Neural Network Control System Research Articles

Novel mingled reformed recurrent hermite polynomial neural network control system applied in continuously variable transmission system

Compared with the classical linear controller, the nonlinear controller can result better control performance for the nonlinear uncertainties of the continuously variable transmission (CVT) system which is spurred by the synchronous reluctance motor (SynRM). The better control performance obtained by use of the proposed novel mingled reformed recurrent Hermite polynomial neural network (MRRHPNN) control system can be presented dynamic behavior for the nonlinear uncertainties of CVT system. The novel MRRHPNN control system can carry out overlooker control system, reformed recurrent Hermite polynomial neural network control (RRHPNN) with an adaptive law, and reimbursed control with an appraised law. Additionally, in accordance with the Lyapunov stability theorem, the adaptive law in the RRHPNN and the appraised law of the reimbursed control are established. Furthermore, two varied learning rates of two weights for the RRHPNN according to increment-type Lyapunov function are derived to help improving convergence. At last the obtained better control performances by use of the proposed control method are verified through the illustrated results by the comparative experimentations.

A new type design of lunar rover suspension structure and its neural network control system

A new type design of lunar rover suspension structure and its neural network control system

Model-free development of control systems for a multi-degree-of-freedom robot

For a multi-degree-of-freedom (MDOF) robot, its dynamics model is very complex, and there are many contained terms. Moreover, with the increase of the degree-of-freedom (DOF), the number of the terms contained in the dynamics equation increases geometrically, and the dynamics equation has the characteristics of highly nonlinear and serious coupling, therefore, it is difficult to achieve the accurate and efficient control. In particular, when the uncertain factors such changes in the load, friction and disturbance are considered, the problems of control are more obvious. To deal with these problems, the two model-free intelligent control systems are designed in this paper: (1) The adaptive sliding mode control (ASMC) system; (2) the fuzzy neural network control (FNNC) system. For the ASMC, the consumed time is shorter, and the efficiency is higher, but the control accuracy is relatively poorer. However, for the FNNC, the control accuracy is relatively higher, but the consumed time is longer, and the efficiency is poorer. In order to give full play to the advantages of the two intelligent control systems, the ASMC and the FNNC are combined to form the adaptive sliding mode-fuzzy neural network control (ASM-FNNC) system, which the priority is given to the ASMC, and the error thresholds are set, when the control error exceeds the thresholds, switch to the FNNC. Finally, the proposed control scheme is applied to a six DOF robot, to verify its effectiveness.

Design, implementation and modelling of the single and multiple extensor pneumatic muscle actuators

ABSTRACTThe soft actuator represents a valuable addition to the robotics research area in last two decades. These actuators provide significant features such as lightweight, softness, high force to weight ratio and the ability to form in different shapes. This article presents a new length model to the single extensor pneumatic muscle actuator (PMA) which is depended on the constructed parameters and the air pressure. On the other hand, the tensile force formula of the contractor actuator has been modified to describe the extension force of the extensor PMA. The parallel structure of four extensor actuators is designed and implemented as continuum arm. The bending behaviour of the proposed arm is illustrated and modelled mathematically. The length model of the single extensor actuator has validated by the comparison between the model and the experiment data and then a neural network (NN) control system is applied to control the elongation of the extensor PMA. The kinematics for the proposed continuum arm are presented to describe the bending of the arm and its direction.

Neural Network Control System of UAV Altitude Dynamics and Its Comparison with the PID Control System

This article proposes a comparative method to assess the performance of artificial neural network’s direct inverse control (DIC-ANN) with the PID control system. The comparison served as an analysis tool to assess the advantages of DIC-ANN over conventional control method for a UAV attitude controller. The development of ANN method for UAV control purposes arises due to the limitations of the conventional control method, which is the mathematical based model, involving complex expression, and most of them are difficult to be solved directly into analytic solution. Although the linearization simplified the solving process for such mathematical based model, omitting the nonlinear and the coupling terms is unsuitable for the dynamics of the multirotor vehicle. Thus, the DIC-ANN perform learning mechanism to overcome the limitation of PID tuning. Therefore, the proposed comparative method is developed to obtain conclusive results of DIC-ANN advantages over the linear method in UAV attitude control. Better achievement in the altitude dynamics was attained by the DIC-ANN compared to PID control method.

