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Summary: Due to air‐gap field harmonic, cogging torque, stator's current time harmonic, and the influence of flux saturation, a six‐phase copper rotor induction motor (SCRIM) drive system has highly nonlinear uncertainties. Thus, the linear control method for the SCRIM drive system is difficult to achieved good performance under the nonlinear uncertainty action. To obtain better control performance, the adaptive backstepping control system using switching function is firstly proposed for controlling the SCRIM drive system to overcome the uncertainty influence. With the proposed control system, the SCRIM drive system holds in robustness to these uncertainties for the tracking of periodic reference trajectories. To enhance the robustness of the SCRIM drive system, the adaptive 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 adaptive backstepping control system using mended recurrent Romanovski polynomials neural network with reformed particle swarm optimization (PSO) is thus proposed to estimate the lumped uncertainty and to compensate estimated error for obtaining better control performance. Furthermore, two variable learning rates of the weights in the mended recurrent Romanovski polynomials neural network are adopted by using reformed PSO to speed up parameter's convergence. Finally, some experimental results with comparative control performances are demonstrated, and then, the effectiveness of proposed control system with better control performance is verified for the position tracking of periodic reference inputs. [ABSTRACT FROM AUTHOR]

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. [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 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. [ABSTRACT FROM AUTHOR]

Because of the unknown nonlinearity and time-varying characteristics of a V-belt continuously variable transmission (CVT) driven electric scooter system using a permanent magnet synchronous motor (PMSM) servo drive, its accurate dynamic model is difficult to establish for the design of the linear controller in the whole system. In order to conquer this difficulty and increase robustness, a novel adaptive modified recurrent Legendre neural network (NN) control system is proposed for controlling a PMSM servo-driven electric scooter with a V-belt CVT system. The novel adaptive modified recurrent Legendre NN control system consists of a modified recurrent Legendre NN control with adaptation law and a compensated control with estimation law. Additionally, the online parameter tuning methodology of the modified recurrent Legendre NN control and the estimation law of the compensated control can be derived by using the Lyapunov stability theorem. Furthermore, two optimal learning rates of the modified recurrent Legendre NN are proposed according to a discrete-type Lyapunov function in order to raise the speed of convergence. Finally, comparative studies are provided by experimental results in order to show the effectiveness of the proposed control scheme. [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]

Highlights: [•] The recurrent modified Elman NN is to control the three-phase PMSG system. [•] A PMSM drive system is designed to generate the maximum power for the PMSG system. [•] The online training recurrent modified Elman NN is developed for the controller. [•] The recurrent modified Elman NN controller improves the voltage performance. [Copyright &y& Elsevier]

The two recurrent modified Elman neural networks (NNs) controlled permanent magnet (PM) synchronous generator system based on wind turbine emulator is proposed to regulate output voltages of rectifier and inverter in this study. First, the wind turbine emulator, which adopts a closed-loop PM synchronous servo motor drive system to act as prime machine, is designed to drive the PM synchronous generator system to yield the maximum power at different wind speeds. Then, the rotor speed of the PM synchronous generator, the DC bus voltage, and current of the rectifier are detected simultaneously to yield maximum DC power of the rectifier through DC bus power control. Moreover, one online training recurrent modified Elman NN controller is developed to regulate DC bus voltage in the output end of rectifier and another online training recurrent modified Elman NN controller is developed to regulate AC power in the output end of inverter in order to improve the control performance. Finally, some experimental results are verified to show the effectiveness of the proposed recurrent modified Elman NN controlled PM synchronous generator system based on wind turbine emulator. [ABSTRACT FROM AUTHOR]

Presents information on a study which described an integral-proportional controller with on-line gain-tuning using a recurrent fuzzy neural network for controlling the mover position of a permanent magnet linear synchronous motor (PMLSM) servo drive system. Modeling of PMLSM; Basic control approach of a PMLSM servo drive.