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This paper presents a novel parameter adjustment scheme to improve the robustness of fuzzy sliding-mode control achieved by the use of an adaptive neuro-fuzzy inference system (ANFIS) architecture. The proposed scheme utilizes fractional-order integration in the parameter tuning stage. The controller parameters are tuned such that the system under control is driven toward the sliding regime in the traditional sense. After a comparison with the classical integer-order counterpart, it is seen that the control system with the proposed adaptation scheme displays better tracking performance, and a very high degree of robustness and insensitivity to disturbances are observed. The claims are justified through some simulations utilizing the dynamic model of a 2-DOF direct-drive robot arm. Overall, the contribution of this paper is to demonstrate that the response of the system under control is significantly better for the fractional-order integration exploited in the parameter adaptation stage than that for the classical integer-order integration.

In this work, two modifications on Levenberg-Marquardt algorithm for feedforward neural networks are studied. One modification is made on performance index, while the other one is on calculating gradient information. The modified algorithm gives a better convergence rate compared to the standard Levenberg-Marquard (LM) method and is less computationally intensive and requires less memory. The performance of the algorithm has been checked on several example problems.

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Control of aerial robots is a popular research field as applications with different payloads lead to a variety of flight missions. Quadrotor-type unmanned systems are one such example considered in this paper. The performance in any flight experiment depends strictly on the chosen feedback control scheme, which is the core issue addressed in the paper. A number of approaches have been reported in the literature and this paper presents a survey of these schemes with an in-depth discussion of recent research outcomes. A detailed performance evaluation of the controllers, namely proportional-integral-derivative control, sliding mode control, backstepping control, feedback linearization-based control and fuzzy control schemes, are presented. Due to the popularity of the quadrotor-type aerial vehicles, the contribution of the current work is to provide an in-depth guide to the autopilot designers of quadrotor-type unmanned aerial vehicles.

We consider integral sliding mode control with a fractional order reaching law in this paper. The reaching law approach is followed and stability of the sliding manifold is shown graphically. We choose a quadrotor-type unmanned aerial vehicle (UAV) to validate the design. The attitude dynamics of the vehicle is controlled by the proposed scheme and it is seen that the proposed form of the control system gives much better results compared with its integer order counterpart. The contribution of the study is to report a highly robust control scheme utilizing the fractional order differintegration operators.

This work considers the fractional order control of a lightweight quadrotor under loss in the battery voltage. Since the outdoor brushless motors are driven via a pulse width modulation (pwm) scheme, handshaking between the dynamic model and controller is established at the pwm level and this constitutes a major contribution of the paper. The attitude control is achieved via fractional order sliding mode control (FSMC) scheme. Necessary stability considerations are presented and it is seen that FSMC is a good alternative for the control of unmanned aerial vehicles.

Noise rejection, handling the difficulties coming from the mathematical representation of the system under investigation and alleviation of structural or unstructural uncertainties constitute prime challenges that are frequently encountered in the practice of systems and control engineering. Designing a controller has primarily the aim of achieving the tracking precision as well as a degree of robustness against the difficulties stated. From this point of view, variable structure systems theory offer well formulated solutions to such ill-posed problems containing uncertainty and imprecision. In this paper, a simple controller structure is discussed. The architecture is known as Adaptive Linear Element (ADALINE) in the framework of neural computing. The parameters of the controller evolve dynamically in time such that a sliding motion is obtained. The inner sliding motion concerns the establishment of a sliding mode in controller parameters, which aims to minimize the error on the controller outputs. The outer sliding motion is designed for the plant. The algorithm discussed drives the error on the output of the controller toward zero learning error level, and the state tracking error vector of the plant is driven toward the origin of the phase space simultaneously. The paper gives the analysis of the equivalence between the two sliding motions and demonstrates the performance of the algorithm on a three degrees of freedom, anthropoid robotic manipulator. In order to clarify the performance of the scheme, together with the dynamic complexity of the plant, the adverse effects of observation noise and nonzero initial conditions are studied. [S0022-0434(00)01704-4]