Collaborative robots often exhibit limited absolute accuracy despite high repeatability, necessitating cost-effective calibration solutions. This paper presents a novel sensor-free self-calibration method for collaborative robots using position and distance constraints. A tri-sphere end-effector with precision balls and magnetic holders enables repeatable Tool Center Point (TCP) positioning (<0.01mm) through hand-guiding, where the three-sphere configuration crucially enhances the orientation calibration accuracy compared to a single-sphere approach. The proposed device eliminates expensive external sensors while establishing geometric constraints through workspace-wide TCP engagements. By analyzing relative position/distance errors between multiple configurations, the method identifies kinematic parameters via a Local Product of Exponential (Local POE) based error model. Experiments demonstrated a 91.7% position error reduction (7.98mm to 0.66mm) and 69.6% orientation improvement (0.0069rad to 0.0021rad), achieving comparable accuracy to laser-tracker methods at <1% device cost. This approach offers a low-cost, mechanically robust solution for enhancing collaborative robot accuracy in industrial applications.

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.

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.

Industrial robots and collaborative robots are widely employed in industry and are progressively being utilized to assist individuals in their daily routines. To improve their absolute accuracy, self-calibration methods using portable local measurement devices are cost-effective solutions. However, compared with the conventional external calibration methods, self-calibration methods employing two configurations as a calibration sample introduce more non-kinematic errors to the robot. Therefore, noise reduction is significantly necessary in self-calibration. A novel Piecewise-weighted Random Sample Consensus (RANSAC) method is proposed in this paper. Instead of choosing an optimal model with all inliers, the proposed method employs a general weight considering both the sample and hypothesis model qualities to generate a new model with Weighted Least Square (WLS) method. Besides, the proposed method turns the target of finding an uncontaminated set of inliers into the training of the proper weight coefficient for WLS, which not only improves the accuracy but also greatly enhances the speed. The self-calibration experiment on a 6 degree-of-freedom(DOF) robot CR10 shows that the accuracy of the proposed Piecewise-weighted RANSAC method makes a 27.7% accuracy improvement from that employing Least Square method, a 20.0% accuracy improvement from that employing standard RANSAC method, and a 5.5% accuracy improvement from that employing LO-RANSAC method. Besides, the proposed method is also over 10.9 times faster than the standard RANSAC method and 18.6 times faster than the LO-RANSAC 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.

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.

Cluster unmanned aerial vehicles (UAVs) are widely demanded. To achieve high rate and large capacity communication with cluster UAVs, one of the key but still challenge task is to perform multitarget acquisition, pointing, and tracking (APT) under complex airborne environment. To solve this problem, this paper gives one composite axis APT system for multitarget laser communication with cluster UAVs. The APT system consists of a gimbal mirror and piezoelectric fast steering mirrors (FSMs), which can achieve independent and synchronous control of multiple optical axes. The cascade control scheme is designed for the gimbal mirror and the H∞ controller is designed for the FSM. To further improve the control accuracy, the hysteresis compensator based on least square support vector machines (LS-SVM) is proposed. The pruning error minimization method of LS-SVM is applied to reduce the computational cost. The simulation studies validate the control performance of the multitarget APT system. Finally, the experimental prototype is developed. The experimental results further validate the effectiveness of the proposed multitarget APT system.

The dual-drive H-gantry is widely used for high-speed, high-precision Cartesian motion. Compared with the conventional rigid-linked design, the flexure-linked counterpart is able to prevent the damage of joints for its smaller interaxial coupling force. However, there are still barriers to further push up its precision, such as parametric uncertainties due to the inaccurate dynamical model, the possible induced vibration during high-speed motion, and the decentralized control structure required by industries. To maintain the tracking precision of carriages and minimize the vibration of the end effector, we aim to optimize parameters in decentralized controllers with choices of flexure pieces. We find that such decentralized feedback structure yields some uncontrollable but stabilizable states in the closed-loop system, and no direct solution from solving the algebraic Riccati equation is available in this case. Such structural constraint, together with constraints due to stability requirement and model uncertainties facilitates us to formulate an H2guaranteed cost control problem within a projected convex domain. From here, efficient numerical procedures are developed to obtain the global optimum by iterative linear programming. The real-time experiment validates the optimality and the robustness of the proposed method.

Base frame calibration is the foundation for the cooperative work of manipulators. The commonly applied method usually constructed a matrix equation. The contact-mode approach is limited by the insufficient accuracy and efficiency while the high cost impedes the application of methods with the noncontact mode. In this paper, a projection-based method is proposed. All the transformation parameters can be solved based on the geometric constraints. When cooperative manipulators form the closed-chain asynchronously, the topological structure is projected into a particular plane. The rotation angle and translation parameters can be determined with utilization of trigonometric functions and a simple calculation process. The greatest novel feature of this analysis is that only two calibration points are required. The experiment result shows that both the accuracy and efficiency can be much better in the asynchronous calibration.

