Search

A two-way fifth-generation (5G) new radio (NR) free-space optical (FSO)-hollow-core fibre (HCF)-underwater wireless optical communication (UWOC) converged systems with a red/green/blue (R/G/B) 3-wavelengths and spatial light modulator (SLM)-based beam-tracking scheme is practically built. It is the first to practically build a two-way FSO-HCF-UWOC converged system with high-speed and long-distance optical wireless-wired-underwater wireless communication characteristics. It shows a 5G NR FSO-HCF-UWOC convergence from drone or buildings to undersea, using R/G/B 3-wavelengths and an SLM as a demonstration. The R/G/B 3-wavelengths are used to enhance the downstream and upstream aggregate transmission rates. An SLM with electrical comparator is used to adjust the laser beam and mitigate laser beam misalignment caused by drone movement or ocean flow. Over a hybrid of 1-km FSO, 10-m HCF, and 10.44-m ocean water-air-ocean water medium, downstream/upstream 5G-millimeter-wave (MMW) 9.1-Gb/s/24-GHz signals are transmitted with satisfactorily low bit error rates and error vector magnitudes, as well as distinct constellations. This demonstrated that the 5G NR FSO-HCF-UWOC converged system exhibits promising potential as it advances the scenario implemented by the 5G-MMW signals over FSO, HCF, and UWOC convergence, paving the way for high-speed and long-distance communications across diverse media. [ABSTRACT FROM AUTHOR]

Due to nonlinear friction and uncertainty effects of linear permanent magnet synchronous motor (LPMSM), the existing linear controllers cannot achieve good control performance. In order to increase the robustness of system under some uncertainties disturbances, the proposed adaptive amended Elman neural network backstepping (AAENNB) control system with error compensation controller is adopted for controlling the LPMSM drive system. Firstly, the field-oriented control (FOC) is applied to formulate the dynamic equation of the LPMSM drive system. Secondly, a backstepping approach is proposed for controlling the motion of LPMSM drive system. The proposed backstepping control system and the mover position of the LPMSM drive system possess good transient control performance under the uncertainties action for the tracking of periodic references. Because of the LPMSM with nonlinear and time-varying dynamic characteristics, an adaptive amended Elman neural network uncertainty observer (AAENNUO) with the adaptive law is proposed to estimate the required lumped uncertainty. Moreover, the error compensation controller with the error estimation law is proposed to compensate the minimum reconstructed error according to Lyapunov stability theorem. Furthermore, to improve convergent speed and to obtain better learning performance, the varied learning rate of the weight in the AENN is regulated by use of the corrected particle swarm optimization (CPSO) algorithm with segment regulation mechanics, that is, the innovativeness for using the CPSO algorithm. At last, the usefulness of the proposed control system is confirmed by experimental results. [ABSTRACT FROM AUTHOR]

A linear permanent magnet synchronous motor (LPMSM) drive system is keeping in many nonlinear effects such as the external load force, the flux saturation, the cogging force, the column friction and Stribeck force, and the parameters variations. Due to the uncertainty effects the existing linear controllers can not achieve better control performances for the LPMSM drive system. To raise robustness under occurrence of uncertainty, the integral backstepping control system with hitting function is proposed for controlling the LPMSM drive system in accordance with the Lyapunov function. To improve larger chattering phenomenon under uncertainties effects, the integral backstepping control system with revised recurrent fuzzy neural network (RRFNN) and mended particle swarm optimization (MPSO) is proposed to operate the LPMSM drive system to raise robustness of system. The RRFNN is used to estimate the value of the external lumped force uncertainty. Moreover, the error compensation control with the error compensation mechanism is proposed to compensate the minimum reconstructed error of the error estimation law. Besides, four variable learning rates in the weights of the RRFNN are regulated by virtue of MPSO with segment regulation to speed-up parameter's convergence. Finally, comparative performances through some tentative upshots are verified that the integral backstepping control system by virtue of RRFNN with MPSO has better control performances than those of the proposed methods for the LPMSM drive system. [ABSTRACT FROM AUTHOR]

