Control Method Research Articles

Fault tolerant control of permanent magnet synchronous motor based on hybrid control strategies DTC-SVM with second order sliding mode control using multi-variable filter

This paper describes direct torque control (DTC) of a permanent magnet synchronous machine (PMSM) powered by a two-level voltage inverter whose switching of switches is based on the Space Vector Modulation. To overcome the robustness of the control to the presence of a fault, we included an improvement of the direct torque control with Space vector modulation (DTC-SVM) by the use of the filters for the flux and the torque and compared to the direct torque control of the DTC-SVM, the PI controllers are replaced with sliding mode blocks, This control method allows giving a new structure DTC -SVM with sliding mode control. The analysis of the results shows good performances for the speed and a considerable reduction of the fluctuations at the level of the torque and the flux.

A triangle decomposition method for the mobility control of mecanum wheel-based robots

Mobile robots are used in a variety of applications including research, education, healthcare, customer service, security and so on. Based upon the application, the robots employ different locomotion systems for their mobility. When it comes to rolling locomotion, the wheels used to provide mobility to robots can be categorized as: tracks, omnidirectional wheels, and unidirectional wheels with a steering system. The ability of omnidirectional wheels to drive machines in small spaces makes them interesting to use. Among the types of omnidirectional wheels, mecanum wheels are widely used due to their inherent benefits. With the right control strategy, robots equipped with mecanum wheels can move freely, in all possible directions. In this study, a triangle decomposition approach is employed for controlling omnidirectional mecanum wheel-based robots. The method consists of breaking down any path into a set of linear motions that can be horizontal, vertical, or oblique. Furthermore, the oblique paths are divided into smaller segments that can be resolved into a horizontal and vertical component in a right-angle triangle. The suggested control method is tested and proved on a simple scenario using Webots simulation software.

Research on Active Flow Control Method of NACA0012 Airfoil with Traveling Wave Structure

Research on Active Flow Control Method of NACA0012 Airfoil with Traveling Wave Structure

Low-Power Consumption Control Method for BLDCM With Very Small Inductance and Nonideal Back EMF

Low-Power Consumption Control Method for BLDCM With Very Small Inductance and Nonideal Back EMF

Voltage and frequency control of microgrid in presence of micro-turbine interfaced to matrix converter

The active and reactive load changes have a significant impact on voltage and frequency. In this paper, in order to stabilize the microgrid (MG) against load variations in islanding mode, the active and reactive power of all distributed generators (DGs), including energy storage (battery), diesel generator, and micro-turbine, are controlled. The micro-turbine generator is connected to MG through a three-phase to three-phase matrix converter, and the droop control method is applied for controlling the voltage and frequency of MG. In addition, a method is introduced for voltage and frequency control of micro-turbines in the transition state from grid-connected mode to islanding mode. A novel switching strategy of the matrix converter is used for converting the high-frequency output voltage of the micro-turbine to the grid-side frequency of the utility system. Moreover, using the switching strategy, the low-order harmonics in the output current and voltage are not produced, and consequently, the size of the output filter would be reduced. In fact, the suggested control strategy is load-independent and has no frequency conversion restrictions. The proposed approach for voltage and frequency regulation demonstrates exceptional performance and favorable response across various load alteration scenarios. The suggested strategy is examined in several scenarios in the MG test systems, and the simulation results are addressed.

Full-Speed Region Predictive Current Control Method of Symmetrical Series-Winding PMSM With Higher DC-Link Utilization

Full-Speed Region Predictive Current Control Method of Symmetrical Series-Winding PMSM With Higher DC-Link Utilization

Least mean square based adaptive control of active power filter

The proposed control scheme is based on the least mean square (LMS) algorithm. The LMS algorithm is employed to estimate the necessary reference tracking current for the active power filter (APF). The proposed control scheme aims to enhance the dynamic response of the APF and minimize steady-state error. The weights of the LMS technique are calculated based on the estimated current of the APF. This algorithm is employed to minimize the error difference between the desired system output and its actual output, known as the mean square error (MSE). The estimated weights are utilized to modify the reference current weights, enabling them to follow the intended current of the APF. The online adaption of the LMS method involves the real-time adjustment of the weights. The performance of the LMS-based APF control is evaluated through a simulation study in MATLAB/Simulink, where it is compared with the conventional control method.

