In this paper stability analysis of fractionalorder nonlinear systems is studied. An extension of Lyapunov direct method for fractional-order systems using Bihari's and Bellman-Gronwall's inequality and a proof of comparison theorem for fractional-order systems are proposed.

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.

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This paper develops a novel controller called adaptive iterative learning sliding mode (AILSM) to control linear and nonlinear fractional-order systems. This controller applies a hybrid structure of adaptive and iterative learning control in to sliding mode method. It can switch between both adaptive and iterative learning control in order to use the advantages of both controllers simultaneously and therefore achieve better control performance. This controller is designed in a way to be robust against the external disturbance. It also estimates unknown parameters of fractional-order system. The proposed controller, unlike the conventional iterative learning control, does not need to apply direct control input to output of the system. It is shown that the controller performs well in partial and complete observable conditions. Illustrative examples verify the performance of the proposed control in presence of unknown disturbances and model uncertainties.

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