrupted features, which can be removed to increase classification accuracy. In this article, we propose a robust feature selection method by eliminating feature noise in the relevance measure. Feature noise is modeled as a zero-mean censored normal distribution, so its entropy is determined by solving the variance equation based on the maximum entropy principle. Then, a noisy channel for feature transmission is proposed to extract class-relevant noise component. Furthermore, a noise-free mutual information metric is developed by removing noise entropy within mutual information. Eventually, a novel criterion is proposed by maximizing relevance based on noise-free mutual information while minimizing redundancy. Experimental results confirm the effectiveness of our approach on datasets from various industrial sectors.

It remains a challenge to improve the accuracy of impedance rendering while ensuring stability under strong impacts during human-robot interaction. In this work, we aim to render the desired impedance for the flexible joint under an admittance control scheme with prescribed performance function (PPF). Specially, Gaussian kernels are introduced as the slack terms for PPF, so that the control stability can be maintained in the presence of abrupt external torques. Meanwhile, a narrower error envelope is yielded when such torques are absent, which also improves the fidelity of the desired impedance model. To achieve the prescribed tracking performance of the inner position loop, a two-stage backstepping control is proposed by defining two first-order composite error surfaces bridged by a second-order dynamic surface. This promulgates the minimum number of backstepping stages under the available state feedback, thus avoiding “explosion of terms.” In addition, dual-adaptive neural networks are incorporated into the backstepping control to compensate for the matched and unmatched disturbances. Real-time experiments are conducted to validate the appeal of the proposed method.

The unit dual quaternion (UDQ)-based product-of-exponential (POE) formula has achieved efficient kinematic calibration for serial manipulators. However, due to the presence of unknown passive joint displacements, it is difficult to directly establish explicit forward kinematic models for parallel manipulators (PMs). This forms a barrier to subsequent error modeling and compensation. This work establishes a novel UDQ-based forward kinematic model for a PM by utilizing constraints on the identical pose of the moving platform across all its chains. Furthermore, the adjoint transformation of UDQ's twist is derived for PM's error modeling. Notably, an index matrix is introduced to achieve a unified representation of active or passive joint displacement. Thereby, this UDQ-based kinematic error modeling method is applicable to general PMs. In addition, an error compensation method is proposed for a PM using the UDQ-based local POE formula, which incorporates the developed forward kinematic model to adjust active joint displacements. The proposed method offers significant runtime savings compared to the traditional homogeneous-transformation-matrix-based POE formula due to the compact data structure and reduced arithmetic operations.

This paper proposes an improved stability observer to obtain the interaction state in real-time for physical human-robot-environment interaction (pHREI). Firstly, a stability observer is designed to effectively avoid the misjudgment caused by the phase advance or delay of the filter. Then, the variable admittance control algorithm is designed according to the value of the observer. Finally, a comparative experiment is carried out. The results show that the proposed method can effectively reduce the oscillation when the operator drags the robot into contact with the environment.

U-shaped ironless permanent magnet linear motors (IL-PMLMs) are widely implemented in precision manufacturing equipment with the advantages of small thrust and high positioning accuracy. However, the structural parameter variations of the motor caused by manufacturing and assembly errors will increase the thrust ripple of the motor and thus cause vibration and reduce the operating accuracy. This paper uses Monte Carlo simulation to analyze the manufacturing tolerances of the motor's primary and secondary structural parameters, including the spacing and position of the winding, the height and width of the permanent magnets, and the thickness of the back iron. Finally, various combinations of parameter tolerances are used for analysis and validation. Assuming all tolerances obey normal distribution, the influence of motor structural parameter tolerances on thrust and thrust ripple is obtained through finite element analysis.