Double-Vector Model-Free Predictive Current Control Method for Voltage Source Inverters With Sampling Noise Suppression

Model-free predictive current control (MFPCC) methods based on look-up tables (LUTs) have been widely applied in voltage source inverters (VSIs) due to their simple implementation and excellent robustness. However, the performance of the VSIs is affected by the voltage vector used, accuracy of current gradients and sampling noise. In order to improve these, a new double-vector MFPCC method is proposed in this paper. First, a synthetic voltage vector composed of double vectors is applied in each control period to reduce current ripples, where the durations of the double vectors are calculated using predicted current gradients. Next, an extended LUT is designed by using the current gradients of the applied synthetic voltage vectors to update all current gradients of eight basic voltage vectors, so as to increase the accuracy of current gradients. Then, the effect of the sampling noise on MFPCC is analyzed in detail and suppressed by a sliding-mode observer (SMO) for the VSIs. Finally, simulation and experimental results have verified the effectiveness of the proposed method.

Dynamic Power Balancing Control Method for Energy Storage DC/DC Parallel Supply System With Low-Frequency Pulsed Load

Dynamic Power Balancing Control Method for Energy Storage DC/DC Parallel Supply System With Low-Frequency Pulsed Load

Design of Interactive Online Teaching System for Big Data Computer Information System Based on Mobile Sensor Network

The combination of the Internet and computers has resulted in many new products, including smart homes. The emergence of smart homes can satisfy people's detection of the internal environment of the home, not only to ensure the continuity and safety of indoor monitoring, but also to provide convenience for people to manage their family life. This paper analyzes the current status of smart home research at home and abroad, and people's needs for homes, and builds and designs smart home systems based on Ethernet and ZigBee protocol wireless sensor networks connected to TCP/IP protocol. In this article, the ARM chip is used in the Android system to ensure that the system and functions are matched, and information is transmitted through the serial port of the development board. After receiving the corresponding sending signal, the wireless module will use ZigBee technology to send the corresponding signal to achieve the goal of controlling home appliances. We divides the control system into two parts, the Android Voice Control Plateform and the data collection terminal. The former is based on the Android operating system and is designed for voice recognition control; the latter uses a ZigBee microcontroller to achieve control and feedback. This article uses the built-in voice control system of the smart home to subtly replace the conventional control method of the smart home. Moreover, the dialogue with users is very high, easy to use, low cost, and very practical. In addition, the research methods used in this article can also be applied to other related fields.

A REVIEW ON FAST DISSOLVING FILM

Orally fast dissolving films (OFDFs) have been introduced in the market recently as they provide convenience and ease of use over other dosage forms such as orally disintegrating tablets. This technology evolved over the past few years from the confection and oral care markets in the form of breath strips and became a novel and widely accepted form by consumers, so OFDFs are gaining the interest of large number of pharmaceutical industries. Orally fast dissolving film is the type of drug delivery system which when placed in the oral cavity, disintegrate or dissolve within few seconds without the intake of water. OFDFs are very similar to postage stamp in their shape, size and thickness. These films have a potential to deliver the drug systemically through intragastric, sublingual or buccal route of administration and also has been used for local action. This type of technology offer a convenient way of dosing medication, not to special population groups like pediatric, geriatric, bedridden patients, mentally ill patients, but also to the general population. The present review provides an account of various formulation considerations, method of preparation and quality control of the OFDFs.

