Motor-load System Research Articles

Research on a hybrid controller combining RBF neural network supervisory control and expert PID in motor load system control

Considering the contradiction among the response speed, overshoot and stability of system when the motor load system adopts PID control, a control strategy combining RBF (Radial Basis Function) neural network supervisory control and expert PID control is designed to effectively improve this problem in this paper. First of all, the related algorithms of RBF neural network supervisory control composed of RBF neural network and PID control (RSC-PID) is introduced. This method can make the motor load system reach a steady state faster than simple PID control. But RSC-PID is also unsatisfactory in terms of overshoot. Based on the RSC-PID control method, a hybrid controller combining RBF neural network supervisory control and expert PID control (RSC-EPID) is proposed. This method combines RSC-PID control theory with expert PID control ideas, further improves the stability and rapidity of the system, reduces the overshoot. Moreover, when the input signal is a time-varying signal with interference, the motor load system shows better anti-interference performance after using RSC-EPID. The simulation results show that RSC-EPID control improves the tracking effect of the output signal of the motor load system, ensures the stability of the system, and improves the performance of the system.

Control of chaotic advection for mixing system using universal motor

This study presents a method of chaotification of universal motor drive by selecting appropriate topology of the power circuit and suitably choosing values of circuit components for mixing application. The purpose of chaotification is to improve homogeneity in the mixture and to reduce consumption of time, cost and energy. In particular, the dynamic behaviour of a pulse-width modulation controlled chopper fed universal motor drive with diode bridge rectifier at the source side has been explored for the occurrence of chaos in the mixer motor. Performance of the drive has been investigated with two different topologies of the filter circuit. The first topology uses a capacitor filter after diode rectifier and the other includes an LC filter at the motor terminals along with source-side capacitor filter. A systematic methodology for selecting appropriate values of the filter circuit components has been proposed for imparting chaotic motion to the motor load system. A comparative study showing a range of load torque and speed for which sub-harmonic or chaotic speed oscillation occurs in the motor for both the topologies of filter circuit has also been presented. Computer simulations and results of laboratory experiment performed on a 0.5 hp, 1800-rpm universal motor validate the proposed design method of the motor drive for the better mixing process.

Vibration Analysis of Motor Stator

Motor vibration is one of the main causes of the motor noise, and the vibration of the motor also brings great harm for the safe and reliable operation of the motor load system. The vibration and noise level is one of the essential elements in evaluating the electrical machines quality. Accurate calculation of the stator natural frequencies and harmonic response of an electrical machine is essential to the prediction of the vibration. This paper takes the common Y series induction motors as the sample. The motor is a complex electromechanical coupling system, which includes stator and rotor. The finite element analysis is used to study on the stator modal shapes and natural frequencies of the induction motor. Then this paper calculates the electromagnetic force by using the finite element method, and gets the spectrum distribution of the radial electromagnetic force. Thus it provides a good method for the motor design by predicting natural frequency and optimizing the motor parameters to avoid resonance. All of them provide theory fundamental basis and technical support for reducing vibration and noise of the motor.

RBF Neural Network Approach for Identification and Control of DC Motors

In this paper, a neural network approach for the identification and control of a separately excited direct (DC) motor (SEDCM) driving a centrifugal pump load is applied. In this application, two radial basis function neural networks (RBFNN) are used: The first is a RBFNN identifier trained offline to emulate the dynamic performance of the DC motor-load system. The second is a RBFNN controller, which is trained to make the motor speed follow a selected reference signal. Two RBFNN control schemes are proposed using direct inverse and internal model control schemes. The performance of the RBFNN identifier and controller is investigated in terms of step response, sharp changes in speed trajectory, and sudden load change, as well as changes in motor parameters. The performance of RBFNN in system identification and control has been compared with the performance of the well-known back-propagation neural network (BPNN). The simulation results show that both of the BPNN and RBFNN controllers exhibit excellent dynamic response, adapt well to changes in speed trajectory and load connected to the motor, and adapt to the variations of motor parameters. Furthermore, the simulation results show that the step response of RBFNN internal model and direct inverse controllers are identical.

