Recurrent Neural Network Identifier Research Articles

Recurrent neural network based high-precision position compensation control of magnetic levitation system

For improving the dynamic quality and steady-state performance, the hybrid controller based on recurrent neural network (RNN) is designed to implement the position control of the magnetic levitation ball system in this study. This hybrid controller consists of a baseline controller, an RNN identifier, and an RNN controller. In the hybrid controller, the baseline controller based on the control law of proportional-integral-derivative is firstly employed to provide the online learning sample and maintain the system stability at the early control phase. Then, the RNN identifier is trained online to learn the accurate inverse model of the controlled object. Next, the RNN controller shared the same structures and parameters with the RNN identifier is applied to add the precise compensation control quantity in real-time. Finally, the effectiveness and advancement of the proposed hybrid control strategy are comprehensively validated by the simulation and experimental tests of tracking step, square, sinusoidal, and trapezoidal signals. The results indicate that the RNN-based hybrid controller can obtain higher precision and faster adjustment than the comparison controllers and has strong anti-interference ability and robustness.

Adaptive Tracking Control of Nonholonomic Mobile Manipulators Using Recurrent Neural Networks

The trajectory tracking problem is considered for a class of nonholonomic mobile manipulators in the presence of uncertainties and disturbances. First, under the assumption that the kinematic subsystem of mobile manipulator is capable of being transformed into the chained form and the dynamic subsystem of mobile manipulator is exactly known without considering external disturbances, a model-based controller is designed at the torque level using backstepping design technology. However, the model-based control may be inapplicable for practical applications, as the uncertainties and disturbances do exist in the dynamics of mobile manipulators inevitably. Thus, a Recurrent Neural Network (RNN) based control system is developed without requiring explicit knowledge of the system dynamics. The control system comprises a RNN identifier and a compensation controller, in which the RNN is utilized to identify the unknown dynamics on-line, and the compensation controller is presented to compensate the approximation error and external disturbances. The online adaptive laws of the control system are derived in the Lyapunov sense so that the stability of the system can be guaranteed. Finally, simulation results for a wheeled mobile manipulator are provided to show the good tracking performance and robustness of the proposed control method.

Static VAR compensator using recurrent neural network

In this paper, an internal model control recurrent neural network method is used to control the switching of thyristor-controlled reactor in a static VAR compensator (SVC) system for regulating the voltage. The novel controller scheme contains several feedback loops instead of only a feed-forward loop as in the conventional recurrent neural network (RNN). In the proposed controller model, the RNN identifier creates a sample of the connected system and its output generates a part of inputs for the RNN controller which then sends the control signal to the SVC system. Three types of non-linear conditions are chosen to test the operational capability of the new control system to perform the voltage regulation satisfying the IEEE Std 519-1992. The test cases contain a three-phase fault power system, opening of one of the transmission lines in a double line transmission system and sudden changes in the load demand. Results show that the proposed control model is capable of regulating the voltage of the system in a desired range.

Neural Network Based Self-Learning Control Strategy for Electronic Throttle Valve

Recently, the application of the electronic throttle has been very popular in the automotive industry. However, difficulties in the control of electronic throttle valves exist due to multiple nonlinearities and plant parameter variations. A neural-network-based self-learning control (SLC) strategy that consists of a fuzzy neural network (FNN) controller and a recurrent neural network (RNN) identifier is proposed for electronic throttle valves in this paper. The FNN controller, which combines the semantic transparency of rule-based fuzzy systems with the learning capability of a neural network, is utilized as an SLC scheme and will be robust to plant parameter variations. An RNN identifier is employed to model the plant and provides plant information for the learning of the FNN controller. Both the structure and the learning algorithm of the control system are presented. The proposed controller is verified by computer simulations and experiments.

A levenberg-marquardt learning applied for recurrent neural identification and control of a wastewater treatment bioprocess

The paper proposed a new recurrent neural network (RNN) model for systems identification and states estimation of nonlinear plants. The proposed RNN identifier is implemented in direct and indirect adaptive control schemes, incorporating a noise rejecting plant output filter and recurrent neural or linear-sliding mode controllers. For sake of comparison, the RNN model is learned both by the backpropagation and by the recursive Levenberg–Marquardt (L–M) learning algorithm. The estimated states and parameters of the RNN model are used for direct and indirect adaptive trajectory tracking control. The proposed direct and indirect schemes are applied for real-time control of wastewater treatment bioprocess, where a good, convergence, noise filtering, and low mean squared error of reference tracking is achieved for both learning algorithms, with priority of the L–M one. © 2009 Wiley Periodicals, Inc.

