This paper addresses the tracking control problem of uncertain nonlinear strict-feedback systems. Firstly, to enhance the tracking performance of control system, an improved prescribed performance function is constructed, which yields that the convergence accuracy of tracking error can reach a preset factor than that of the traditional prescribed performance function-based control method. Then the fuzzy logic system is used to approximate the unknown nonlinear dynamics on a compact set integrated by an ideal controller, from which an adaptive fuzzy control law is designed by combining the improved prescribed performance function and fuzzy logic system. The stability of closed-loop system and the convergence of tracking error are analyzed in the Lyapunov sense. Finally, simulation and experimental results show the feasibility and effectiveness of the proposed method.

To improve the positioning accuracy of industrial robots and avoid using the coordinates of the end effector, a novel kinematic calibration method based on the distance information is proposed. The kinematic model of an industrial robot is established. The relationship between the moving distance of the end effector and the kinematic parameters is analyzed. Based on the results of the analysis and the kinematic model of the robot, the error model with displacements as the reference is built, which is linearized for the convenience of the following identification. The singular value decomposition (SVD) is used to eliminate the redundant parameters of the error model. To solve the problem that traditional optimization algorithms are easily affected by data noise in high dimension identification, a novel extended Kalman filter (EKF) and regularized particle filter (RPF) hybrid identification method is presented. EKF is used in the preidentification of the linearized error model. With the preidentification results as the initial parameters, RPF is used to identify the kinematic parameters of the linearized error model. Simulations are carried out to validate the effectiveness of the proposed method, which shows that the method can identify the error of the parameters and after compensation the accuracy of the robot is improved.

The positioning accuracy of articulated serial robots in the workpiece coordinate system (WCS) is vital for practical applications, as command positions or planned paths are typically defined in WCS. However, conventional error models for kinematic calibration primarily focus on positioning accuracy in the base coordinate system (BCS), without adequately fulfilling the accuracy requirements of WCS. To enhance the positioning accuracy in WCS, we propose a novel error model based on the vector inner product, the calibration accuracy of which is independent of coordinate systems. The vector inner product error is constructed from positioning error, and a mapping model correlating with kinematic parameter errors is derived. Following the establishment of the model, the kinematic parameters are identified. The results of experiments using a CS612 robot reveal a 36% and 15% improvement in the positioning accuracy within WCS after calibration, over the existing distance error model and position error model. Finally, a method for compensating the positioning error in WCS is presented, and a deburring application case study demonstrates that the proposed model reduces the maximum positioning error of the deburring path from 1.270 to 0.207 mm after compensation, outperforming the distance error model and position error model by 61% and 31%, respectively.

In robotic automation, compact layouts and the limited range of measurement devices often confine the robot’s calibration space, leading to ill-conditioned Jacobian matrices for length parameters and significant estimation errors. To address this issue, joint configurations in the confined calibration space are modeled as perturbations around those under a fixed end-effector position. By applying matrix perturbation theory, the relationship between the smallest singular value of the identification matrix and the joint configuration perturbations is established. Building on this theoretical basis, a novel dual-tool distance constraint method is introduced to alleviate the ill-conditioning of the identification matrix and enhance parameter identification accuracy. The proposed method was validated using an ABB IRB 4600 industrial robot. Experimental results in the confined calibration space demonstrate that, compared with conventional methods, the proposed approach not only mitigates ill-conditioning but also significantly improves the robot’s positioning accuracy.

Insufficient stiffness of industrial robots is a significant factor which affects its positioning accuracy. To improve the positioning accuracy, a novel positioning error compensation method based on the stiffness modelling is proposed in this paper. First, the positioning errors considering the end load and gravity of industrial robots due to stiffness are analyzed. Based on the results of analysis, it is found that the positioning errors can be described by two kinds of deformation errors at joints: the axial deformation error and the radial deformation error. Then, the axial deformation error is modelled by the differential relationship of kinematics equations. The model of radial deformation error is deduced through the recurrence method and rotation transformation between joints. Finally, these two models are transformed into a Cartesian coordinate system, and a positioning error compensation method based on these two models is presented. Simulations based on the finite element analysis are implemented to verify the positioning error compensation method. The results show that the suggested method can efficiently predict the positioning error according to the gravity and loads, so that the positioning accuracy of industrial robots can be improved with the proposed method.

To improve the positioning accuracy of collaborative robots, a novel modeling and calibration method for collaborative robots is proposed based on position information and modified local product of exponentials (LPoE). The kinematic error model is derived from the kinematic model through differential transformation. To solve the problem of the high redundancy and complexity of the error model that is difficult to identify, the errors of the kinematic parameters are all transferred to the initial position and posture matrix of the local coordinate system, which simplifies the error model and improves the speed and accuracy of the identification calculation. However, the simplified error model still requires posture data which are very difficult to acquire in practice. For this reason, the position error is separated from the kinematic model, and an error model based on position data is established. Then, a kinematic calibration method of the collaborative robot based on position data is proposed, which simplifies the measurement process and improves the efficiency of calibration. The effectiveness of the method is verified by simulations and experiments on a six degree-of-freedom collaborative robot.