Sensorless Energy Conservation Control for Permanent Magnet Synchronous Motors Based on a Novel Hybrid Observer Applied in Coal Conveyer Systems

The estimation and control of the rotor position for sensorless permanent magnet synchronous motor (PMSM) drives based on extended Kalman filter (EKF) and artificial neural network (ANN) is presented in this paper. The EKF is a rotor position estimator which is a full-order stochastic observer for the recursive optimum state estimation of a nonlinear dynamic system in real time by using signals that are in the noisy environment. An ANN constructed by radial basis function neural network (RBFNN) and a parameter adjustable mechanism is applied to speed control loop of the PMSM drives to cope with the effect of the system dynamic uncertainty and external load. In this paper, firstly, a mathematical model for PMSM is derived, and a sensorless FOC is built up. Secondly, the rotor position and rotor speed which are estimated by using EKF is described. These estimated values are feed-backed to the current loop for FOC and to the speed loop for RBFNN-based self-tuning PI control. Thirdly, a very high-speed IC hardware description language (VHDL) is presented to describe the behavior of the adopted control and estimation algorithm. Fourthly, to verify the correctness of the designed VHDL code of the control and the estimation algorithm, based on electronic design automation (EDA) simulator link, a co-simulation work is constructed by Simulink and ModelSim. And some simulation results verify the correctness and effectiveness. Finally, an experimental system with a PMSM, a motor driver circuit, and a field programmable gate array (FPGA) board are set up to implement the proposed rotor position estimation and speed control algorithm.

In this paper, by measuring the phase voltages and currents of the permanent magnet synchronous motor (PMSM) drive, a neural-network-based rotor position and speed estimation method for PMSM is described. The proposed estimator includes two recurrent neural networks, one is used to estimate rotor speed and rotor position, and the other is used to estimate stator current. Through using an improved recursive prediction error algorithm, on-line adaptative estimation is realized. The simulation results show that the proposed approach gives a good estimation of rotor speed and position. Especially, the proposed approach has low sensitivity to perturbations of the mechanical parameters and torque disturbances.

This paper presents an algorithm for estimating speed and rotor position in permanent magnet synchronous motors (PMSMs). Stator voltage and current measurements are used to build a nonlinear reduced-order observer. Back electromotive forces (EMFs) are estimated first, and then rotor position and speed are reconstructed from the estimated EMFs. The observer is implemented and tested with a commercial permanent magnet ac drive (PMACD). The experimental results show that the observer gives a good estimation of speed and rotor position over a wide speed range.

This paper deals with the derivation of the permanent magnet synchronous motor (PMSM) model in alpha-beta coordinate system. The exact model of PMSM is essential for rotor position and speed estimation algorithms. Information about the rotor position is necessary even to be able to control drive speed. Extended Kalman filter (EKF) was used to estimate the rotor position. Conventional algorithms based on EKF are developed using a simple model of permanent magnet synchronous motor (L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</sub> = L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> = L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sub> ), thereby they provide bad performance in the low speeds. Extended mathematical performance in the (L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</sub> ≠ L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> ≠ L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sub> ) makes possible for EKF to operate in low speeds, because information about rotor position is contained in changes of stator inductance.

Permanent Magnet Synchronous Motors (PMSMs) are widely used mainly due to their high torque per volume, high efficiency and low maintenance cost, among other advantages. To perform the vector control on rotor speed and stator currents, the feedback of those variables is necessary, which can be done directly or by estimation. Measuring rotor position and speed directly requires the use of a mechanical device attached to the motor shaft, increasing the drive system volume and its maintenance cost. To overcome such disadvantages, many sensorless methods for speed estimation have been proposed. Among those methods, various strategies based on Artificial Neural Networks (ANNs) can be found. This paper presents a back-electromotive force estimator based on Fully Connected Cascade ANNs (FCC-ANNs). From the estimator, rotor position and speed can be obtained. Simulation and experimental results using automatically generated C code functions for the FCC-ANNs using fixed point notation provided rotor position estimation with simple implementation.

Sensorless rotor position estimation of PMSM for low and high rotor speed

ABSTRACTThis paper proposes a new method to detect the initial rotor position at standstill of permanent magnet synchronous motor (PMSM). To estimate rotor position and rotor speed from the back electromotive force (EMF) voltage, we apply sensorless speed control based on sliding-mode observer (SMO). The initial rotor position is detected by using a suitable high-frequency sequence of voltage pulses intermittently applied to the stator windings at standstill. With this approach, we managed to minimise the error on the estimated position to 3.75° electrical degrees without additional materials and uncomplicated calculations. The stability of the proposed SMO was verified using the Lyapunov method. Numerical simulations and experiments demonstrate that the novel SMO method can effectively estimate rotor position and speed with achievement of good static and dynamic performance. The experimental implementation is carried out on powerful dSpace DS1103 controller board based on the digital signal processor (DSP) TMS320F240.

This paper proposes an adaptive full-order Sliding Mode Observer (SMO) for the rotor speed and position estimation in the Permanent Magnet Synchronous Generator (PMSG). A full-order terminal SMO is designed to make the estimated stator current track the actual stator current, and eliminate the chattering. Furthermore, A back Electro Motive Force (EMF) observer adopts adaptive control to estimate the back EMF. Thus the rotor speed can be estimated from the adaptive law, and the rotor position can be calculated by the back EMF. The design of the adaptive back EMF observer further reduced the chattering. Compared simulation results are presented to validate the proposed method.

