Adaptive Recurrent Neural Network Research Articles

Novel Adaptive Recurrent Legendre Neural Network Control for PMSM Servo-Drive Electric Scooter

Because an electric scooter driven by permanent magnet synchronous motor (PMSM) servo system has the unknown nonlinearity and the time-varying characteristics, its accurate dynamic model is difficult to establish for the design of the linear controller in whole system. In order to conquer this difficulty and raise robustness, a novel adaptive recurrent Legendre neural network (NN) control system, which has fast convergence and provide high accuracy, is proposed to control for PMSM servo-drive electric scooter under external torque disturbance in this study. The novel adaptive recurrent Legendre NN control system consists of a recurrent Legendre NN control with adaptation law and a remunerated control with estimation law. In addition, the online parameter tuning methodology of the recurrent Legendre NN control and the estimation law of the remunerated control can be derived by using the Lyapunov stability theorem. Finally, comparative studies are demonstrated by experimental results in order to show the effectiveness of the proposed control scheme.

Adaptive recurrent Chebyshev neural network control for PM synchronous motor servo‐drive electric scooter with V‐belt continuously variable transmission

SummaryBecause of unknown nonlinearity and time‐varying characteristics of electric scooter with V‐belt continuously variable transmission (CVT) driven by permanent magnet synchronous motor (PMSM), its accurate dynamic model is difficult to establish for the design of the linear controller in whole system. In order to conquer this difficulty and raise robustness, an adaptive recurrent Chebyshev neural network (NN) control system is proposed to control for PMSM servo‐drive electric scooter with V‐belt CVT under lumped nonlinear external disturbances in this study. The adaptive recurrent Chebyshev NN control system consists of a recurrent Chebyshev NN control and a compensated control with estimation law. In addition, the online parameters tuning methodology of the recurrent Chebyshev NN and the estimation law of the compensated controller can be derived by using the Lyapunov stability theorem. Moreover, the two optimal learning rates of the recurrent Chebyshev NN based on a discrete‐type Lyapunov function are proposed to guarantee the convergence of tracking error. Finally, comparative studies are demonstrated by experimental results in order to show the effectiveness of the proposed control scheme. Copyright © 2014 John Wiley & Sons, Ltd.

Adaptive recurrent Chebyshev neural network control for permanent magnet synchronous motor servo-drive electric scooter

Because of unknown nonlinearity and time-varying characteristic of electric scooter with V-belt continuously variable transmission system driven by using permanent magnet synchronous motor, its accurate dynamic model is difficult to establish for the design of the linear controller in whole system. In order to conquer this difficulty and raise robustness, an adaptive recurrent Chebyshev neural network control system is proposed to control for permanent magnet synchronous motor servo-drive electric scooter with V-belt continuously variable transmission under lumped nonlinear external disturbances in this study. The adaptive recurrent Chebyshev neural network control system consists of a recurrent Chebyshev neural network control and a compensated control with estimation law. In addition, the online parameter tuning methodology of the recurrent Chebyshev neural network and the estimation law of the compensated controller can be derived by using the Lyapunov stability theorem. Moreover, the two optimal learning rates of the recurrent Chebyshev neural network based on the discrete-type Lyapunov function are proposed to guarantee the convergence of tracking error. Finally, comparative studies are demonstrated by experimental results in order to show the effectiveness of the proposed control scheme.

Adaptive position tracking control system based on recurrent fuzzy wavelet neural networks for robot manipulators

In this article, we propose an adaptive recurrent fuzzy wavelet neural network control strategy to improve high-accuracy position tracking for robot manipulators. In order to deal with the unknown knowledge of the robot system problems, the adaptive recurrent fuzzy wavelet neural networks are applied in the main controller to approximate the unknown dynamics without the requirement of prior knowledge. In addition, an adaptive robust control law is also developed to eliminate uncertainties that consist of estimation errors and disturbances from the robot control system. The design of the adaptive online learning algorithms is determined using the Lyapunov stability theorem. Therefore, the proposed controller proves that it can guarantee not only the stability and robustness but also the tracking performance of the robot manipulators control system. The effectiveness and robustness of the proposed method are demonstrated by comparative simulation and experimental results.

