Showing papers by "Ardashir Mohammadzadeh published in 2019"
TL;DR: Simulation studies presented indicate that the proposed control method results in good performance under time-varying topology, unknown dynamics and external disturbances, and the effectiveness of the proposed DGT2FS is verified also on modeling problems with high dimensional real-world data sets.
Abstract: In this paper, a robust adaptive control scheme is proposed for the leader following control of a class of fractional-order multi-agent systems (FMAS). The asymptotic stability is shown by a linear matrix inequality (LMI) approach. The nonlinear dynamics of the agents are assumed to be unknown. Moreover, the communication topology among the agents is assumed to be unknown and time-varying. A deep general type-2 fuzzy system (DGT2FS) using restricted Boltzmann machine (RMB) and contrastive divergence (CD) learning algorithm is proposed to estimate uncertainties. The simulation studies presented indicate that the proposed control method results in good performance under time-varying topology, unknown dynamics and external disturbances. The effectiveness of the proposed DGT2FS is verified also on modeling problems with high dimensional real-world data sets.
54 citations
01 Aug 2019
TL;DR: A novel robust predictive control strategy is proposed for the synchronization of fractional-order time-delay chaotic systems using a recurrent non-singleton type-2 fuzzy neural network for the estimation of the unknown functions.
Abstract: In this paper, a novel robust predictive control strategy is proposed for the synchronization of fractional-order time-delay chaotic systems. A recurrent non-singleton type-2 fuzzy neural network (RNT2FNN) is used for the estimation of the unknown functions. Additionally, another RNT2FNN is used for the modeling of the tracking error. A nonlinear model-based predictive controller is then designed based on the proposed fuzzy model. The asymptotic stability of the approach is derived based on the Lyapunov stability theorem. A number of simulation examples are presented to verify the effectiveness of the proposed control method for the synchronization of two uncertain fractional-order time-delay identical and nonidentical chaotic systems. The proposed control strategy is also employed for high-performance position control of a hydraulic actuator. In this example, the nonlinear mechanical model of the hydraulic actuator, instead of a mathematical model, is simulated. The example demonstrates that the proposed control strategy can be applied to a wide class of nonlinear systems.
41 citations
TL;DR: It is shown that the proposed fuzzy control method results in better performance in the presence of unknown fractional-order and unknown perturbed dynamics.
Abstract: In this paper, a fuzzy control method is proposed for a class of fractional-order chaotic systems. The dynamics of the system are unknown and are perturbed by the external disturbances. Also, the value of the fractional-order is assumed to be unknown. The type-2 fuzzy systems (T2FSs) are employed to estimate the unknown functions in the dynamics of the system. The parameters of T2FS and the value of fractional-order are estimated by unscented Kalman filter. The upper bound of the approximation error is online estimated, and a new fractional-order compensator is designed to eliminate the effect of the uncertainties and to guarantee the closed-loop stability. The effectiveness of the proposed method is shown by simulations, and the results are compared with some other techniques. It is shown that the proposed method results in better performance in the presence of unknown fractional-order and unknown perturbed dynamics.
37 citations
TL;DR: A new approach based on the square-root cubature quadrature Kalman filter (SR-CQKF) is proposed for the training the level of the secondary membership and the centers of membership functions and it is demonstrated that the developed method results in high performance in contrast to the other methods.
36 citations
TL;DR: An optimal control scheme, based on dynamic programming strategy, is presented for synchronization of uncertain fractional-order chaotic/hyperchaotic systems and a type-2 fuzzy wavelet neural network (T2FWNN) is proposed for estimation of the unknown functions in dynamics of system.
Abstract: In this paper, an optimal control scheme, based on dynamic programming strategy, is presented for synchronization of uncertain fractional-order chaotic/hyperchaotic systems. In the scheme, a type-2 fuzzy wavelet neural network (T2FWNN) is proposed for estimation of the unknown functions in dynamics of system. For solving the fractional optimal control problem, fractional-order derivative is approximated by using Oustaloup recursive approximation method. Simulation studies verify the effectiveness of the proposed control scheme and the proposed T2FWNN.
29 citations
TL;DR: It is revealed that the outputs of the IM could track the desired signals in the presence of the mentioned disturbances and the proposed control approach resulted in better performance.
Abstract: In this study, a new adaptive controller is introduced for the induction motors (IMs) based on immersion and invariance (I&I) technique. The dynamics of the IM system are perturbed by some disturbances such as time-varying rotor resistance and load torque. Accordingly, an adaptive controller is designed and the uncertain parameters are online estimated. The adaptation laws are obtained from the stability analysis based on I&I method. The simulation results verified the effectiveness of the proposed control method. It is revealed that the outputs of the IM could track the desired signals in the presence of the mentioned disturbances. Also, the results are compared with the conventional control methods and it is concluded that the proposed control approach resulted in better performance.
10 citations
2 citations
TL;DR: A new robust control was proposed for a class of induction motor system based on the immersion and invariance (I&I) approach, and the proposed controller was examined in this paper.
Abstract: In this paper, a new robust control was proposed for a class of induction motor (IM) system based on the immersion and invariance (I&I) approach. The proposed controller was examined in nor...
2 citations
10 Mar 2019
1 citations
01 Apr 2019
TL;DR: Simulations results show the effectiveness of the proposed network and the proposed learning algorithm for training recurrent interval type-2 fuzzy neural networks.
Abstract: This paper proposes a novel learning algorithm benefitting from square-root cubature Kalman filters for training recurrent interval type-2 fuzzy neural networks. The recurrence property in this network is feeding the output of each input to itself. Simulations results show the effectiveness of the proposed network and the proposed learning algorithm.