A robust observer based sensorless control method for the permanent magnet synchronous motor (PMSM) is proposed in this paper. First, the robust observer is proposed to estimate the back electromotive force (EMF) of the PMSM regardless of the uncertainty. Second, the stability of the proposed robust observer is analyzed by using the Lyaunov theory. Third, by the estimated back EMF, the phase locked loop (PLL) based approach is proposed to calculate the rotor position and speed of the PMSM. Finally, the simulation is conducted to verify the effectiveness of the proposed method. Compared with the traditional sliding mode observer (SMO), the proposed robust observer approach has the better system performance for the estimation of rotor position and speed, while it can effectively eliminate the chattering of the observer.

The observability analysis is presented and a combining method applying extended Kalman filter (EKF) and high frequency injection (HFI) method is proposed to realize the estimation of rotor speed and position at standstill and in normal situation for permanent magnet synchronous machine (PMSM). Considering the observability of start-up situation, a surface-mounted permanent magnet synchronous machine (SPMSM) model is developed using vector control method, then the high frequency injection methods are carried out to estimate the rotor speed and position accurately at standstill, while the extended Kalman filter algorithm is applied to validate the observability. Simulation results are shown that the proposed method can estimate the rotor position and speed accurately.

When the permanent magnet synchronous motor is operated at a low speed. The rotor position and speed are very difficult to estimate using the extended flux or back EMF method. A novel modified current slope estimating method is used to estimate the rotor position and speed in low speed in this paper. The mathematical models of an interior permanent magnet synchronous motor (IPMSM) are deduced. The basic principle of modified current slope method is introduced. The simulation control system is built based on Matlab and a TMS320LF2407 digital signal processor is used to execute the rotor position and speed estimation. The experimental and simulation results have shown that the rotor position and speed can be accurately estimated in a low-speed operating region.

By using the observed stator voltages and currents, a method to estimate the rotor speed and position of permanent magnet synchronous motor (PMSM) based on extended Kalman filter was proposed. Then the simulations and preliminary experiment for the proposed observer of extended Kalman filter were designed and implemented. The results showed that the method can track the rotor speed and position accurately, even though the system noise and measurement noise existed in the control system. Moreover, the method is possible to estimate the speed and rotor position and implement PMSM drives without position and speed sensors.

Mechanical model is generally required in high dynamic sensorless motor control schemes for zero phase lag estimation of rotor position and speed. However, the rotational inertia uncertainty will cause dynamic estimation errors, eventually resulting in performance deterioration of the sensorless control system. Therefore, this article proposes a high dynamic performance sensorless control strategy with online adjustment of the rotational inertia. Based on a synthetic back electromotive force model, the voltage equation of interior permanent magnet synchronous motor is transformed to that of an equivalent non-salient permanent magnet synchronous motor. Then, an extended nonlinear observer is designed for interior permanent magnet synchronous motor in the stator-fixed coordinate frame, with rotor position, speed and load torque simultaneously estimated. The effect of inaccurate rotational inertia on the estimation of rotor position and speed is investigated, and a novel rotational inertia adjustment approach that employs the gradient descent algorithm is proposed to suppress the dynamic estimation errors. The effectiveness of the proposed control strategy is demonstrated by experimental tests.

Hall sensors are commonly used to detect rotor position information in permanent magnet synchronous motors (PMSM). However, due to the low resolution of Hall sensors, the speed signal directly collected by the motor during rotation contains significant random errors and noise. Using this signal directly may increase the error and jitter in the rotor position estimation, thereby affecting the control performance of the system. This paper proposes a novel position estimation method that combines the Kalman filter and phase-locked loop (PLL) to precisely obtain the rotor position. In the proposed method, to suppress the noise and errors of the speed signal, the state and observation equations are established using the Kalman filter algorithm. Additionally, to enhance the precision of the rotor position estimation, the position obtained by integrating the speed from the Kalman filter is processed using the PLL algorithm, and the PLL algorithm parameters are dynamically corrected. To verify the feasibility and accuracy of the proposed speed and position estimation method, simulations and experiments are performed, respectively. By adopting the proposed method, the speed error is reduced by 30% to 50%, and the current harmonic component is reduced by about 48.7%, which effectively improves the accuracy of the rotor position estimation.

This paper describes a new method of rotor position and speed estimation of a surface permanent-magnet synchronous motor based on a model reference adaptive system (MRAS). The proposed method features the MRAS in a current control loop to estimate a rotor position and speed only with current sensors. A transient response of the estimated rotor position and speed in the MRAS determines transient behavior of the sensorless control. For this reason, the important point is to investigate a relationship between an actual and the estimated rotor speed. First, this paper proposes a structure of the sensorless control applied in the current control loop. Next, it proves the stability of the proposed method using hyperstability theory, and presents an optimal method of adjusting the transient behavior of the estimated rotor speed to that of the actual rotor speed with nonlinear compensation. Finally, several experimental results show the performance of the proposed method.