FAT-based robust adaptive controller design for electrically direct-driven robots using Phillips q-Bernstein operators

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.

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“…Industrial robots based on trajectory planning algorithms and kinematics account for more than 20% of the high-end electronics market and are mainly used for welding, processing, assembly, sorting, and cleaning of electronics. 6 The application levels of various trajectory planning algorithms in industrial robots at home and abroad are shown in Figure 7.…”

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“…23,24 The considerable point is that, despite the universal approximation feature of the neural networks/fuzzy systems to approximate nonlinear functions; they experience restrictions, turning their construction and design into a complex issue. 25,26 In approximators based on fuzzy logic, several parameters such as numerical ranges/types of membership functions, the inference system, fuzzification/defuzzification approaches, and the rule database must be selected appropriately to accomplish the design goals. On the other hand, in approximators based on neural networks, features such as the number of neurons/hidden layers, the network architecture, and the activation functions' type should be properly chosen.…”

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