Adaptive fuzzy neural network control of wastewater treatment process with multiobjective operation

Abstract This study investigates an adaptive fuzzy neural network control system for the multiobjective operation of wastewater treatment process (WWTP) with standard effluent quality (EQ) as well as low energy consumption (EC). The control system consists of an optimization module with the adaptive multiobjective differential evolution (AMODE) algorithm and a control module with the adaptive fuzzy neural network (AFNN). First, an AMODE algorithm, using the adaptive adjustment strategies for selecting the suitable scaling factor and crossover rate, is developed to optimize all objectives simultaneously. Then, the optimal set-points of the dissolved oxygen concentration in the fifth tank ( S O5 ) and the nitrogen nitrate concentration in the second anoxic tank ( S NO2 ) of WWTP can be obtained by the AMODE algorithm. Second, an AFNN controller, based on an adaptive second order algorithm, is employed to trace the set-points of S O5 and S NO2 for achieving the process performance. Finally, the proposed control system is applied on the Benchmark Simulation Model 1 (BSM1). The performance comparison with other algorithms indicates that the proposed control system yields better effluent qualities and lower average operation consumption.

Analysis of Control Method for Magnetic Bearing Systems

Magnetic bearing systems have attracted extensive attention in the fields of high speed, spotless area, vacuum space, etc. System performance depends largely on the control link, and it has become a research focus to improve controller performance to ensure high precision stable suspension and high anti-interference capability. This paper considers optimized, sliding mode, robust, fuzzy, and neural network control systems and assesses their research status and limitations for magnetic bearing systems. Algorithms for proposed vibration and high speed flexible rotor controls are illustrated. Finally, development trends for control technology of magnetic bearing systems are discussed.

Comparative Dynamic Control of SynRM Servodrive Continuously Variable Transmission System Using Blend Amend Recurrent Gegenbauer-Functional-Expansions Neural Network Control and Altered Artificial Bee Colony Optimization

In comparison control performance with more complex and nonlinear control methods, the classical linear controller is poor because of the nonlinear uncertainty action that the continuously variable transmission (CVT) system is operated by the synchronous reluctance motor (SynRM). Owing to good learning skill online, a blend amended recurrent Gegenbauer-functional-expansions neural network (NN) control system was developed to return to the nonlinear uncertainties behavior. The blend amended recurrent Gegenbauer-functional-expansions NN control system can fulfill overseer control, amended recurrent Gegenbauer-functional-expansions NN control with an adaptive dharma, and recompensed control with a reckoned dharma. In addition, according to the Lyapunov stability theorem, the adaptive dharma in the amended recurrent Gegenbauer-functional-expansions NN and the reckoned dharma of the recompensed controller are established. Furthermore, an altered artificial bee colony optimization (ABCO) yields two varied learning rates for two parameters to find two optimal values, which helped improve convergence. Finally, the experimental results with various comparisons are demonstrated to confirm that the proposed control system can result in better control performance.

PSO-Self-Organizing Interval Type-2 Fuzzy Neural Network for Antilock Braking Systems

Nowadays, the antilock braking system (ABS) is the standard in all modern cars. The function of ABS is to optimize the maximize wheel traction by preventing wheel lockup during braking, so it can help the drivers to maintain steering maneuverability. In this study, a self-organizing interval type-2 fuzzy neural network (SOT2FNN) control system is designed for antilock braking systems. This control system comprises a main controller and a robust compensation controller; the SOT2FNN as the main controller is used to mimic an ideal controller, and the robust compensation controller is developed to eliminate the approximation error between the main controller and the ideal controller. To guarantee system stability, adaptive laws for adjusting the parameters of SOT2FNN based on the gradient descent method are proposed. However, in control design, the learning rates of adaptive law are very important and they significantly affect control performance. The particle swarm optimization method is therefore applied to find the optimal learning rates for the weights in reduction layer and also for the means, the variances of the Gaussian functions in the input membership functions. Finally, the numerical simulations of ABS response in different road conditions are provided to illustrate the effectiveness of the proposed approach.

