Adaptive Gain Tuning Rule for Nonlinear Sliding-mode Speed Control of Encoderless Three-phase Permanent Magnet Assisted Synchronous Motor

In this paper, an adaptive gain tuning rule is designed for the nonlinear sliding mode speed control(NSMSC) in order to enhance the dynamic performance and the robustness of the permanent magnet assisted synchronous reluctance motor(PMa-Syn RM) with considering the parameter uncertainties. A nonlinear sliding surface whose parameters are altering with time is designed at first. The proposed NSMSC can minimize the settling time without any overshoot via utilizing a low damping ratio at starting along with a high damping ratio as the output approaches the target set-point. In addition, it eliminates the problem of the singularity with the upper bound of an uncertain term that is hard to be measured practically as well as ensures a rapid convergence in finite time, through employing a simple adaptation law. Moreover, for enhancing the system efficiency throughout the constant torque region, the control system utilizes the maximum torque per ampere technique. The nonlinear sliding surface stability is assured via employing Lyapunov stability theory. Furthermore, a simple sliding mode estimator is employed for estimating the system uncertainties. The stability analysis and the experimental results indicate the effectiveness along with feasibility of the proposed speed estimation and the NSMSC approach for a 1.1-k W PMa-Syn RM under different speed references, electrical and mechanical parameters disparities, and load disturbance conditions.

Improving Performance of Recurrent Neural Networks Using Simulated Annealing for Vertical Wind Speed Estimation

An accurate vertical wind speed(WS)data estimation is required to determine the potential for wind farm installation.In general,the vertical extrapolation of WS at different heights must consider different parameters fromdifferent locations,such as wind shear coefficient,roughness length,and atmospheric conditions.The novelty presented in this article is the introduction of two steps optimization for the Recurrent Neural Networks(RNN)model to estimate WS at different heights using measurements from lower heights.The first optimization of the RNN is performed to minimize a differentiable cost function,namely,mean squared error(MSE),using the Broyden-Fletcher-Goldfarb-Shanno algorithm.Secondly,the RNN is optimized to reduce a non-differentiable cost function using simulated annealing(RNN-SA),namely mean absolute error(MAE).Estimation ofWS vertically at 50 m height is done by training RNN-SA with the actualWS data a 10–40 m heights.The estimatedWS at height of 50 m and the measured WS at 10–40 heights are further used to train RNN-SA to obtain WS at 60 m height.This procedure is repeated continuously until theWS is estimated at a height of 180 m.The RNN-SA performance is compared with the standard RNN,Multilayer Perceptron(MLP),Support Vector Machine(SVM),and state of the art methods like convolutional neural networks(CNN)and long short-term memory(LSTM)networks to extrapolate theWS vertically.The estimated values are also compared with realWS dataset acquired using LiDAR and tested using four error metrics namely,mean squared error(MSE),mean absolute percentage error(MAPE),mean bias error(MBE),and coefficient of determination(R2).The numerical experimental results show that the MSE values between the estimated and actualWS at 180mheight for the RNN-SA,RNN,MLP,and SVM methods are found to be 2.09,2.12,2.37,and 2.63,respectively.

Estimation of Vehicle Speed Based on Wheel Speeds from ASR System in Four-Wheel Drive Vehicles

Three major methods currently in the use of determining vehicle speed based on wheel speeds,the minimum wheel speed,minimum wheel speed corrected by slope method and the Kalman filter method,are analyzed,with merits and defects of each approach stated. Through simulations,the Kalman filter method based on minimum wheel speed shows improved accuracy,in addition to better adaptivity to vehicle reference speed. It also can be used to acceleration ship regulation (ASR) in part-time four-wheel drive vehicles.

Speed Estimation Using Commercial Wi-Fi Device in Smart Home

With the development of Internet of Things(IoT),the speed estimation technology has attracted significant attention in the field of indoor security,intelligent home and personalized service.Due to the indoor multipath propagation,the speed information is implicit in the motion-induced reflected signal.Thus,the wireless signal can be leveraged to measure the speed of moving target.Among existing speed estimation approaches,users need to either carry a specialized device or walk in a predefined route.Wi-Fi based approaches provide an alternative solution in a device-free way.In this paper,we propose a direction independent indoor speed estimation system in terms of Electromagnetic(EM)wave statistical theory.Based on the statistical characteristics of EM waves,we establish the deterministic relationship between the Autocorrelation Function(ACF)of Channel State Information(CSI)and the speed of a moving target.Extensive experiments show that the system achieves a median error of 0.18 m/s for device-free single target walking speed estimation.

Sensored and sensorless scalar-control strategy of a wind-driven BDFRG for maximum wind-power extraction

This paper presents a scalar volt per hertz(V/f)control technique for maximum power tracking of a grid-connected wind-driven brushless doubly fed reluctance generator(BDFRG).The proposed generator has two stator windings namely;power winding,directly connected to the grid and control winding,connected to the grid through a bi-directional converter.In order to enhance the performance of the proposed scalar-control strategy,a soft starting method is suggested to avoid the over-current of the bi-directional converter.Moreover,the capability of generator speed estimation for sensorless control is also studied.The capability of the proposed scalar-control technique is validated using a sample of simulation results.In addition,the presented simulation results ensure the effectiveness of the proposed control strategy for maximum wind-power extraction under windspeed variations.Furthermore,the results show that the estimated generator speed is in a good accordance with the actual generator speed which supports sensorless control capability.

Sliding mode approach applied to sensorless direct torque control of cage asynchronous motor via multi-level inverter

To improve the robustness and performance of the dynamic response of a cage asynchronous motor,a direct torque control(DTC)based on sliding mode control(SMC)is adopted to replace traditional proportional-integral(PI)and hysteresis comparators.The combination of the proposed strategy with sinusoidal pulse width modulation(SPWM)applied to a three-level neutral point clamped(NPC)inverter brings many advantages such as a reduction in harmonics,and precise and rapid tracking of the references.Simulations are performed for a three-level inverter with SM-DTC,a two-level inverter with SM-DTC and the three-level inverter with PI-DTC-SPWM.The results show that the SM-DTC method achieves better performance in terms of reference tracking,while adoption of the threelevel inverter topology can effectively reduce the ripples.Applying the SM-DTC to the three-level inverter presents the best solution for achieving efficient and robust control.In addition,the use of a sliding mode speed estimator eliminates the mechanical sensor and this increases the reliability of the system.