Owing to air-gap field harmonic, cogging torque, stator's current time harmonic and the influence of flux saturation, a synchronous reluctance motor (SynRM) drive system has highly nonlinear uncertainties. Thus the linear control method for the SynRM drive system is difficult to achieved good performance under the nonlinear uncertainty action. To obtain better control performance the novel nonlinear backstepping control system using upper bound with switching function is firstly proposed for controlling the SynRM drive system to prevail the lumped uncertainty. With the proposed control system, the SynRM servo-drive system holds in robustness to uncertainties for the tracking of periodic reference trajectories. To enhance the robustness of the SynRM drive system, the novel nonlinear backstepping control system using adaptive law is proposed for estimating the required lumped uncertainty to reduce chattering phenomenon. When the inertia of the counterweight is varying, this proposed method can perform well in general situations, but cannot get a satisfactory performance. The novel nonlinear backstepping control system using reformed recurrent Hermite polynomial neural network with adaptive law and error estimated law is thus proposed to estimate the lumped uncertainty and compensate estimated error for obtaining better control performance. Furthermore, two varied learning rates of the reformed recurrent Hermite polynomial neural network is derived according to increment type Lyapunov function to speed-up parameter's convergence. Finally, some experimental results with comparative control performances are demonstrated, and then the effectiveness of the proposed control system with better control performance is verified for the position tracking of periodic reference inputs with torque ripple consideration. [ABSTRACT FROM AUTHOR]

The good control performance of the permanent magnet linear synchronous motor (LSM) drive system is very difficult to achieve using linear controller because of uncertainty effects, such as ending-fictitious force. A backstepping approach is proposed to control the motion of the LSM drive system. With the proposed backstepping control system, the mover position of the LSM drive achieves good transient control performance and robustness. Although favorable tracking responses can be obtained by the backstepping control system, the chattering in the control effort is critical because of the large control gain. Because there are many nonlinear and time-varying uncertainties in the LSM drive systems, the nonlinear backsteping control system, which an adaptive modified recurrent Laguerre orthogonal polynomial neural network (NN) is used to estimate uncertainty, is thus proposed to reduce the chattering in the control effort and thereby enhance the robustness of the LSM drive system. In addition, the on-line parameter training methodology of the modified recurrent Laguerre orthogonal polynomial NN is based on the Lyapunov stability theorem. Furthermore, two optimal learning rates of the modified recurrent Laguerre orthogonal polynomial NN are derived to accelerate parameter convergence. Finally, comparison of the experimental results of the present study demonstrates the high control performance of the proposed control scheme. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Because a V-belt continuously variable transmission system driven by Permanent magnet synchronous motor (PMSM) has many nonlinear and time-varying characteristics, the linear control design with better control performance has to execute a complex and time consuming procedure. To reduce this difficulty and raise robustness of system under the occurrence of the uncertainties, a hybrid recurrent Laguerre-orthogonal-polynomial Neural network (NN) control system which has online learning ability to respond to the system's nonlinear and time-varying behavior is proposed in this study. This control system consists of an inspector control system, a recurrent Laguerre- orthogonal-polynomial NN control with adaptive law and a recouped control with estimated law. Moreover, the adaptive law of online parameter in the recurrent Laguerre-orthogonal-polynomial NN is derived using Lyapunov stability theorem. Two optimal learning rates of the parameters based on modified Particle swarm optimization (PSO) are proposed to achieve fast convergence. Finally, to verify the effectiveness of the proposed control scheme, comparative studies are demonstrated by experimental results. [ABSTRACT FROM AUTHOR]

The composite recurrent Laguerre orthogonal polynomials neural network (NN) control system using altered particle swarm optimization (PSO) is developed for controlling the V-belt continuously variable transmission (CVT) system driven by permanent magnet synchronous motor to obtain better control performance. The simplified dynamic and kinematic models of a V-belt CVT system are derived by law of conservation. The 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 altered 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. [ABSTRACT FROM AUTHOR]