Self-organizing map approach for classification of mechanical and rotor faults on induction motors

Two neural network-based schemes for fault diagnosis and identification on induction motors are presented in this paper. Fault identification is performed using self-organizing maps neural networks. The first scheme uses the information of the motor phase current for feeding the network, in order to perform the diagnosis of load unbalance and shaft misalignment faults. The network is trained using data generated through the simulation of a motor-load system model, which allows including the effects of load unbalance and shaft misalignment. The second scheme is based on the motor’s active and reactive instantaneous powers, in order to detect and diagnose faults whose characteristic frequencies are very close each other, such as broken rotor bars and oscillating loads. This network is trained using data obtained through the experimental measurements. Additional experimental data are later applied to both networks in order to validate the proposal. It is demonstrated that the proposed strategies are able to correctly identify, both unbalanced and misaligned load, as well as broken bars and low-frequency oscillating loads, thus avoiding the need for an expert to perform the task.

Are my motors oversized?

Three-phase induction motors accounts with an important share of the electricity consumption in industrialized countries. Therefore, the misapplication of such machines conducts to efficiency reduction and large amounts of energy waste. Motor oversize is one of the most frequently misapplication encountered and difficult to be fixed. The paper presents a road map to evaluate whether a direct line fed three-phase induction motor is oversized, considering not only its steady loading, but also its dynamic behavior. In addition, case studies are presented and some solutions to increase the energy efficiency of the whole motor–load system are proposed.

Implementation of Fixed-Point PID Controller Based on the Modified Delta Operator and Form for Autonomous Robots

There are power/size and price/cost constraints in realizing autonomous robots such as humanoid robots. To meet these specifications, fixed-point microprocessors with a short word-length is suitable for the control. The modified delta form for fixed-point microprocessors was previously proposed. Considering that many autonomous robots are controlled by PID-like controllers and DC motors, this paper presents the procedure for applying the implementation methodology to a PID-like controller. As an illustration, a PID-like control of a DC Motor-load system is considered. The experimental results showing the effectiveness are presented.

Application of the Neural Network for a Hydraulic Motor/Load System (3rd Report, An Improvement of Steady State Error Using Dither)

The purpose of this study is to develop a precise angular position control system of the hydraulic motor with undesirable dead zone using a Neural Network. In the previous works, we showed that the state feedback Neural Network control has a good effect on improving position control accuracy and robustness, and then, it was confirmed that the application of the PWM signal to the Neural Network position control system attains a very small angular positioning. 0n the other hand, it is well known that one method for eliminating the dead zone characteristics is to superimpose the dither signal into the input current of the servovalve. ln this study, the effective method using a dither signal together with the on-line training of the Neural Network is proposed in order to achieve precise positioning performance. Amplitude and frequency of the dither signal are examined by analyzing a linearized model of the hydraulic motor load system. From the computer simulation and experiments, it is recognized that the dither effect improve the steady performance of Neural Network based position control system.

Detailed modelling and estimation of practical robotic parameters for precision control

A rudimentary motor-load system is used to derive complex models of robotic systems. This economic manipulator displays all the fundamental nonlinear characteristics of a standard industrial robot. The manipulator has several advantages over its more costly rival, accurate values of inertia are easily assigned, and as a result secondary features are easily isolated. For the real system the physical characteristics of the electrical hardware, are modelled and then the actuator and mechanical transmission effects determined. Complex friction parameters of the system are then estimated; these allow the development of friction models and make possible friction compensation for control. Other practical considerations such as real-time parameter estimation are examined.

Position control of a brushless DC motor without velocity measurements

The paper presents a rotor position-velocity tracking controller for a brushless DC motor-load system without velocity measurement. An 'observed integrator backstepping' procedure and a nonlinear rotor velocity observer are utilised to design a nonlinear tracking controller which guarantees globally exponentially stable position-velocity tracking. The proposed controller is successfully implemented on a brushless DC motor-load system.

NEMA Standards for Induction Motors and Their Application to the Cement Industry

Familiarity with NEMA standards and the influence of the power supply on the motor-load system permits the user to make the necessary decision as to whether a standard or special motor is needed for his particular application. This paper highlights the more important aspects of NEMA standards for ac induction motors from the application engineer's standpoint and the effect of the power supply and the load on motor characteristics.