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Robust amplitude-limited interval type-3 neuro-fuzzy controller for robot manipulators with prescribed performance by output feedback

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Abstract

This paper proposes a new observer-based bounded adaptive fuzzy controller for robotic manipulators with a prescribed performance subjected to uncertainties. To this end, interval type-3 fuzzy logic systems are introduced, and the system uncertainties are roughly modeled by an interval type-3 fuzzy neural network. The proposed controller is designed based on a robust adaptive command-filtered backstepping control scheme. Projection-type adaptive laws and saturation functions are effectively utilized to guarantee that actuator limitations are not violated by calculating the maximum required control signals a priori. The actuator disturbances and external perturbations are compensated by the designed robust control scheme. Furthermore, a high-gain observer is applied to estimate unmeasurable states. To improve the transient performance and to achieve better steady-state tracking errors, the prescribed performance control is suggested. The stability of the suggested closed-loop control system is studied by the Lyapunov theorem, and system signals are proved to be uniformly ultimately bounded. Lastly, simulation results show the potency of the proposed control method.

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Correspondence to Khoshnam Shojaei.

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Elhaki, O., Shojaei, K., Mohammadzadeh, A. et al. Robust amplitude-limited interval type-3 neuro-fuzzy controller for robot manipulators with prescribed performance by output feedback. Neural Comput & Applic 35, 9115–9130 (2023). https://doi.org/10.1007/s00521-022-08174-5

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