Sensorless fuzzy control algorithm for permanent magnet synchronous motor based on particle swarm optimization parameter identification and harmonic extraction

ABSTRACT In sensorless control of permanent magnet synchronous motor (IPMSM) with sliding mode observer, the problem of parameter robustness and poor stability seriously affects the control effect of sensorless operation. Therefore, a fuzzy sliding mode observer (SMO) phaselocked loop (PLL) combining particle swarm optimization (PSO) parameter identification and recursive least squares adaptive linear (RLS-Adaline) harmonic extractor is proposed. First, the fuzzy controller is used to process the control parameters of the sliding mode observer and the phaselocked loop. Secondly, the RLS-Adaline harmonic extractor is used to effectively filter the higher harmonic component of the back electromotive force (EMF), thus significantly improving the sensorless control effect. Thirdly, the PSO algorithm is used to identify the parameters of the motor, and the effect of parameter identification is judged by the performance index. The experimental results show that the proposed control method can effectively reduce the speed error and rotor position error.

FAULT-TOLERANT CONTROL ALGORITHM FOR AUTOMOTIVE ELECTRONIC MECHANICAL BRAKING RAMP ASSIST

The electronic mechanical braking system directly affects the driving safety of automobiles, but there are auxiliary control methods. Therefore, a fault-tolerant control algorithm for automotive electronic mechanical braking ramp assistance is proposed. The faults of the electronic mechanical braking ramp assistance system and select sensor faults are analyzed as the main research object. By introducing a BP neural network, the longitudinal speed of the vehicle, the wheel speed, the difference between the target slip rate and the current slip rate, and the rate of change of the difference are taken as the network input. Online learning is used to adjust and update the weight coefficients of each layer of the network, output fault diagnosis results and optimized PI controller parameters, and achieve fault-tolerant control. Three different types of sensor fault signals were artificially added in the experimental testing. After the proposed fault-tolerant control method, the controlled signals were used to replace the fault signals, ensuring the normal driving of the car.

A long-horizon move-blocking based direct power model predictive control for dynamic enhancement of DC microgrids

This research paper presents a novel long-horizon move-blocking based direct power model predictive control (DPMPC) strategy, uniquely designed to enhance the dynamic performance of boost converters in the grid-connected mode within DC microgrids. A precise dynamic model is developed considering a boost converter connected to a DC-link supplying constant power and resistive loads. To predict the system's dynamic behavior over an extended interval and enhance its performance in the presence of constant power loads, the long-horizon based finite control set model predictive control (FCS-MPC) method is introduced. To address the computational complexity associated with the conventional long-horizon DPMPC approach, a move-blocking (MB) strategy is incorporated into FCS-MPC, reducing the computational burden while improving the dynamic performance of the boost converter. Unlike recent studies that utilize DPMPC with short prediction interval, this paper presents evidence that a longer prediction interval is crucial for maintaining system stable and enhancing the dynamic response and also utilizing MB to make the control implementable in the real-time. The simulation results conducted using MATLAB/Simulink demonstrate the effective performance of the proposed strategy across various operating conditions of the boost converter. Experimental results further validate the strategy's capability to enhance the dynamic performance of the boost converter. Importantly, the experimental findings confirm the feasibility of implementing the proposed long-horizon move-blocking DPMPC strategy in real-time applications.

Government Religious Discrimination, Support of Religion, and Muslim Minority-Related Societal Violence in Western Democracies

Does government-based religious discrimination against religious minorities and government support of majority religion affect religiously motivated societal violence between minorities and majority religious groups in Western democracies? Analyzing Muslim minorities, this study tries to answer the question by looking specifically at the religious violence perpetrated by and against these minorities in the West. Using a novel cross-national time-series data on 25 Western countries disaggregated by victim and perpetrator groups, this paper finds that while discrimination contributes to a country encountering religiously driven societal violence perpetrated by both Muslim and majority religious groups, government support for majority religion seems to pose no security threat. Furthermore, a case study analysis of the UK employing the synthetic control method corroborates the results of the cross-country analysis. The findings have important policy implications for counter-strategies against both Islamic and right-wing violent extremism in the West.

