Cluster synchronization in fractional-order network with nondelay and delay coupling

In this paper, cluster synchronization for fractional-order complex network with nondelay and delay coupling is investigated. Based on the stability theory of fractional-order systems and the properties of fractional derivative, both static and adaptive control schemes are adopted to design effective controllers. Sufficient condition for achieving cluster synchronization about static controllers is provided. From the condition, the needed feedback gains can be estimated by simple calculations. Further, adaptive control scheme is introduced to design unified controllers. Noticeably, in the adaptive controllers, the feedback gains need not be calculated in advance and can adjust themselves to the needed values according to updating laws. Finally, numerical simulations are given to demonstrate the correctness of the obtained results.

System Characterization and Adaptive Tracking Control of Quadrotors under Multiple Operating Conditions

This paper presents an adaptive controller design framework with input compensation for quadrotor systems, which deals with different system operating conditions with a uniform update law for the controller parameters. The motivation of the work is to handle the situation that existing adaptive control schemes are either restricted to the system equilibrium as the hover condition or unable to deal with the diverse system uncertainties which cause system interactor matrix and high-frequency gain matrix to change. An adaptive control scheme equipped with an input compensator is constructed to make the system to have a uniform interactor matrix and a consistent pattern of the gain matrix signs over different operating conditions, which are key prior design conditions for model reference adaptive control applied to quadrotor systems. To deal with the uncertain system high-frequency gain matrix, a gain matrix decomposition technique is employed to parametrize an error system model in terms of the gain parameters and tracking errors, for the design of an adaptive parameter update law with reduced system knowledge. It is ensured that all closed-loop system signals are bounded, and the system output tracks a reference output asymptotically despite the system parameter uncertainties and the uncertain offsets at non-equilibrium operating conditions. The proposed scheme expands the capacity of adaptive control for quadrotors to operate at multiple operating conditions in the presence of system uncertainties. Simulation results of a quadrotor with the proposed adaptive control scheme are presented to show the desired system performance.

Enhanced Model Reference Adaptive Control Scheme for Tracking Control of Magnetic Levitation System

Magnetic Levitation is a process in which an object is suspended with the support of the magnetic field. Despite being an unstable system, Magnetic Levitation Systems (MAGLEV) have profound applications in various fields of engineering. MAGLEV systems are sensitive, unstable, and nonlinear and uncertainties always pose a challenge in Controller Design. As a solution, adaptive controllers came into existence with adaptation mechanisms to cover the system uncertainties. In this study, a simple, novel, and an effective approach to the Enhanced Adaptive Control scheme is proposed for the ball position control and tracking of an unstable Magnetic Levitation System. The proposed Enhanced Model Reference Adaptive Scheme (EMRAC) follows the same phenomenon of the Model Reference Adaptive Scheme (MRAC) with a slight difference in its control strategy. The proposed scheme consists of Proportional-Integral-Velocity plus Feed Forward as the control structure and a modified version of the standard tuning rule is used as the adaptation mechanism. The control scheme is applied to a standard benchmark Magnetic Levitation System and the tracking performance of the scheme is tested by applying square and multi-sine pattern trajectories to the Magnetic Levitation System. The performance of the developed Enhanced MRAC performance is compared with that of the Proportional Integral Velocity with Feedforward Control (PIV+FF) scheme and the proposed control scheme is proven to be more suitable. The performance of the proposed scheme is also analyzed with Power Spectral Density and Root Mean Square Error to evaluate the ball position tracking control. It is inferred from the experimental results that Enhanced MRAC accommodates the changes and makes the system more reliable with good tracking ability.

Stability analysis of adaptive control using fuzzy adapting rate neural emulator: Experimental validation on a thermal process

In this study, an adaptive control based on fuzzy adapting rate for neural emulator of nonlinear systems having unknown dynamics is proposed. The indirect adaptive control scheme is composed by the neural emulator and the neural controller which are connected by an autonomous algorithm inspired from the real-time recurrent learning. In order to ensure stability and faster convergence, a neural controller adapting rate is established in the sense of the continuous Lyapunov stability method. Numerical simulations are included to illustrate the effectiveness of the proposed method. The performance of the proposed control strategy is also demonstrated through an experimental simulation.