Permanent Magnet Synchronous Servo Research Articles

Enhancement of Variable Speed Brushless DC Motor using Neural Network

Background/Objectives: Direct Current (DC) motor plays an important role, especially in the industry as it is used in various applications as consequences from its unique characteristic. However, some limitations of variable speed DC motor cause the application of DC motor become less effective whereas some available speed controllers have difficulties in controlling the motor speed. An improved design based on neural network controller for the Brushless Direct Current (BLDC) permanent magnet motor drives is introduced in this paper. The variable input speed is applied to the controller in order to maintain the BLDC motor output speed. Methods/Statistical Analysis: In the proposed enhanced scheme, the mathematical model of BLDC motor and a back propagation Artificial Neural Network (ANN) algorithm are considered and included to replace the conventional method of Proportional Integral Derivative (PID). The proposed ANN algorithm is based on the plant information, where the number of input and output neurons in each layer of the network are designed to be equivalent to the input and output number of signals of the proposed system. The architecture of the proposed ANN controller is designed to has a 2-3-1 structure of the network, where the two input neurons will exist in the input layer. For the first incoming neuron is the different signal among the desired and the real signal. Meanwhile, the second input neuron illustrates the differ between earlier error signal and the ongoing error signal. The network parameter is updated once the process for learning model is defined to ensure the ANN is appropriate to be applied in the proposed system and meet the motor features. Findings: From the analysis, the output error of the system effectively decreases once the training method is implemented where sufficient training dataset for the input-output mapping plant is significantly acquired. The analysis of overshoot, rise time and steady state error for the speed range between 1000 and 1500 rpm indicates that the proposed Neural Network Control (NNC) technique has successfully improved the performance of the BLDC motor drive and outperforms the conventional state-of-the-art methods. Applications/Improvements: The complete simulation model of BLDC motor using NNC controller has been developed. In order to make it more effective, the implementation of the simulation work into the real hardware to investigate the performance of BLDC motor become more interesting. Besides, the NNC controller can be implemented in other applications such as DC-DC converter, permanent magnet synchronous motor or servo motor.

Cogging Force Analysis of Moving Magnet Type Permanent Magnet Synchronous Linear Servo Motor

Linear motor eliminates the middle part of mechanical drive train, but because of structural characteristics of itself, must to take effective measures to reduce thrust fluctuations, or direct drive will lost its original meaning. In this paper, firstly research on detent force of moving magnet type permanent magnet synchronous linear servo motor and analyze the principle of generate detent force, secondly analyze the cogging force of moving magnet type permanent magnet synchronous linear motor and obtain the expression of cogging force maximum value, finally propose effective method to reduce detent force.

Design and Simulation of Permanent Magnet Synchronous Motor Servo System

In this paper, current loop and speed loop for analysis, design a permanent magnet synchronous motor PMSM servo system. The system does not need to establish a precise mathematical model, the interference can be generated during the operation of the system, the errors always compensated, fast response, with better dynamic characteristics and location tracking capabilities, Matlab simulation results verify the correctness of the system design.

Recurrent modified Elman neural network control of permanent magnet synchronous generator system based on wind turbine emulator

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.

Recurrent modified Elman neural network control of PM synchronous generator system using wind turbine emulator of PM synchronous servo motor drive

The recurrent modified Elman neural network (NN) controlled a permanent magnet (PM) synchronous generator system, which is driven by wind turbine emulator of a PM synchronous motor servo drive, is developed to regulate output voltage of rectifier (or AC to DC power converter) and inverter (or DC to AC power converter) in this study. First, the wind turbine emulator of a closed loop PM synchronous motor servo drive is designed to produce the maximum power for the PM synchronous generator system. Then, the rotor speed of the PM synchronous generator, the output DC bus voltage and current of the rectifier are detected simultaneously to yield maximum power output of the rectifier through DC bus power control. Because the PM synchronous generator system is a nonlinear and time varying dynamic system, the online training recurrent modified Elman NN control system is developed to regulate DC bus voltage of the rectifier and AC line voltage of the inverter in order to improve the control performance. Furthermore, the online training recurrent modified Elman NN control system with the variable learning rate is derived based on Lyapunov stability theorem, so that the stability of the system can be guaranteed. Finally, some experimental results are verified to show the effectiveness of the proposed recurrent modified Elman NN controlled PM synchronous generator system.

The Hardware System Design of PMSM Controller Based on DSP2812

For the efficiency of PMSM, in the paper the matched controller was developed to improve the servo performance of permanent magnet synchronous servo system. According to the basic principle of vector control for PMSM, the hardware system of PMSM controller was constituted based on TMS320F2812, it contains power drive circuit, control circuit, feedback circuit and other auxiliary circuits. It can provide reference scheme for the design of PMSM controller, and laid the foundation for the industrial production of the PMSM controller.

Total sliding-mode controller for PM synchronous servo motor drive using recurrent fuzzy neural network

In this paper, the dynamic responses of a recurrent-fuzzy-neural-network (RFNN) sliding-mode-controlled permanent-magnet (PM) synchronous servo motor are described. First, a newly designed total sliding-mode control system, which is insensitive to uncertainties, including parameter variations and external disturbance in the whole control process, is introduced. The total sliding-mode control comprises the baseline model design and the curbing controller design. In the baseline model design, a computed torque controller is designed to cancel the nonlinearity of the nominal plant. In the curbing controller design, an additional controller is designed using a new sliding surface to ensure the sliding motion through the entire state trajectory. Therefore, in the total sliding-mode control system, the controlled system has a total sliding motion without a reaching phase. Then, to overcome the two main problems with sliding-mode control, i.e., the assumption of known uncertainty bounds and the chattering phenomena in the control effort, an RFNN sliding-mode control system is investigated to control the PM synchronous servo motor. In the RFNN sliding-mode control system, an RFNN bound observer is utilized to adjust the uncertainty bounds in real time. To guarantee the convergence of tracking error, analytical methods based on a discrete-type Lyapunov function are proposed to determine the varied learning rates of the RFNN. Simulated and experimental results due to periodic step and sinusoidal commands show that the dynamic behaviors of the proposed control systems are robust with regard to uncertainties.