3-Dimensional sliding mode adaptive MIMO recurrent fuzzy neural network control for two-link manipulator system

A novel methodology of 3-dimensional sliding mode adaptive multi-inputs multi-outputs (MIMO) recurrent fuzzy neural network (SAMRFNN) control for two-link manipulator system is proposed. This control scheme consists of a sliding mode (SM) controller and a 3-dimensional adaptive multi-inputs multi-outputs (MIMO) recurrent fuzzy neural network (AMRFNN) controller. The SM controller is to cope with uncertain dynamics of system and external disturbances; and the AMRFNN controller is used to approach the ideal controller of SM controller such as to stabilize the system. The Lyapunov theorem based adaptive laws are derived to tune the SAMRFNN parameters such that the stability and convergence of those can be guaranteed. In the simulations, persistency excitation (PE) effects and results of all parameters have been discussed and demonstrated; meanwhile, better effectiveness and performances of the proposed SAMRFNN control are demonstrated by comparisons with the adaptive fuzzy neural network (AFNN) control and state feedback control.

Adaptive recurrent neural network control of uncertain constrained nonholonomic mobile manipulators

In this article, motion/force control problem of a class of constrained mobile manipulators with unknown dynamics is considered. The system is subject to both holonomic and nonholonomic constraints. An adaptive recurrent neural network controller is proposed to deal with the unmodelled system dynamics. The proposed control strategy guarantees that the system motion asymptotically converges to the desired manifold while the constraint force remains bounded. In addition, an adaptive method is proposed to identify the contact surface. Simulation studies are carried out to verify the validation of the proposed approach.

Adaptive Recurrent Network Network Uncertainty Observer Based Integral Backstepping Control for a PMSM Drive System

The permanent magnet synchronous motor (PMSM) is suitable for high-performance servo applications and has been used widely for the industrial robots, computer-numerically-controlled (CNC) machine tools and elevators. The control performance of the actual PMSM drive system depends on many parameters, such as parameter variations, external load disturbance, and friction force. Their relationships are complex and the actual PMSM drive system has the properties of nonlinear uncertainty and time-varying characteristics. It is difficult to establish an accurate model for the nonlinear uncertainty and time-varying characteristics of the actual PMSM drive system Therefore, an adaptive recurrent neural network uncertainty observer (ARNNUO) based integral backstepping control system is developed to overcome this problem in this paper. The proposed control strategy is based on integral backstepping control combined with RNN uncertainty observer to estimate the required lumped uncertainty. An adaptive rule of the RNN uncertainty observer is employed to on-line adjust the weights of sigmoidal functions by using the gradient descent method and the backpropagation algorithm in according to Lyapunov function. This ARNNUO has the on-line learning ability to respond to the system’s nonlinear and time-varying behaviors. Experimental results are executed to show the control performance of the proposed control scheme. DOI: http://dx.doi.org/10.11591/ijpeds.v2i2.260

Integral Backstepping Control for a PMSLM Using Adaptive RNNUO

Due to uncertainties exist in the applications of the a permanent magnet synchronous linear motor (PMSLM) servo drive which seriously influence the control performance, thus, an integral backstepping control system using adaptive recurrent neural network uncertainty observer (RNNUO) is proposed to increase the robustness of the PMSLM drive. First, the field-oriented mechanism is applied to formulate the dynamic equation of the PMSLM servo drive. Then, an integral backstepping approach is proposed to control the motion of PMSLM drive system. With proposed integral backstepping control system, the mover position of the PMSLM drive possesses the advantages of good transient control performance and robustness to uncertainties for the tracking of periodic reference trajectories. Moreover, to further increase the robustness of the PMSLM drive, an adaptive RNN uncertainty observer is proposed to estimate the required lumped uncertainty. The effectiveness of the proposed control scheme is verified by experimental results.