Comparative dynamic control for continuously variable transmission with nonlinear uncertainty using blend amend recurrent Gegenbauer-functional-expansions neural network

Because the nonlinear uncertainty of the continuously variable transmission system operated by the synchronous reluctance motor is unknown, control performance obtained for classical linear controller is poor, with comparison to more complex, nonlinear control methods. Due to good learning skill online, a blend amended recurrent Gegenbauer-functional-expansions neural network (NN) control system was developed to return to the nonlinear uncertainties behavior. The blend amended recurrent Gegenbauer-functional-expansions NN control system can fulfill overseer control, amended recurrent Gegenbauer-functional-expansions NN control with an adaptive dharma and recompensed control with a reckoned dharma. In addition, according to the Lyapunov stability theorem, the adaptive dharma in the amended recurrent Gegenbauer-functional-expansions NN and the reckoned dharma of the recompensed controller are established. Furthermore, an altered artificial bee colony optimization yields two varied learning rates for two parameters to find two optimal values, which helped improving convergence. Finally, various comparisons of the experimental results are demonstrated to confirm that the proposed control system can result better control performance.

A Six-Phase CRIM Driving CVT using Blend Modified Recurrent Gegenbauer OPNN Control

Because the nonlinear and time-varying characteristics of continuously variable transmission (CVT) systems driven by means of a six-phase copper rotor induction motor (CRIM) are unconscious, the control performance obtained for classical linear controllers is disappointing, when compared to more complex, nonlinear control methods. A blend modified recurrent Gegenbauer orthogonal polynomial neural network (OPNN) control system which has the online learning capability to come back to a nonlinear time-varying system, was complied to overcome difficulty in the design of a linear controller for six-phase CRIM driving CVT systems with lumped nonlinear load disturbances. The blend modified recurrent Gegenbauer OPNN control system can carry out examiner control, modified recurrent Gegenbauer OPNN control, and reimbursed control. Additionally, the adaptation law of the online parameters in the modified recurrent Gegenbauer OPNN is established on the Lyapunov stability theorem. The use of an amended artificial bee colony (ABC) optimization technique brought about two optimal learning rates for the parameters, which helped reform convergence. Finally, a comparison of the experimental results of the present study with those of previous studies demonstrates the high control performance of the proposed control scheme.

Wind Turbine Driving a PM Synchronous Generator Using Novel Recurrent Chebyshev Neural Network Control with the Ideal Learning Rate

A permanent magnet (PM) synchronous generator system driven by wind turbine (WT), connected with smart grid via AC-DC converter and DC-AC converter, are controlled by the novel recurrent Chebyshev neural network (NN) and amended particle swarm optimization (PSO) to regulate output power and output voltage in two power converters in this study. Because a PM synchronous generator system driven by WT is an unknown non-linear and time-varying dynamic system, the on-line training novel recurrent Chebyshev NN control system is developed to regulate DC voltage of the AC-DC converter and AC voltage of the DC-AC converter connected with smart grid. Furthermore, the variable learning rate of the novel recurrent Chebyshev NN is regulated according to discrete-type Lyapunov function for improving the control performance and enhancing convergent speed. Finally, some experimental results are shown to verify the effectiveness of the proposed control method for a WT driving a PM synchronous generator system in smart grid.

RETRACTED: Novel application of continuously variable transmission system using composite recurrent Laguerre orthogonal polynomials modified PSO NN control system

Because the V-belt continuously variable transmission system spurred by permanent magnet (PM) synchronous motor has much unknown nonlinear and time-varying characteristics, the better control performance design for the linear control design is a time consuming procedure. In order to overcome difficulties for design of the linear controllers, the composite recurrent Laguerre orthogonal polynomials modified particle swarm optimization (PSO) neural network (NN) control system which has online learning capability to come back to the nonlinear and time-varying of system, is developed for controlling PM synchronous motor servo-driven V-belt continuously variable transmission system with the lumped nonlinear load disturbances. The composite recurrent Laguerre orthogonal polynomials NN control system consists of an inspector control, a recurrent Laguerre orthogonal polynomials NN control with adaptation law and a recouped control with estimation law. Moreover, the adaptation law of online parameters in the recurrent Laguerre orthogonal polynomials NN is originated from Lyapunov stability theorem. Additionally, two optimal learning rates of the parameters by means of modified PSO are posed in order to achieve better convergence. At last, comparative studies shown by experimental results are illustrated to demonstrate the control performance of the proposed control scheme.