Robust speed-controlled induction motor drive based on recurrent neural network

This paper proposes a recurrent neural network speed controller for an induction motor drive. This speed controller consists of a recurrent neural network identifier (RNNI) and recurrent neural network controller (RNNC). The RNNI is used to provide real-time adaptive identification of the unknown motor dynamics. The RNNC is used to produce an adaptive control force so that the motor speed can accurately track the reference command. A back-propagation algorithm was used as the learning algorithm to automatically adjust the weights of the RNNI and RNNC in order to minimize the performance functions. The proposed control scheme can quickly estimate the plant parameters and produce a control force, such that the motor speed can accurately track the reference command. Both computer simulations and experimental results demonstrated that the proposed control scheme was able to obtain robust speed control.

SLIDING MODE CONTROL OF AEROBIC BIOPROCESS USING RECURRENT NEURAL IDENTIFIER

The paper proposed a new adaptive control system containing a Recurrent Neural Network (RNN) identifier, a Sliding Mode (SM) controller, and an integral term. The SM control is derived defining the sliding surface with respect to the output tracking error. The state and parameter information to resolve the SM control is obtained from a RNN identifier, which permits the SM control to maintain the sliding regime when the plant parameters changed. The simulation results obtained with a continuous stirred tank reactor plant model confirmed the good quality of the control.

Neural-network-identification-based adaptive control of wing rock motions

The proposed control system comprises a computation controller and a tracking controller. The computation controller containing a recurrent neural network identifier is the principal controller, and the tracking controller is designed to achieve L2 tracking performance with a desired attenuation level. To investigate the effectiveness of the proposed control system the design methodology is used to control a wing rock motion, manifested by a limit-cycle oscillation predominantly for an aircraft operating at subsonic speeds and high angles of attack. Simulation results demonstrate that the proposed control system can achieve favourable tracking performance for the wing rock motion.

Adaptive discrete - time sliding mode control using recurrent neural identifier

A new adaptive control system containing a Recurrent Neural Network (RNN) identifier and a Sliding Mode Controller (SMC) is proposed. The SMC is derived defining the sliding surface with respect to the output tracking error. The necessary state and parameter information to resolve the SMC is obtained from a RNN identifier, which gives the needed flexibility when the plant parameters changed. The good quality of the proposed control scheme is illustrated by simulation results, obtained with a fed-batch fermentation plant model.

A stable adaptive neural-network-based scheme for dynamical system control

A stable adaptive neural-network-based control scheme for dynamical systems is presented and a continuous recurrent neural network model of dynamical systems is constructed in this paper. A novel algorithm for updating weights in the neural network, which is not derived from the conventional back propagation algorithm, is also constructed. The proposed control law is obtained adaptively by a continuous recurrent neural network identifier, but not by a conventional neural network controller. In such a way, the stability in the sense of the Lyapunov stability can be guaranteed theoretically. The control error converges to a range near the zero point and remains within the domain throughout the course of the execution. Numerical experiments for a longitudinal vibration ultrasonic motor show that the proposed control scheme has good control performance.

Adaptive recurrent-neural-network control for linear induction motor

In this study an adaptive recurrent-neural-network controller (ARNNC) is proposed to control a linear induction motor (LIM) servo drive. First, the secondary flux of the LIM is estimated with an adaptive flux observer on the stationary reference frame and the feedback linearization theory is used to decouple the thrust force and the flux amplitude of the LIM. Then, an ARNNC is proposed to control the mover of the LIM for periodic motion. In the proposed controller, the LIM servo drive system is identified by a recurrent-neural-network identifier (RNNI) to provide the sensitivity information of the drive system to an adaptive controller. The backpropagation algorithm is used to train the RNNI on line. Moreover, to guarantee the convergence of identification and tracking errors, analytical methods based on a discrete-type Lyapunov function are proposed to determine the varied learning rates of the RNNI and the optimal learning rate of the adaptive controller. The effectiveness of the proposed control scheme is verified by both the simulated and experimental results. Furthermore, the advantages of the proposed control system are indicated in comparison with the sliding mode control system.