The disturbance observer (DOB) is widely used in high precision motion control applications as an effective means of rejecting disturbances. DOB design essentially boils down to the design of the Q filter. In traditional DOB design, the Q filter follows a standard form such as the Butterworth or binomial, where the bandwidth is the only tunable parameter. In this paper, we employ a more general form of Q filter, and tune the parameters iteratively using a data-based approach according to some design criterion. Simulation results show that the data-based design achieve improved disturbance rejection in low frequency region while the high frequency noise attenuation and robust stability is not compromised.

Linear direct feed drives are widely used in machine tools, but an abrupt counter force from the secondary part will induce the jerk to the metro frame contacted with the linear motor and cause the vibration of auxiliary devices on it. The jerk-decoupling cartridge (JDC) provides a buffer to reduce such an impact. To systematically take care of both the tracking error and the jerk induced to the metro frame, this paper presents an integrated design approach to determine parameters in the JDC and the position controller of the feed drive. The initial formulated non-convex optimization problem is converted to convex constrained gradient optimization problem and linear step searching problem. Thus, fast convergence of parameters is achieved within first few iterations. Through a series of simulation, the effectiveness of proposed methodology is verified.

In this paper, an automatic office-based ear surgical device for the treatment of Otitis Media with Effusion (OME) which overcomes the disadvantages of the conventional surgical treatment is introduced. Since the device carries out the surgery automatically under the guidance of force sensing information, this information must be reliable so that the safety of the device can be ensured. Hence, a force detection method based on the disturbance observer is proposed in this paper. To overcome the disadvantages of the conventional disturbance observer, an advanced disturbance observer is designed and its stability is analyzed. Finally, the simulation study on the disturbance observer is carried out. The results show that the advanced disturbance observer can estimate the disturbance correctly and precisely.

Otitis media with effusion (OME), a worldwide common ear disease, affecting adults and children when the middle ear is infected. Grommet insertion treatment leads to the development and design of a medical device allowing fast and automatic grommet insertion in an earlier work. In the medical device design, a precision motion system is used to provide the accurate movements for the precise grommet insertion procedure. Besides that, a fiberscope is used to provide the vision for the device. Medical imaging is a technique or process to create visual representations of body for clinical analysis and medical intervention. This paper addresses the design consideration and evaluation of a built-in visual analysis platform equipped on the eardrum microsurgery device. In this special configuration, a fiberscope is placed within the tip of a cutting tool to facilitate image analysis of the motorised surgery with high precision movement. Images from this fiberscope are analysed to build important features during the operation such as proximity estimation, angle measurement and touch detection. These enhanced features are hardly achieved in the traditional approach where a common microscope is used. The platform is implemented under a computerised system and evaluated through experimental results.

Jerk derivative feedforward control is proposed and used in recent literatures. However, such feedforward control scheme is restricted to the minimum-phase system which has constrained static gains in the flexible modes. This paper presents a novel form of feedforward controller for a wider class of flexible motion systems, even to be non-minimum phase, as long as the summation of DC gains of flexible modes is positive. The newly proposed feedforward scheme gives superior performance over existing feedforward schemes. Especially, the tracking error can be almost eliminated, when the plant's lowest frequency anti-resonance is outside the main frequency band of the reference trajectory. In other cases, this novel controller still gives adequate tracking performance, while other feedforward controllers are malfunctioned.

Valve Stiction is a common nonlinear phenomenon in pneumatic control valves and it causes oscillations in the control loops. A model of valve stiction that is easy to implement and accurate is desired for analysis of this phenomenon. Compared with the physical model, the data-driven model does not require excess knowledge on various physical parameters, thus it is widely used in modeling and diagnosis of valve stiction behavior. In this paper, modifications are made to the Two-layer binary tree data-driven model to overcome its shortcomings on handling instantaneous input command on reverse motion. It has simpler logic structure compared with recent proposed XCH model. Accuracy of the revised binary tree model is then tested and validated by ISA control valve standard test.

The scanning accuracy of piezoelectric mechanisms over broadband frequencies is limited due to inherent dynamic hysteresis. This phenomenon has been a key bottleneck to the use of piezoelectric mechanisms in fast and precision scanning applications. This paper presents a systematic model identification and composite control strategy without hysteresis measurement for such applications. First, least squares estimation using harmonic signals is applied to achieve the Preisach density function. Next, the hysteresis output is estimated, such that the non-hysteretic dynamics can be identified. The discrete composite control strategy is proposed with a feedforward-feedback structure. The feedforward controller is the primary component designed for the performance. The secondary proportional-integral (PI) feedback controller is employed to suppress disturbances for robustness. Finally, the identification and composite control strategy is implemented with a dSPACE 1104 board for a real piezoelectric actuator setup. The experimental results indicate that adequate scanning performance can be sustained at a rate higher than the first resonant frequency.

Nonlinearities including friction and force ripple, exist in the permanent magnet linear motors, which limit control precision. In this paper, the dynamics model of the linear motor under nonlinear effects are first given. Parameter estimation via limit cycle experiments using the dual-relay feedback apparatus are proposed. Guidelines for choosing appropriate relay gains to tune the limit cycle amplitudes and frequencies within the feedback system with friction and force ripple are also provided. All the results are verified by simulation.