Chih-Hong Lin

Self-constructing fuzzy neural network speed controller for permanent-magnet synchronous motor drive

A self-constructing fuzzy neural network (SCFNN) which is suitable for practical implementation is proposed. The structure and the parameter learning phases are performed concurrently and online in the SCFNN. The structure learning is based on the partition of input space and the parameter learning is based on the supervised gradient decent method using a delta adaptation law. Several simulation and experimental results are provided to demonstrate the effectiveness of the proposed SCFNN control stratagem with the implementation of a permanent-magnet synchronous motor speed drive. Moreover, the simulation results of time varying and nonlinear disturbances are given to show the dynamic characteristics of the proposed controller over a broad range of operating conditions.

A permanent-magnet synchronous motor servo drive using self-constructing fuzzy neural network controller

A self-constructing fuzzy neural network (SCFNN) is proposed to control the rotor position of a permanent-magnet synchronous motor (PMSM) drive to track periodic step and sinusoidal reference inputs in this study. The structure and the parameter learning phases are preformed concurrently and online in the SCFNN. The structure learning is based on the partition of input space, and the parameter learning is based on the supervised gradient descent method using a delta adaptation law. Several simulation and experimental results are provided to demonstrate the effectiveness of the proposed SCFNN control stratagem under the occurrence of parameter variations and external disturbance.

Decoupled stator-flux-oriented induction motor drive with fuzzy neural network uncertainty observer

A stator-flux-oriented induction motor drive using online rotor time-constant estimation with a robust speed controller is introduced in this paper. The estimation of the rotor time constant is made on the basis of the model reference adaptive system using an energy function. The estimated rotor time-constant is used in the current-decoupled controller, which is designed to decouple the torque and flux in the stator-flux-field-oriented control. Moreover, a robust speed controller, which is comprised of an integral-proportional speed controller and a fuzzy neural network uncertainty observer, is designed to increase the robustness of the speed control loop. The effectiveness of the proposed control scheme is demonstrated by simulation and experimental results.

Incremental motion control of linear synchronous motor

In this study a particular incremental motion control problem, which is specified by the trapezoidal velocity profile using multisegment sliding mode control (MSSMC), is proposed to control a permanent magnet linear synchronous motor (PMLSM) servo drive system. First, the structure and operating principle of the PMLSM are described in detail. Second, a field-oriented control PMLSM servo drive is introduced. Then, each segment of the multisegment switching surfaces is designed to match the corresponding part of the trapezoidal velocity profile, thus the motor dynamics on the specified-segment switching surface have the desired velocity or acceleration corresponding part of the trapezoidal velocity profile. In addition, the proposed control system is implemented in a PC-based computer control system. Finally, the effectiveness of the proposed PMLSM servo drive system is demonstrated by some simulated and experimental results.

On-line gain-tuning IP controller using RFNN

In this study an integral-proportional (IP) controller with on-line gain-tuning using a recurrent fuzzy neural network (RFNN) is proposed to control the mover position of a permanent magnet linear synchronous motor (PMLSM) servo drive system. The structure and operating principle of the PMLSM are first described in detail. A field-oriented control PMLSM servo drive is then introduced. After that, an IP controller with on-line gain tuning using an RFNN is proposed to control the mover of the PMLSM for achieving high-precision position control with robustness. The backpropagation algorithm is used to train the RFNN on line. Moreover to guarantee the convergence of tracking error for the periodic step-command tracking, analytical methods based on a discrete-type Lyapunov function are proposed to determine the varied learning rates of the RFNN. Furthermore, the proposed control system is implemented in a PC-based computer control system, Finally, the effectiveness of the proposed PMLSM servo drive system is demonstrated by some simulated and experimental results. Accurate tracking response and superior dynamic performance can be obtained due to the powerful on-line learning capability of the RFNN. In addition, the proposed on-line gain-tuning servo drive system is robust with regard to parameter variations and external disturbances.

Adaptive sliding-mode control for motor-toggle servomechanism

In this study, the dynamic responses of an adaptive sliding-mode controlled motor-toggle servo mechanism are described. The servomechanism is a toggle mechanism actuated by a permanent magnet (PM) synchronous servomotor. First, a newly-designed sliding-mode controller, which is insensitive to uncertainties including parameter variations and external disturbance, is introduced. Then, to overcome the two main problems with sliding-mode control, i.e., the assumption of known uncertainty bounds and the chattering phenomenon in the control effort, an adaptive sliding-mode controller is investigated to control the motor-toggle servomechanism. In the adaptive sliding-mode controller, a simple adaptive algorithm is utilized to adjust the uncertainty bounds in real-time. Simulated and experimental results due to periodic step and sinusoidal commands show that the dynamic behaviors of the proposed controllers are robust with regard to uncertainties.

On-line gain tuning using RFNN for linear synchronous motor

In this study an integral-proportional (IP) controller with on-line gain tuning using a recurrent fuzzy-neural-network (RFNN) is proposed to control a permanent magnet linear synchronous motor (PMLSM) drive system. First, the structure and operating principle of the PMLSM are described in detail. Second, an IP controller with gain-tuning using a RFNN is proposed to control the position of the moving table of the PMLSM achieve high-precision position control with robustness. The backpropagation algorithm is used to train the RFNN online. Then, an IP controller with gain tuning using a RFNN is implemented in a PC-based computer control system. Finally, the effectiveness of an IP controller with gain tuning using a RFNN controlled PMLSM drive system is demonstrated by some experimental results. Accurate tracking response and superior dynamic performance can be obtained due to the powerful online learning capability of the RFNN. Furthermore, an IP controller with gain tuning using a RFNN is robust with regard to parametric variations.

Adaptive fuzzy neural network control for motor-toggle servomechanism

In this study, the dynamic responses of an adaptive fuzzy neural network (FNN) controlled toggle mechanism is described. The toggle mechanism is driven by a permanent magnet (PM) synchronous servo motor. First, based on the principle of computed torque, an adaptive controller is developed to control the position of a slider of the motor-toggle servomechanism. Since the selection of control gain of the adaptive controller has a significant effect on the system performance, an adaptive FNN controller is proposed to control the motor-toggle servomechanism. In the proposed adaptive FNN controller, an FNN is adopted to facilitate the adjustment of control gain on line. Moreover, simulated and experimental results due to a periodic sinusoidal command show that the dynamic behaviors of the proposed adaptive and adaptive FNN controllers are robust with regard to uncertainties.

Variable-structure control for linear synchronous motor using recurrent fuzzy neural network

A newly designed variable-structure controller using recurrent fuzzy neural network (RFNN) to control the mover position of a permant magnet linear synchronous motor (PMLSM) servo drive is developed in this study. First, a variable-structure adaptive (VSA) controller is adopted to control the mover position of the PMLSM where a simple adaptive algorithm is utilized to estimate the uncertainty bounds. Then, to further improve the rate of convergence of the estimation, a variable-structure controller using RFNN is investigated, in which the RFNN is utilized to estimate the lumped uncertainty real-time. Simulated and experimental results show that the proposed variable-structure controller using RFNN provides high-performance dynamic characteristics and is robust with regard to plant parameter variations and external disturbance. Furthermore, comparing with the VSA controller, smaller control effort is resulted and the chattering phenomenon is reduced by the proposed variable-structure controller using RFNN.