Robot position accuracy plays a very important role in advanced industrial applications. This article proposes a new method for enhancing robot position accuracy. In order to increase robot accuracy, the proposed method models and identifies determinable error sources, for instance, geometric errors and joint deflection errors. Because non-geometric error sources such as link compliance, gear backlash, and others are difficult to model correctly and completely, an artificial neural network is used for compensating for the robot position errors, which are caused by these non-geometric error sources. The proposed method is used for experimental calibration of an industrial Hyundai HH800 robot designed for carrying heavy loads. The robot position accuracy after calibration demonstrates the effectiveness and correctness of the method.

This paper proposes a new calibration method for enhancing robot positional accuracy of the industrial manipulators. By combining the joint deflection model with the conventional kinematic model of a manipulator, the geometric errors and joint deflection errors can be considered together to increase its positional accuracy. Then, a neural network is designed to additionally compensate the unmodeled errors, specially, non-geometric errors. The teaching-learning-based optimization method is employed to optimize weights and bias of the neural network. In order to demonstrate the effectiveness of the proposed method, real experimental studies are carried out on HH 800 manipulator. The enhanced position accuracy of the manipulator after the calibration confirms the feasibility and more positional accuracy over the other calibration methods.

In this study, a fault-tolerant control (FTC) tactic using a sliding mode controller–observer method for uncertain and faulty robotic manipulators is proposed. First, a finite-time disturbance observer (DO) is proposed based on the sliding mode observer to approximate the lumped uncertainties and faults (LUaF). The observer offers high precision, quick convergence, low chattering, and finite-time convergence estimating information. Then, the estimated signal is employed to construct an adaptive non-singular fast terminal sliding mode control law, in which an adaptive law is employed to approximate the switching gain. This estimation helps the controller automatically adapt to the LUaF. Consequently, the combination of the proposed controller–observer approach delivers better qualities such as increased position tracking accuracy, reducing chattering effect, providing finite-time convergence, and robustness against the effect of the LUaF. The Lyapunov theory is employed to illustrate the robotic system’s stability and finite-time convergence. Finally, simulations using a 2-DOF serial robotic manipulator verify the efficacy of the proposed method.

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