A Microcontroller Sensorless Speed Control of a Direct Current Motor

In this paper a new speed and disturbance torque observer, applied to the induction machine with encoder, is proposed. In practical situations, when designing the control loop, the noise statistics needed for tuning a stochastic observer are generally unknown. However, the bounds of the process and measurement noises can be clearly defined, for example, if they are generated by a quantizer. The increased DSP computational power and new mathematical tools, like the interval arithmetic, allow us to calculate both the bounds of the estimation error and the observed states. A nonlinear optimization algorithm is used here to obtain the gain of the observer that minimizes the estimation error. Some experimental tests with an AC motor have been performed in order to compare the proposed observer with a state bounding algorithm.

Contact force and torque sensing for serial manipulator based on an adaptive Kalman filter with variable time period

According to the characteristics of the permanent magnet synchronous motor (PMSM) current model with strong nonlinearity, designing its rotor position and speed observer with the state covariance matrix of UFK updated by H∞ robust filtering, ensure the observation performance under adverse situation. To contrastingly analysis H∞ robust UFK, extended Kalman Filter and UFK when viewing motor position and speed error variance during noise and measurement noise process, the observed position value of UKF observer measured by four methods is mainly identical, the speed observation value of H∞ robust UFK observer is obviously less than the latter two, and the maximum in speed error of H∞ robust Kalman Filter observer is 5rad/min, variance is 3.45rad/min, the latter both values are close to 18rad/min and variances are 11.44rad/min.

This work presents a sensorless Maximum Torque per Ampere (MTPA) for the synchronous reluctance motor (SynRM) based on speed and torque observer. From the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\alpha \beta$</tex> mathematical model alongside with extended back-EMF model from the literature, a current and back-EMF observer is applied, allowing to estimation of the speed and position. Furthermore, a second speed observer is proposed allowing for a torque disturbance observer, generating the torque reference for MTPA algorithm. Moreover, the MTPA provides the synchronous reference currents which are controlled via Field Oriented Control. Simulation results demonstrate the effectiveness of the proposed scheme. Simulation results demonstrate the effectiveness of the proposed scheme.

In this paper, a speed-independent finite control set model predictive torque control for induction machine based on sliding mode observer is proposed. With this new method, the finite control set model predictive controller is designed as speed-independent. For the estimation of the flux, the sliding mode observer is applied. The sliding mode observer is designed as the speed-independent. In this way, predictive torque controller and observer are not dependent on the speed any more. The influence brought by speed error is eliminated. The work is developed with a 2-level voltage source inverter. The cost function is built to select the best switching vector for voltage source inverter. At every sampling period, all 7 different inverter switching vectors are taken into calculation. The best switching vectors could be confirmed by minimizing the cost function. The process and results are presented in the paper to verify the proposed strategy.

The growing global demand for household air conditioners necessitates reliable and efficient motor operation under high-temperature conditions. Single rotary compressors, widely used in air-conditioning systems, suffered from excessive speed ripples, noise, and vibration caused by significant load torque pulsations. This paper developed an innovative method for estimating magnet temperature for air-conditioning motors without lookup tables in conjunction with reducing the speed ripple through precise load torque estimation. A dual observer system is adopted, enabling independent gain settings for both the load torque observer and speed observer, thereby enhancing estimation accuracy and dynamic response. By estimating the load torque pattern, the proposed method reduces speed ripple and estimates magnet temperature without relying on lookup tables. In this study, magnet temperature can be determined from the voltage equation, with a permissible torque reference modification. By applying the proposed method, speed ripple was reduced by 74.3% compared with pre-compensation levels, and the magnet temperature estimation error was within 5 °C for a temperature increase from 26 °C to 76 °C. These results confirm the robustness, computational efficiency and feasibility of the proposed method, making it suitable for low-cost embedded systems in household appliances.

In this paper, an adaptive load torque observer based on backstepping control is designed, which achieves accurate load estimation where the load is unknown. Based on this, in order to reduce the loss of the motor at low load, a smooth switching strategy of rotor flux based on speed error is designed. According to the real-time speed error of the induction motor, the smooth switching strategy achieves dynamic flux switching. Firstly, when the uncertain load occurs for the first time in the recursive design, the adaptive law of the load is designed, and a novel adaptive load torque observer is obtained, which accurately estimates the uncertain load torque in real time. Secondly, the relationship between the loss and the rotor flux is established by analyzing the loss model of induction motor, and the optimal rotor flux is obtained. The smooth switching control strategy based on speed error is designed to realize the efficiency optimization of induction motor. Finally, the control strategy proposed in this paper is experimentally verified on the LINKS-RT platform. The results show that the proposed control strategy has excellent load disturbance attenuation performance and reduces the energy loss.