Targeting and synchronization at tokamak with recurrent artificial neural networks

In this study, we propose an adaptive recurrent neural networks synchronization of H-mode and edge localized modes that is important for obtaining a long-pulse tokamak without disruption regime. The deterministic part of the plasma behavior should be synchronized with stochastic part by introducing stochastic artificial neural network.

An Adaptive Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-ion Batteries


 
 
 Prognostics is an emerging science of predicting the health condition of a system (or its components) based upon current and previous system states. A reliable predictor is very useful to a wide array of industries to predict the future states of the system such that the maintenance service could be scheduled in advance when needed. In this paper, an adaptive recurrent neural network (ARNN) is proposed for system dynamic state forecasting. The developed ARNN is constructed based on the adaptive/recurrent neural network architecture and the network weights are adaptively optimized using the recursive Levenberg-Marquardt (RLM) method. The effectiveness of the proposed ARNN is demonstrated via an application in remaining useful life prediction of lithium-ion batteries.
 
 

Prediction of ionospheric total electron content using adaptive neural network with in-situ learning algorithm

The Ionospheric Total Electron Content is responsible for the group delay of the signals from the Navigation satellites. This delay causes ranging error, which in turn degrades the accuracy of position estimated by the receivers. For critical applications, single frequency receivers resort to Satellite Based Augmentation Systems in order to have improved accuracy and integrity. The performance of these systems in terms of accuracy can be improved if predictions of the delays are available simultaneously with real measurements. This paper attempts to predict the Total Electron Content using adaptive recurrent Neural Network at three different locations of India. These locations are selected at the magnetic equator, at the equatorial anomaly crest and outside the anomaly range, respectively. In-situ Learning Algorithm has been used for tracking the non-stationary nature of the variation. Prediction is done for different prediction intervals. It is observed that, for each case, the mean and root mean square values of prediction errors remain small enough for all practical applications. Analysis of Variance is also done on the results.

Adaptive dynamic friction observer and recurrent fuzzy neural network estimator design with backstepping control

In this study, robust non-linear dynamic friction control is considered using a dynamic friction observer and intelligent control. An adaptive dynamic friction observer based on the LuGre friction model is proposed to estimate the friction parameters and a directly immeasurable friction state variable. A recurrent fuzzy neural network (RFNN) approximator and reconstructed error compensator are also designed to give additional robustness to the control system under friction model uncertainty. A proposed composite control scheme with a basic backstepping controller is applied to the position tracking control of the servo system. The performances of the proposed friction observer and the friction controller are demonstrated by some simulations and experiments.

Design and implementation of an adaptive recurrent neural networks (ARNN) controller of the pneumatic artificial muscle (PAM) manipulator

This paper presents the design, development and implementation of an adaptive recurrent neural networks (ARNN) controller suitable for real-time manipulator control applications. The unique feature of the ARNN controller is that it has dynamic self-organizing structure, fast learning speed, good generalization and flexibility in learning. The proposed adaptive algorithm focuses on fast and efficient optimization by weighting parameters of inverse recurrent neural models used in the ARNN controller. This approach is employed to implement the ARNN controller with a view to controlling the joint angle position of the highly nonlinear pneumatic artificial muscle (PAM) manipulator in real-time. The performance of this novel proposed controller was found to be superior compared with a conventional PID controller. These results can be applied to control other highly nonlinear systems as well.

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.

Transient Stability Enhancement of Power Systems by Lyapunov-Based Recurrent Neural Networks UPFC Controllers

A Lyapunov-based recurrent neural networks unified power flow controller (UPFC) is developed for improving transient stability of power systems. First, a simple UPFC dynamical model, composed of a controllable shunt susceptance on the shunt side and an ideal complex transformer on the series side, is utilized to analyze UPFC dynamical characteristics. Secondly, we study the control configuration of the UPFC with two major blocks: the primary control, and the supplementary control. The primary control is implemented by standard PI techniques when the power system is operated in a normal condition. The supplementary control will be effective only when the power system is subjected by large disturbances. We propose a new Lyapunov-based UPFC controller of the classical single-machine-infinite-bus system for damping enhancement. In order to consider more complicated detailed generator models, we also propose a Lyapunov-based adaptive recurrent neural network controller to deal with such model uncertainties. This controller can be treated as neural network approximations of Lyapunov control actions. In addition, this controller also provides online learning ability to adjust the corresponding weights with the back propagation algorithm built in the hidden layer. The proposed control scheme has been tested on two simple power systems. Simulation results demonstrate that the proposed control strategy is very effective for suppressing power swing even under severe system conditions.

