FAT-based robust adaptive controller design for electrically direct-driven robots using Phillips q-Bernstein operators. Issue 10 (15th October 2022)

• Record Type: Journal Article
• Authors: Izadbakhsh, Alireza; Kalat, Ali Akbarzadeh; Nikdel, Nazila
• Is Part Of: Robotica. Volume 40:Issue 10(2022)
• Page Start: 3415
• Page End: 3434
• Publication Date: 2022-10-15
• Subjects: function approximation technique -- industrial manipulator -- uncertainties -- Philips q-Bernstein operator -- adaptive control -- Chebyshev neural network; Robots -- Periodicals; 629.89205
• DOI: 10.1017/S0263574722000303
• Languages: English
• ISSNs: 0263-5747

Abstract

Abstract: This article proposes a robust and adaptive controller for industrial robot arms with multiple degrees of freedom without the need for velocity measurement. Many of the controllers designed for manipulators are model-based and require detailed knowledge of the system model. In contrast to these methods, this paper proposes a model-free controller using the Philips q-Bernstein operator as universal approximator. The designed controller can approximate uncertainties including external disturbances and unmodeled dynamics based on its universal approximation capability. Besides, most of the controllers revealed for robot arms are torque-based, which is not a realistic presumption from a practical point of view. In the proposed control method, the voltage applied to the actuator is considered as the control signal. However, unlike many voltage-based methods, the need to know the exact models of the system and the actuator has been eliminated in the presented method. Also, adaptive rules are extracted during the Lyapunov analysis to ensure system stability. Finally, to analyze the performance of the presented controller, this method is simulated for an industrial robot arm, and the results are analyzed. The proposed methodology is also compared to those of a strong state-of-the-art approximator, the Chebyshev neural network.