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In presence of the uncertainties in robot dynamics, model-based control law can easily fail. In our paper, this important question is tackled by using fuzzy adaptive control to drive with obstacle avoidance an industrial redundant manipulator under the hypothesis of uncertain dynamics. This hypothesis makes our controller an original one, since the problem of the tracking trajectory in the presence of obstacles and assuming that the robot dynamics are unknown has not been discussed before in the literature. Besides, the proposed obstacle avoidance is achieved by generating a self-motion. Furthermore, the usual procedure related to the obstacle avoidance based on Euclidean distance in 3D space is made easier since this one includes only the parameters of 2D space. This self-motion is directly incorporated into the adaptive fuzzy control scheme via the filtered tracking errors. The proposed method is tested by simulations in the case of redundant robot (PUMA 560) operating in 3D space in presence of obstacles. Despite that the robot dynamics are assumed to be uncertain, the proposed control performance is satisfactory. Indeed, the trajectory tracking control, incorporating self-motion obstacle avoidance, operates effectively with weak tracking control errors and bounded actuator torques evolving in admissible range.

This paper proposes a novel fuzzy adaptive controller for achieving an appropriate generalized projective synchronization (GPS) of two incommensurate fractional-order chaotic systems. The master system and the slave system, considered here, are assumed to be with non-identical structure, external dynamical disturbances, uncertain models and distinct fractional-orders. The adaptive fuzzy systems are used for estimating some unknown nonlinear functions. A Lyapunov approach is adopted for deriving the parameter adaptation laws and proving the stability of the closed-loop system. Under some mild assumptions, the proposed controller can guarantee all the signals in the closed-loop system remain bounded and the underlying synchronization errors asymptotically converge towards a small of neighborhood of the origin. Finally, some numerical experiment results are presented to illustrate the effectiveness of the proposed synchronization scheme.

This paper presents a new adaptive fuzzy vector controller (AFVC) to handle the torque and speed tracking problem of a doubly-fed induction motor (DFIM) as an alternative to classical PI based vector control method generally used for its simplicity. However, the control performance of DFIM is still influenced by the variations of the parameters, the external load disturbances and perturbations in practical applications. Then, it is difficult to achieve high control performances of DFIM by using conventional PI-type control techniques. The proposed AFVC scheme uses adaptive fuzzy systems to reasonably approximate the uncertain dynamics appearing in the DFIM, relaxing thereby the usual modeling requirement about the DFIM dynamics. Of fundamental interest, it is shown that all the closed-loop signals are bounded and the tracking errors exponentially converge to a residual set. Probing simulation results are given to emphasize the effectiveness of the proposed AFVC system with respect to the usual feedback linearization based vector control (FLVC) system.