Compensation for secondary uncertainty in electro-hydraulic servo system by gain adaptive sliding mode variable structure control

Based on consideration of the differential relations between the immeasurable variables and measurable variables in electro-hydraulic servo system, adaptive dynamic recurrent fuzzy neural networks(ADRFNNs) were employed to identify the primary uncertainty and the mathematic model of the system was turned into an equivalent linear model with terms of secondary uncertainty. At the same time, gain adaptive sliding mode variable structure control(GASMVSC) was employed to synthesize the control effort. The results show that the unrealization problem caused by some system’s immeasurable state variables in traditional fuzzy neural networks(TFNN) taking all state variables as its inputs is overcome. On the other hand, the identification by the ADRFNNs online with high accuracy and the adaptive function of the correction term’s gain in the GASMVSC make the system possess strong robustness and improved steady accuracy, and the chattering phenomenon of the control effort is also suppressed effectively.

Adaptive recurrent neural network control using a structure adaptation algorithm

This paper proposes an adaptive recurrent neural network control (ARNNC) system with structure adaptation algorithm for the uncertain nonlinear systems. The developed ARNNC system is composed of a neural controller and a robust controller. The neural controller which uses a self-structuring recurrent neural network (SRNN) is the principal controller, and the robust controller is designed to achieve L 2 tracking performance with desired attenuation level. The SRNN approximator is used to online estimate an ideal tracking controller with the online structuring and parameter learning algorithms. The structure learning possesses the ability of both adding and pruning hidden neurons, and the parameter learning adjusts the interconnection weights of neural network to achieve favorable approximation performance. And, by the L 2 control design technique, the worst effect of approximation error on the tracking error can be attenuated to be less or equal to a specified level. Finally, the proposed ARNNC system with structure adaptation algorithm is applied to control two nonlinear dynamic systems. Simulation results prove that the proposed ARNNC system with structure adaptation algorithm can achieve favorable tracking performance even unknown the control system dynamics function.

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.

An adaptive recurrent-neural-network motion controller for X-Y table in CNC Machine

In this paper, an adaptive recurrent-neural-network (ARNN) motion control system for a biaxial motion mechanism driven by two field-oriented control permanent magnet synchronous motors (PMSMs) in the computer numerical control (CNC) machine is proposed. In the proposed ARNN control system, a RNN with accurate approximation capability is employed to approximate an unknown dynamic function, and the adaptive learning algorithms that can learn the parameters of the RNN on line are derived using Lyapunov stability theorem. Moreover, a robust controller is proposed to confront the uncertainties including approximation error, optimal parameter vectors, higher-order terms in Taylor series, external disturbances, cross-coupled interference and friction torque of the system. To relax the requirement for the value of lumped uncertainty in the robust controller, an adaptive lumped uncertainty estimation law is investigated. Using the proposed control, the position tracking performance is substantially improved and the robustness to uncertainties including cross-coupled interference and friction torque can be obtained as well. Finally, some experimental results of the tracking of various reference contours demonstrate the validity of the proposed design for practical applications.

Adaptation of diagonal recurrent neural network model

An adaptive direct recurrent neural network model is developed for nonlinear dynamic system modelling in this paper. The model adaptation is achieved with the extended Kalman filter (EKF). A novel recursive algorithm is proposed to calculate the Jacobian matrix in the model adaptation so that the algorithm is simple and converges fast. The effectiveness of the developed adaptive model is demonstrated by applying to modelling a simulated continuous stirred tank reactor (CSTR). The model converges to the new process dynamics very quickly after a constant disturbance is added, and therefore can be used as an adaptive model in the adaptive model predictive control or internal model control for time-varying systems or fault tolerant control of nonlinear systems.