Nonlinear Robust Control of Vehicle Stabilization System with Uncertainty Based on Neural Network

To effectively suppress the effects of uncertainties including unmodeled dynamics and external disturbances in the vehicle stabilization system, a nonlinear robust control strategy based on a multilayer neural network is proposed in this paper. First, the mechanical and electrical coupling dynamics model of the vehicle stabilization system, considering model uncertainty and actuator dynamics, is refined. Second, the lumped uncertainty of the vehicle stabilization system is estimated by a multi-layer neural network and compensated by feedforward control. The high robustness of the system is ensured by constructing the sliding mode feedback control law. The proposed control method overcomes the limitations of sliding mode technology and the neural network and is naturally applied to the vehicle stabilization system, avoiding the adverse effects of high-gain feedback. Based on Lyapunov theory, it is demonstrated that the proposed controller is able to achieve the desired stability tracking performance. Finally, the effectiveness of the proposed control strategy is verified by co-simulation and comparative experiments.

Decentralized Swarm Control in Communication-Constrained Environments Using a Blended Leader Follower-Artificial Potential Field with Biologically Inspired Interactions

Abstract This paper presents a study of how communication ranges influence the performance of a new decentralized control method for swarms of autonomously navigating ground vehicles that uses a blended leader-follower / artificial potential field approach. While teams of autonomous ground vehicles (AGV) that can navigate autonomously through off-road terrain have a variety of potential uses, it may be difficult to control the team in low-infrastructure environments that lack long-range radio communications capabilities. In this work, we propose a novel decentralized swarm control algorithm that combines the potential-field planning method with the leader-follower control algorithm and biologically-inspired inter-robot interactions to effectively control the navigation of a team of AGV (swarm) through rough terrain using only a single lead vehicle. We use simulated experimentation to demonstrate the robustness of this approach using only point-to-point wireless communication with realistic communication ranges. Furthermore, we analyze the range requirements of the communication network as the number in the swarm increases. We find that wireless communication range must increase as the number of agents in the swarm increases in order to effectively control the swarm. Our analysis showed that mission success decreased by 40% when the communication range was reduced from 100 meters to 200 meters, with the exact reduction also depending on the number of vehicles.

Intelligent Control of Pre-Chamber Pressure Based on Working Condition Identification for the Coke Dry Quenching Process

The pre-chamber pressure is an important control parameter that affects the coke dry quenching process. It often fluctuates violently and is detrimental for the safe operation of the coke dry quenching process. This study proposes an intelligent control method for the pre-chamber pressure based on working condition identification for the coke dry quenching process to realize stable control of the pre-chamber pressure. First, by describing the coke dry quenching process and analyzing the factors affecting the pre-chamber pressure, an intelligent control strategy was developed. Then, the K-means clustering algorithm was used to identify the working conditions of pre-chamber, and the working conditions were divided into two categories: stable and fluctuating. For stable conditions, a fuzzy proportional-integral-derivative controller was designed to improve the pressure control accuracy. For fluctuating conditions, an expert controller was designed to rapidly adjust the pressure. Finally, experiments based on actual data were performed and the results showed that the proposed method can effectively improve the control accuracy of pressure under different conditions. This satisfies the requirements for a continuous coke dry quenching process.

Research of Sensorless Control Method for PMSM Based on Specific Voltage Feed-forward Decoupling

Research of Sensorless Control Method for PMSM Based on Specific Voltage Feed-forward Decoupling

Impacts of Forced Immigration: The Venezuelan Diaspora and the Brazilian Labor Market

ABSTRACT This article estimates the impact of immigrant influxes on local labor markets, taking advantage of a natural experiment generated by a massive wave of forced migration of Venezuelans to Roraima, a state in northern Brazil, from 2016 onward. Estimates show that a 1% increase in the share of migrants in the state population due to immigration resulted in 1.1% higher earnings for native workers in the formal sector, 0.3% fewer hours worked weekly, and 2.3% higher wages. Results are confirmed by employing a synthetic control method.