Because of unknown nonlinear and time-varying characteristics of V-belt continuously variable transmission (CVT)-driven electric scooter by using permanent magnet synchronous motor (PMSM) servo drive system, all gains tuning process for linear controller is a very time-consuming task. A hybrid modified recurrent Legendre neural network (NN) control system, which consists of an inspector control, a hybrid modified recurrent Legendre NN control and a recouped control with estimation law, is proposed for controlling the V-belt CVT-driven electric scooter under the occurrence of the nonlinear load disturbances and the variation of parameters to acquire better control performance. Moreover, the online parameters tuning method of the modified recurrent Legendre NN is based on Lyapunov stability theorem and gradient descent method. Furthermore, the two optimal learning rates of the hybrid modified recurrent Legendre NN control system are derived according to discrete Lyapunov function to enhance convergence speed. The proposed control scheme is capable of responding to system's nonlinear and time-varying behaviors due to online learning ability. Finally, some experimental results are verified to show that the effectiveness of the proposed hybrid modified recurrent Legendre NN control system controlled the V-belt CVT-driven electric scooter by using PMSM servo drive system. [ABSTRACT FROM AUTHOR]

Because permanent magnet synchronous generator (PMSG) system driven by permanent magnet synchronous motor (PMSM) based on wind turbine emulator (WTE) is a nonlinear and time-varying system with high complication, an accurate dynamic model of the PMSG system directly driven by WTE is difficult to establish for the linear controller design. In order to conquer this difficulty and improve the robustness of dynamic system, the PMSG system controlled by the online-tuned parameters of the novel modified recurrent wavelet neural network (NN)-controlled system is proposed to control output voltages and powers of controllable rectifier and inverter in this study. First, a closed-loop PMSM-driven system based on WTE is designed for driving the PMSG system to generate output power. Second, the rotor speeds of the PMSG, the voltages, and currents of the two power converters are detected simultaneously to yield maximum power output. In addition, two sets of the online-tuned parameters of the modified recurrent wavelet NN controllers in the controllable rectifier and inverter are developed for the voltage-regulating controllers in order to improve output performance. Finally, some experimental results are verified to show the effectiveness of the proposed novel modified recurrent wavelet NN controller for the power output of the PMSG system driven by WTE. [ABSTRACT FROM AUTHOR]

Due to nonlinear uncertainties of the electric scooter such as nonlinear friction force of the transmission belt and clutch, these will lead to degenerate tracking responses in command current and speed of the permanent magnet synchronous motor (PMSM) servo-driven electric scooter. In this study a novel hybrid recurrent wavelet neural network (HRWNN) control system is proposed to raise robustness of the PMSM servo-driven electric scooter under the occurrence of the variation of rotor inertia and load torque disturbance. First, the field-oriented mechanism is applied to formulate the dynamic equation of the PMSM servo drive. Then, a novel HRWNN control system is proposed to control motion for a PMSM servo-driven electric scooter. The HRWNN control system composed of a supervisor control, a RWNN and a compensated control with adaptive law. The online parameter training methodology with adaptive law in the RWNN is derived based on the Lyapunov stability theorem. Then adaptive law of the parameter in the RWNN can be updated by using the gradient descent method and the backpropagation algorithm. Finally, the effectiveness of the proposed control scheme is verified by experimental results. [ABSTRACT FROM AUTHOR]

The Editor-in-Chief has retracted this article. [ABSTRACT FROM AUTHOR]

This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1007/s40815-021-01067-0. [ABSTRACT FROM AUTHOR]

The Editor-in-Chief is issuing an Editorial Expression of Concern for this Article. [ABSTRACT FROM AUTHOR]

This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s12206-020-1243-8 [ABSTRACT FROM AUTHOR]