Various optimal control and system designs involve searching for a feedback gain matrix with structural constraints. As an alternative solution, the parameter space methods map the constraints from the state space to another extended state-input space, in which an equivalent optimization problem is solved. However, to further extend its applications, there are still some issues need to be addressed, such as the limited type of structural constraints, the marginally stable solutions, and the low computation efficiency. In this article, we aim to make this method applicable to a class of structural constraints for some elements in the gain matrix being zero or with intrarow and intracolumn constraints. To address such structured control problem, we propose a procedure to transform the original system to an extended system with the decentralized feedback matrix. From here, the mapping rules to the parameter space are given for the decentralized feedback matrix with both intrarow and intracolumn constraints. To avoid oscillatory closed-loop dynamics, we include the closed-loop dominant pole constraints during optimization. In addition, to improve the computation efficiency during optimization, we revise the cutting plane logic, which allows adding multiple linear constraints within a single iteration. Simulation examples demonstrate the effectiveness of the proposed method.

Removing the parameter redundancy in the kinematic error model does improve the robustness of parameter identification. However, this does not help much on saving the calculation time on the error compensation process, which is closely related to the data structure used for representing rigid-body motion. Compared with the homogenous transformation matrix (HTM), the unit dual quaternion (UDQ) can describe the rigid-body motion in term of an array with eight entries, thus it is a conducive data structure for efficient kinematic computation. In this article, the exponential and logarithm mappings of an UDQ are defined explicitly so that the relationship between finite and instantaneous motions is set up under the exponential coordinate. Thereafter, by defining the adjoint operator of UDQ to transform the twist under different frames, the linearized kinematic error models are built with the local product-of-exponentials (POE) formula. This facilitates the upcoming parameter identification and error compensation processes. Especially, the counts of elementary operations are evaluated throughout the compensation processes, which shows the number of arithmetic operations are greatly reduced compared with using the POE formula based on the HTM. Additionally, the simple data structure in term of 1-D arrays saves the data dispatch time when performing adjoint operations. These make the developed model suitable for calibration of the articulated robot with a long sequence of trajectories. Experiments on a 6-degree-of-freedom industrial robot validate the effectiveness of the proposed approach.

In a multiwire slicer, it is very crucial to keep wire sending smooth and tension constant to guarantee the quality of silicon wafers. Since the wire sending status has impact on the wire tension, it needs to be controlled to be smooth enough first. The measurement of the wire sending state is based on the two tensometers near the wire sending point and the tension adjusting unit, respectively. The measuring principle is given in detail. The smooth wire sending control is realized through a position adjusting unit. To achieve good control performance, the wire sending state is divided into several sections, and a novel feedforward multiconditioned P controller is designed. After the smooth wire sending controller is designed, the factors related to the wire tension vibration are analyzed deeply, and then, a self-tuning fuzzy controller is proposed. The parameters of the controller can be tuned according to the wire moving velocity. To show the validity of the proposed method, experiments on real platform are well conducted.

The ten papers in this special section aim gather the latest research and development works on design, sensing, and intelligent control of robotics and mechatronics systems resulting from the emerging information sharing and design technologies.

This paper analyzes the influences of displacement sensor noise on the power amplifiers of active magnetic bearing systems in both current and voltage control schemes. An effective evaluation index has been proposed to guarantee that the comparison is meaningful and fair enough to both control schemes. In addition, an appropriate controller is designed for each control scheme, while each controller is capable of dealing with both linear and nonlinear plants. Three significant findings are obtained through a thorough computer simulation. Firstly, using the proposed evaluation index, the maximum displacement response values are almost identical for the two control schemes, when the same unit pulse of external disturbance force signal is inputted; secondly, the current control scheme is more sensible to the noise; and lastly, the power amplifier in voltage control scheme has better noise suppression capability than current control scheme.

This paper studies the dynamic response of a high-speed ball-screw drive in a machine tool feed system. The Timoshenko beam theory is employed to model the rotating screw, while the nut is modeled as a lumped moving mass subjected to an external force resolved into three mutually perpendicular components. The dynamic model of the ball-screw drive is formulated based on the assumed mode method. To study the deformation characteristics of the ball-screw drive with different constrains, the dynamic equations are solved by using the fourth order Runge-Kutta method. In this paper, the moving mass and force effect are taken into consideration synchronously, and the relative velocity relationship of screw and nut is studied as well. The results show that the pitch of the screw, which determines the relative speeds of the rotating beam with respect to the moving excitation, have strong influences on the lateral deformation. Major factors affecting the ball-screw drive deformation are identified to illustrate its vibration performance.