Sliding mode based fractional-order iterative learning control for a nonlinear robot manipulator with bounded disturbance

Abstract

In this paper a new type of sliding mode based fractional-order iterative learning control (ILC) is proposed for nonlinear systems in the presence of uncertainties. For the first time, a sliding mode controller is combined with fractional-order ILC. This sliding mode based [Formula: see text] and [Formula: see text]-type ILC is applied on a nonlinear robot manipulator. Convergence of the proposed method is investigated when the stability is also proved. In this method, the control signal at any iteration is generated in two parts. The first section comes from the sliding mode controller while the second part is output of the fractional-order ILC. The latter signal is assessed using its previous amount and the sliding mode error signal. The achieved control law is capable of controlling nonlinear iterative processes, perturbed by bounded disturbances with high accuracy. The same frequent disturbance is eliminated by the iterative learning part, while the effect of nonrepetitive uncertainty is improved by the sliding mode part. The sliding mode based [Formula: see text]-type ILC (as an adaptive control law) is proposed to control a single-link arm robot. The controller is then improved to sliding mode based [Formula: see text]-type ILC. The effectiveness of the proposed method is again investigated on a single-link robot manipulator through a simulation approach. It is shown that the controller for [Formula: see text] provides performance by means of faster response together with more accuracy with respect to a conventional ILC.

Adaptive fractional‐order control of power system frequency in the presence of wind turbine

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Control of a class of fractional-order systems with mismatched disturbances via fractional-order sliding mode controller

“…The idea of using fractional-order controllers for the control of dynamical systems belongs to Oustaloup. Oustaloup presented the Commande Robuste d’Ordre Non Entier (CRONE) controller (Ghasemi et al, 2016; Oustaloup, 1988).…”

Enhanced data-driven optimal iterative learning control for nonlinear non-affine discrete-time systems with iterative sliding-mode surface

“…Therefore, a sliding model control-based ILC method has been proposed in Chen et al (2012), Ding and Yang (2014) and Ghasemi et al (2016), where the ILC is used to handle the structured uncertainties and the SMC is applied to handle the unstructured uncertainties. However, all these sliding mode surfaces (Chen et al, 2012; Ding and Yang, 2014; Ghasemi et al, 2016) are designed in the time domain and cannot reflect the dynamic behavior of the system in iteration domain. Actually, the iteratively varying factors are inevitable in the sliding model surfaces due to the non-repetitive uncertainties.…”

“…Therefore, an iteration-time-varying sliding model surface will be more suitable for a repeatable control system in addressing non-repetitive uncertainties. Further, the existing sliding model control-based ILC methods (Chen et al, 2012; Ding and Yang, 2014; Ghasemi et al, 2016) are mainly focused on nonlinear systems, which are affined to the control input and the controller design relies on the affine model structure. Up to now, the extension of SMC-based ILC to the nonaffine nonlinear systems is still challenging because the affine model structure is unknown.…”