We investigate non-standard interaction effects in antineutrino-electron scattering experiments with baselines short enough to ignore standard oscillation phenomena. The setup is free of ambiguities from the interference between new physics and oscillation effects and is sensitive to both semileptonic new physics at the source and purely leptonic new physics in the weak interaction scattering at the detector. We draw on the TEXONO experiment as the model system, extending its analysis of non-standard interaction effects at the detector to include the generally allowed non-standard interaction phase at the detector and both non-universal and flavor changing new physics at the reactor source. We confirm that the current data allows for new physics constraints at the detector of the same order as those currently published, but we find that constraints on the source new physics are at least an order of magnitude weaker. The new physics phase effects are at the 5% level, noticeable in the 90% C.L. contour plots but not significantly affecting the conclusions. Based on projected increase in sensitivity with an upgraded TEXONO experiment, we estimate the improvement of sensitivity to both source and detector non-standard interactions. We find that the bounds on source parameters improve by an order of magnitude, but do not reach parameter space beyond current limits. On the other hand, the detector new physics sensitivity would push current limits by factors 5 to 10 smaller.

The superconducting (SC) fault current limiter (FCL) is very attractive to enhance the security and the power quality of the electricity grid, so its great interest throughout the world. Most of SC FCL uses the quench of a SC element and the thermal phenomena are fundamental. We study them with two approaches: experiments and calculations. For the experiments, we have developed temperature sensors deposited directly on coated conductors to achieve the best thermal coupling and a very short time response. The sensors are micron size copper meanders. A very thin polymer layer provides the electrical isolation. The sensor performances are very satisfying both in terms of time response and accuracy. The measurements are in good agreement with temperature calculations, performed using the current data during limiting experiments. The recovery phase is very important and has been studied using the temperature measurements. The cooling conditions such as bath temperature play an important part for the recovery. Several tests carried out on different samples are presented and analysed. These works are important for a better understanding of the complex phenomena occurring during a limitation and for the design of a conductor suitable for FCL.

A sensorless drive scheme with adaptive gain speed controller for brushless DC motors is proposed in this paper. There are three observers in the control system, they are a disturbance voltage observer, a speed observer, and a disturbance torque observer. These observers are used not only for a position and speed estimator in the sensorless vector control, but also for a current controller. The rotor position and speed estimator are robust for parameter variations, such as the e.m.f. constant. The validity of the sensorless vector control of brushless DC motors is confirmed with computer simulations.

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Deep learning to catalyze inverse molecular design

On-road measurement of NH3 and N2O emissions from a Euro V heavy-duty vehicle

Plant-specific human reliability analysis (HRA) tailors human error probabilities (HEPs) to a facility's unique contexts. The diversity of digital human-machine interfaces and operational cultures necessitates methods grounded in empirical data rather than generic databases to ensure true plant-specificity. Advancing beyond prior fragmented data applications, this study establishes a systematic pipeline integrating the HuREX (Human Reliability data Extraction) database with the EMBRACE (EMpirical data-Based crew Reliability Assessment and Cognitive Error analysis) method. By sharing a unified task taxonomy, this framework directly translates plant-specific simulator data into coherent HEP estimates. In an application of this framework to the APR1400 plant, time-dependent failure probabilities were derived using Bayesian inference on 30 simulator performance records. Concurrently, nominal primitive error probabilities (NPEPs) were estimated via logistic regression from 44,585 task records. Due to current data scarcity, performance shaping factor (PSF) multipliers were determined through structured expert elicitation. This data-method integration provides a replicable foundation for generating context-sensitive HEPs in digitalized control rooms, highlighting both empirical strengths and current data limitations. • A plant-specific HRA approach is developed by integrating HuREX with EMBRACE. • HuREX data support the estimation of the time and cognitive failure probabilities. • The proposed approach is applied to estimate HEPs for the APR1400 plant. • Limitations such as reliance on expert judgment are discussed.

The realistic compact star models are considered in [Formula: see text] gravity. The main result is that simple modified gravity can be consistent with current observational mass limit of neutron stars, with current data on their masses and radii; also, it simplifies solution of the hyperon problem. The effect of strong magnetic field in [Formula: see text]-gravity is also investigated. The existence of more compact (in comparison with General Relativity) stars with large magnetic fields in central regions is possible. In fact the second branch of stability appears.