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.

Quick-Return Servomechanism With Adaptive Fuzzy Neural Network Control

The dynamic response of an adaptive fuzzy neural network (FNN) controlled quick-return mechanism, which is driven by a permanent magnet (PM) synchronous servo motor, is described in this study. The crank and disk of the quick-return mechanism are assumed to be rigid. First, Hamilton’s principle and Lagrange multiplier method are applied to formulate the mathematical model of motion. Then, based on the principle of computed torque, an adaptive controller is developed to control the position of a slider of the quick-return servomechanism. Moreover, 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 quick-return servomechanism. In the proposed adaptive FNN controller, an FNN is adopted to facilitate the adjustment of control gain on line. Simulated and experimental results due to periodic step and sinusoidal commands show that the dynamic behavior of the proposed adaptive FNN control system are robust with regard to parametric variations and external disturbances.

Fuzzy sliding mode controlled slider–crank mechanism using a PM synchronous servo motor drive

The dynamic response of a sliding mode controlled slider–crank mechanism, which is driven by a permanent magnet (PM) synchronous servo motor, is studied in this paper. The rod and crank are assumed to be rigid. The Hamilton’s principle and Lagrange multiplier method are applied to formulate the equation of motion. Reducing the differential-algebraic equations and employing the Runge–Kutta numerical method, the state variable representation is obtained. Moreover, based on the principles of the sliding mode control, a position controller is developed. Then, a simple fuzzy inference mechanism is utilized to estimate the upper bound of uncertainties for the sliding mode controller. Numerical results show that the dynamic behavior of the proposed controller–motor–mechanism system is robust to parametric variation and external disturbance.

Variable structure adaptive control for PM synchronous servo motor drive

A newly designed variable-structure controller for a permanent magnet (PM) synchronous servo motor drive, which is insensitive to uncertainties including parameter variations and external load disturbance, is introduced. To overcome the two main problems with variable-structure control, i.e. the assumption of known uncertainty bounds and chattering phenomena in the control effort, a variable-structure adaptive (VSA) controller is investigated. In the VSA controller a simple adaptive algorithm is utilised to estimate the uncertainty bounds; moreover, the chattering phenomenon is reduced. A variable-structure direct adaptive (VSDA) controller comprising the VSA control algorithm and a direct adaptation law is proposed to further improve the control performance of the variable-structure controller. The position control of a PM synchronous servo motor drive with the variable-structure control strategies is illustrated. Simulated and experimental results show that the developed controllers provide high-performance dynamic characteristics and are robust with regard to plant parameter variations and external load disturbance.

A toggle mechanism servo system with variable-structure model-following control

A toggle mechanism, which is driven by a permanent-magnet synchronous servo motor, with variable-structure model-following control (VSMFC) is proposed in this study. The rod and crank of the toggle mechanism are assumed to be rigid. The Hamilton principle and L agrange multiplier methods are applied to formulate the equation of motion. Based on the principles of the adaptive model-following control and the variable-structure control, a VSMFC system is developed to control the position of a slider of the toggle mechanism servo system. Numerical and experimental results show that the dynamic behaviour of the proposed controller-motor-mechanism system is robust to parametric variations and external disturbances.

A PM synchronous servo motor drive with an on-line trained fuzzy neural network controller

A permanent magnet (PM) synchronous servo motor drive with integral-proportional (IP) position controller and a proposed on-line trained fuzzy neural network (FNN) controller is introduced in this paper. First, an IP position controller is designed according to the estimated plant model to match the time-domain command tracking specifications. Then the resulting closed-loop tracking transfer function is used as the reference model, and an adaptive signal generated from the proposed FNN controller, whose membership functions and connective weights are trained on-line according to the model-following error of the states, is added to the control system to preserve a favorable model-following characteristics under various operating conditions.

Nonlinear control of synchronous servo drive

The complete control design for a permanent magnet synchronous (PMAC) motor, derived from the input-output linearization, is presented in the paper. The motor model, written in the rotor's d-q coordinates, is nonlinear with respect to the state variables and linear in the control. The input-output linearization makes it possible to write the motor's model in Brunovski decoupled canonical form which makes the synthesis of linear controllers possible. The proposed control structure allows perfect tracking of smooth references in the case of nominal parameters. In relation to the bounded parameter perturbations, the robustness of the feedback system is improved by introducing an additional compensation signal which assures the stability of the perturbed system in Lyapunov's sense. The influence of parameter variations that prevents exact compensation of the control plant nonlinearities was analyzed for the PMAC motor. A description of the laboratory setup is given, and the experimental results of the proposed PMAC servo drive control are presented.

Analysis and implementation of a real-time predictive current controller for permanent-magnet synchronous servo drives

A real-time current controller for PWM inverter-fed permanent-magnet synchronous motor drives is presented and analyzed. The proposed current control scheme is based on predictive control with a parallel integral loop added to compensate for the inaccuracy of the motor model and for the variations of motor parameters and DC voltage source. The proposed current control scheme is analyzed and its performance is evaluated by computer simulation. An EPROM-based implementation is presented in which calculations and pulsewidth modulation are executed by lookup tables resulting in high-speed operation. The controller performance is evaluated using a prototype l kW PM synchronous servo drive. Experimental results are given and discussed. >