Modelling and control of six‐phase induction motor servo‐driven continuously variable transmission system using blend modified recurrent Gegenbauer orthogonal polynomial neural network control system and amended artificial bee colony optimization

AbstractBecause the nonlinear and time‐varying characteristics of the continuously variable transmission system operated using a six‐phase copper rotor induction motor are unknown, improving the control performance of the linear control design is time‐consuming. To capture the nonlinear and dynamic behaviour of the six‐phase copper rotor induction motor servo‐driven continuously variable transmission system, a blend modified recurrent Gegenbauer orthogonal polynomial neural network (NN) control system, which has the online learning capability to return to the nonlinear time‐varying system, was developed. The blend modified recurrent Gegenbauer orthogonal polynomial NN control system can perform overseer control, modified recurrent Gegenbauer orthogonal polynomial NN control, and recompensed control. Moreover, the adaptation law of online parameters in the modified recurrent Gegenbauer orthogonal polynomial NN is based on the Lyapunov stability theorem. The use of amended artificial bee colony optimization yielded two optimal learning rates for the parameters, which helped improve convergence. Finally, comparison of the experimental results of the present study with those of previous studies demonstrated the high control performance of the proposed control scheme. Copyright © 2016 John Wiley & Sons, Ltd.

Blend modified recurrent Gegenbauer orthogonal polynomial neural network control for six-phase copper rotor induction motor servo-driven continuously variable transmission system using amended artificial bee colony optimization

Because the non-linear and time-varying characteristics of the continuously variable transmission (CVT) system driven by using a six-phase copper rotor induction motor (IM) are unknown, improving the control performance of the linear control design is time consuming. To overcome difficulties in the design of a linear controller for the six-phase copper rotor IM servo-driven CVT system with lumped non-linear load disturbances, a blend modified recurrent Gegenbauer orthogonal polynomial neural network (NN) control system, which has the online learning capability to return to the non-linear time-varying system, was developed. The blend modified recurrent Gegenbauer orthogonal polynomial NN control system can perform overseer control, modified recurrent Gegenbauer orthogonal polynomial NN control and recompensed control. Moreover, the adaptation law of online parameters in the modified recurrent Gegenbauer orthogonal polynomial NN is based on the Lyapunov stability theorem. The use of amended artificial bee colony optimization (ABCO) yielded two optimal learning rates for the parameters, which helped improve convergence. Finally, comparison of the experimental results of the present study with those of previous studies demonstrated the high control performance of the proposed control scheme.

RETRACTED: Design of a composite recurrent Laguerre orthogonal polynomial neural network control system with ameliorated particle swarm optimization for a continuously variable transmission system

Because the nonlinear and time-varying characteristics of the V-belt continuously variable transmission system driven by a permanent magnet synchronous motor (PMSM) are unknown, improving the control performance of the linear control design is time-consuming. To overcome difficulties in the design of a linear controller for the PMSM servo-driven V-belt continuously variable transmission system with lumped nonlinear load disturbances, a composite recurrent Laguerre orthogonal polynomial neural network (NN) control system with ameliorated particle swarm optimization (PSO), which has the online learning capability to respond to the nonlinear time-varying system, was developed. The composite recurrent Laguerre orthogonal polynomial NN control system can perform inspector control, recurrent Laguerre orthogonal polynomial NN control which involves an adaptation law, and recouped control which involves an estimation law. Moreover, the adaptation law of online parameters in the recurrent Laguerre orthogonal polynomial NN is based on the Lyapunov stability theorem. The use of ameliorated particle swarm optimization yielded two optimal learning rates for the parameters, which helped improve convergence. Finally, comparison of the experimental results of the present study with those of previous studies demonstrated the high control performance of the proposed control scheme.

