Adaptive Recurrent Fuzzy Neural Network Research Articles

Recurrent Neuro-Fuzzy Control of Grid-Interfaced Solid Oxide Fuel Cell (SOFC) System

The paper presents controller design, modelling, and simulations of the solid oxide fuel cell (SOFC) system. The SOFC model is used for the development of the fuzzy control scheme to improve the system performance. The SOFC is widely acknowledged for the clean distributed power generation. However, dire process problems occur frequently when the stand-alone fuel cell is directly interfaced with the electricity grid. Moreover, sustaining the optimal power quality and load following is the huge challenge, during the peak power demand schedule from the utility grid and large load perturbations. Consequently, a suitable and highly efficient control system is required for controlling and following the power load demands for the complex grid interfaced SOFC power systems. Therefore, a novel nonlinear hybrid adaptive recurrent fuzzy neural network (ARFNN) is developed for the control of the grid interfaced SOFC. The rapid power load following and safe SOFC operation requirement is also considered in the design of the closed loop control system. Simulation results proved that the proposed hybrid ARFNN enhances the optimal power quality and load-following than the conventional PI control scheme.

Wastewater treatment control method based on a rule adaptive recurrent fuzzy neural network

PurposeThe purpose of this paper is to present an on-line modeling and controlling scheme based on the dynamic recurrent neural network for wastewater treatment system.Design/methodology/approachA control strategy based on rule adaptive recurrent neural network (RARFNN) is proposed in this paper to control the dissolved oxygen (DO) concentration and nitrate nitrogen (SNo) concentration. The structure of the RARFNN is self-organized by a rule adaptive algorithm, and the rule adaptive algorithm considers the overall information processing ability of neural network. Furthermore, a stability analysis method is given to prove the convergence of the proposed RARFNN.FindingsBy application in the control problem of wastewater treatment process (WWTP), results show that the proposed control method achieves better performance compared to other methods.Originality/valueThe proposed on-line modeling and controlling method uses the RARFNN to model and control the dynamic WWTP. The RARFNN can adjust its structure and parameters according to the changes of biochemical reactions and pollutant concentrations. And, the rule adaptive mechanism considers the overall information processing ability judgment of the neural network, which can ensure that the neural network contains the information of the biochemical reactions.

Recurrent fuzzy neural network backstepping control for the prescribed output tracking performance of nonlinear dynamic systems

This paper proposes a backstepping control system that uses a tracking error constraint and recurrent fuzzy neural networks (RFNNs) to achieve a prescribed tracking performance for a strict-feedback nonlinear dynamic system. A new constraint variable was defined to generate the virtual control that forces the tracking error to fall within prescribed boundaries. An adaptive RFNN was also used to obtain the required improvement on the approximation performances in order to avoid calculating the explosive number of terms generated by the recursive steps of traditional backstepping control. The boundedness and convergence of the closed-loop system was confirmed based on the Lyapunov stability theory. The prescribed performance of the proposed control scheme was validated by using it to control the prescribed error of a nonlinear system and a robot manipulator.

Sliding Mode Control of a Class of Uncertain Nonlinear Time-Delay Systems Using LMI and TS Recurrent Fuzzy Neural Network

This paper proposes the sliding mode control using LMI techniques and adaptive recurrent fuzzy neural network (RFNN) for a class of uncertain nonlinear time-delay systems. First, a novel TS recurrent fuzzy neural network (TS-RFNN) is developed to provide more flexible and powerful compensation of system uncertainty. Then, the TS-RFNN based sliding model control is proposed for uncertain time-delay systems. In detail, sliding surface design is derived to cope with the non-Isidori-Bynes canonical form of dynamics, unknown delay time, and mismatched uncertainties. Based on the Lyapunov-Krasoviskii method, the asymptotic stability condition of the sliding motion is formulated into solving a Linear Matrix Inequality (LMI) problem which is independent on the time-varying delay. Furthermore, the input coupling uncertainty is also taken into our consideration. The overall controlled system achieves asymptotic stability even if considering poor modeling. The contributions include: i) asymptotic sliding surface is designed from solving a simple and legible delay-independent LMI; and ii) the TS-RFNN is more realizable (due to fewer fuzzy rules being used). Finally, simulation results demonstrate the validity of the proposed control scheme.

Adaptive recurrent fuzzy neural networks for active noise control

This paper discussed nonlinear active noise control (ANC). Some adaptive nonlinear noise control approaches using recurrent fuzzy neural networks (RFNNs) were derived. The proposed RFNNs were feed-forward fuzzy neural networks (NNs) with different local feedback connections that are used to construct dynamic fuzzy rules. Different recurrent connection strategies, diagonal recurrent and full connected recurrent ones, were considered. In addition, different fuzzy operation strategies, product (multiply) inference and “summation” (addition) inference, were proposed. Because RFNN-based ANC systems can capture the dynamic behavior of a system through the feedback links, the exact lag of the input variables need not be known in advance. Online dynamic back-propagation learning algorithms based on the error gradient descent method were proposed, and the local convergence of a closed-loop system was proven using the discrete Lyapunov function. A nonlinear simulation example showed that an adaptive ANC system based on an RFNN with summation inference is superior to a system based on other fuzzy NNs.

Adaptive recurrent fuzzy neural network control for synchronous reluctance motor servo drive

In the paper an adaptive recurrent fuzzy neural network (ARFNN) control system is proposed, to control a synchronous reluctance motor (SynRM) servo drive. First, the field-oriented mechanism is applied to formulate the dynamic equation of the SynRM servo drive. Then, the ARFNN control system is proposed to control the rotor of the SynRM servo drive for the tracking of periodic reference inputs. In the ARFNN control system, the RFNN controller is used to mimic an optimal control law, and the compensated controller with adaptive algorithm is proposed to compensate for the difference between the optimal control law and the RFNN controller. Moreover, an online parameter training methodology, which is derived using the Lyapunov stability theorem and the backpropagation method, is proposed to increase the learning capability of the RFNN. The effectiveness of the proposed control scheme is verified by simulated and experimental results.