Перспективная конструкция многофункционального культиватора для склонов с автоматической биометрически корректируемой системой контроля поперечной устойчивости

The authors propose the concept of a control system based on the creation of avated biometric corrected neural network control system implemented on the basis tillage machines and tractors unit capable to function effectively within the system.For this approach, it is planned to develop a software system that provides for tracking the behavior of machine and tractor unit operator in real time. The resulting data will be fed to the input of artificial neural networks of different topologies.

Direct Adaptive Neural Control of Uncertain Pure-Feedback Systems with Tracking Accuracy Known a Priori via Backstepping Design

This paper concerns the adaptive neural network tracking control problem for a class of uncertain pure-feedback nonlinear systems. The main features of the proposed control scheme are shown here and compared to those of existing uncertain pure-feedback systems. First, the accuracy of the ultimate tracking error is given a priori. However, the tracking accuracy of the system cannot be determined a priori if existing control methods are used. Second, unlike in the case of traditional adaptive neural network control systems, the convergence of the tracking error was here analyzed using Barbalat's lemma based on nonnegative functions rather than Lyapunov functions. Third, with help from two novel nth-order continuously differentiable switching functions, the ultimate tracking error can be proven to reach accuracy given a priori and all closed-loop signals are uniformly ultimately bounded. Finally, a simulation is provided to illustrate the effectiveness of the proposed control approach.

RETRACTED ARTICLE: Hybrid recurrent Laguerre-orthogonal-polynomials neural network control with modified particle swarm optimization application for V-belt continuously variable transmission system

A V-belt continuously variable transmission system driven by a permanent magnet synchronous motor has much unknown nonlinear and time-varying characteristics. In order to capture the system's nonlinear and dynamic behavior, a hybrid recurrent Laguerre-orthogonal-polynomials neural network (NN) control system with modified particle swarm optimization (PSO) is proposed for achieving online better learning capacity and faster convergence to enhance system robustness. The hybrid recurrent Laguerre-orthogonal-polynomials NN control system can perform inspected control, recurrent Laguerre-orthogonal-polynomials NN control, which involves an adaptive law, and recouped control, which involves an estimated law. Moreover, the adaptive law of online parameters in the recurrent Laguerre-orthogonal-polynomials NN is derived by means of Lyapunov stability theorem. Furthermore, two optimal learning rates of the online parameters in the recurrent Laguerre-orthogonal-polynomials NN by means of modified PSO are applied to achieve online better learning capacity and faster convergence. Finally, to show the effectiveness of the proposed control scheme, comparative studies are demonstrated by experimental results.

Application of V-Belt Continuously Variable Transmission System Using Hybrid Recurrent Laguerre Orthogonal Polynomials Neural Network Control System and Modified Particle Swarm Optimization

Because the V-belt continuously variable transmission (CVT) system spurred by permanent magnet synchronous motor (PMSM) has unknown nonlinear and time-varying properties, the better control performance design for the linear control design is a time consuming procedure. In order to conquer difficulties for design of the linear controllers, the hybrid recurrent Laguerre orthogonal polynomials neural network (NN) control system, which has online learning ability to react to unknown nonlinear and time-varying characteristics, is developed for controlling PMSM servo-driven V-belt CVT system with the lumped nonlinear load disturbances. The hybrid recurrent Laguerre orthogonal polynomials NN control system consists of an inspector control, a recurrent Laguerre orthogonal polynomials NN control with adaptation law, and a recouped control with estimation law. Moreover, the adaptation law of online parameters in the recurrent Laguerre orthogonal polynomials NN is originated from Lyapunov stability theorem. Additionally, two varied learning rates of the parameters by means of modified particle swarm optimization (PSO) are posed in order to achieve better convergence. At last, comparative studies shown by experimental results are illustrated in order to verify the effectiveness of the proposed control scheme.