Control Method Research Articles

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.

Fault-Tolerant Phototaxis of a Modular System Inspired by Gonium pectorale Using Phase-Based Control

In this study, we proposed a model for modular robots in which autonomous decentralized modules adaptively organize their behavior. The phototaxis of Gonium pectorale, a species of volvocine algae, was modeled as a modular system, and a fault-tolerant modular control method of phototaxis was proposed for it. The proposed method was based on the rotation phase of the colony and adaptively adjusted an internal response-related parameter to enhance the fault tolerance of the system. Compared to a constant parameter approach, the simulation results demonstrated a significant improvement in the phototaxis time for positive and negative phototaxis during module failures. This method contributes to achieving autonomous, decentralized, and purposeful mediation of the modules necessary for controlling modular robots.

A new hybrid control strategy for improving ride comfort on lateral suspension system of railway vehicle

The ride comfort is a key research area for the quality improvement of railway vehicles. Several control methods have been developed to improve the ride comfort, in which the semi-active control approach inherits the advantages of passive and active control approaches. However, the semi-active control may perform ineffectively in vibration attenuation when the carbody load changes. To address this issue, we proposed a novel hybrid control method that combines the skyhook (SH) and displacement velocity (DV) control methods to reduce vibrations of the carbody in the lateral direction under carbody load changes. First, a hybrid control model built in Matlab software was applied to a quarter railway vehicle model for a preliminary assessment of the ride comfort. Second, to provide a more comprehensive evaluation, the proposed model was applied to a whole railway vehicle model using co-simulation (Matlab-Simpack). In the quarter model, under the sinusoidal excitation, the hybrid control showed a lower acceleration by 48.5% and 42.8% when the carbody load reduced to a half and a quarter, respectively, compared to the passive control. This finding was confirmed with the whole model. Moreover, in the whole model, under the track irregular excitation, the hybrid control showed a lower acceleration by 26.6% and 17.4% when the carbody load reduced to a half and a quarter, respectively. Further, the proposed method showed an acceptable safety level, measured by the derailment coefficient (0.0743 at a half load and 0.089 at a quarter load). In conclusions, the proposed hybrid SH-DV control exhibits high robustness, adaptability, effectiveness, and acceptable safety level in response to variations in body load.

On-board model predictive control for autonomous lane keeping with fuzzy preview distance: Design and experiment

This paper concerns with the development of computationally efficient lane keeping control method for the autonomous vehicles. To obtain autonomous lane keeping, model predictive control (MPC) scheme was intensively investigated in the previous studies owing to its inherent advantage of dealing with constrained multivariable systems. However, the tradeoff problem between the good tracking performance and the low computational complexity is inevitably raised in developing of MPC-based lane keeping technology. To alleviate the conflict between performance and cost, an on-board MPC with fuzzy preview distance is designed in this study. A linear system dynamics model is used in the MPC design to reduce the computational cost, and a fuzzy logic algorithm is developed to select an appropriate preview distance for enhancing the MPC performance. Further, hardware-in-the-loop test is adopted to explore the effectiveness and efficiency of the proposed control method. In comparison to the proportional-integral controller, the experimental results show that the MPC is more sensitive to the selective value of fixed preview distance. Since the significant impact of preview distance selection on the MPC-based lane keeping performance, the fuzzy logic algorithm is of the essence in terms of selecting the appropriate preview distance for MPC enhancement under different vehicle speed and road curvature. Eventually, experimental results validate that the proposed fuzzy preview distance algorithm can effectively improve the MPC-based lane keeping performance for autonomous vehicles subject to limited computational resource.

Synchronization of complex networks with PID pinning controller

Recently, synchronization of complex networks has been studied deeply; however, most existing works take the standard proportional control strategy approach to pinning controllers, which produces a steady-state error and has a significant time of fluctuation. In order to solve this problem, we propose a new method using the Proportional-Integral-Differential (PID) control to design a pinning control method for synchronization of complex networks. Based on Lyapunov theory, the stability of the network is proved, and sufficient synchronization conditions are obtained. Finally, examples are given to evaluate the effectiveness of the proposed PID pinning method and compare it to the traditional proportional pinning controller and adaptive pinning controller.

Adaptive output feedback stabilisation control for cascaded systems with output constraints and disturbance

The system under consideration is a cascaded system composed of integral input-to-state stable inverse dynamics and a non-holonomic system. An adaptive output feedback stabilisation control is proposed for the system with disturbance and output constraints. The dynamic uncertainty is eliminated using the technique that changes the supply rate. An extended state observer is proposed by defining the non-vanishing disturbance as a new state variable of the system. The asymmetric time-varying output constraint is solved by utilising the tan-type barrier Lyapunov function. The result shows that the adaptive output feedback controller guarantees the stability of the closed-loop system without violating the asymmetric time-varying output constraints under the backstepping technique. Simulation results are given to verify the effectiveness of the control method.

Boundary feedback control of Navier–Stokes equations around arbitrary bluff body with prescribed drag and lift coefficients

This paper presents a boundary feedback control method for the two- and three-dimensional Navier–Stokes equations around an arbitrary bluff body, with prescribed drag and lift coefficients. In order to determine the feedback control law, we consider an extended system coupling the equations governing the Navier–Stokes problem with an equation satisfied by the control on the bluff body, which is a part of the domain boundary. We utilize the artificial compressibility method, which approximates the Navier–Stokes equations in the limit, along with a priori estimation techniques, to establish a boundary control. Such a control law ensures that the prescriptions of the drag and lift coefficients are attained. Subsequently, a compactness result allows to let the compressibility parameter tend to zero. The application of the Faedo-Galerkin method is essential in reaching this result.

Hierarchical Control of AC/DC Hybrid Microgrid Based on Primary Model Predictive Optimization and Secondary Switching Control

The fluctuating characteristics of renewable energy generation in hybrid AC/DC microgrids, combined with timevarying loads, can result in high total harmonic distortion (THD) and distorted output voltage and current waveforms. To address these issues, a faster and more comprehensive primary and secondary hierarchical control method is required. In this paper, a faster model predictive optimization algorithm is introduced as a primary control method to predict operational states in advance, maintain a low THD state, and reduce the impact on power quality. Then, a secondary switching control is added to correct frequency and power allocation errors caused by primary control, recover microgrid voltage and frequency to their rated values, and ensure stable reactive power. Finally, simulation and comparison results prove the proposed method's effectiveness and applicability.

Investigation of design methodology for doubly-fed bearingless flux reversal motor

This article integrates the technology of bearingless motors with the basic theory of magnetic flux reverse motors, proposes a doubly fed bearingless magnetic flux reverse motor, and conducts research on its structural design, operating mechanism, electromagnetic performance analysis, mathematical model, control method, digital control system, and other aspects. The basic structure of a new type of doubly fed bearingless magnetic flux reverse motor is designed, and the principle of radial suspension force generation is studied. The performance related to torque and radial suspension force is analyzed. A mathematical model of torque and radial suspension force is established, including magnetic field distribution, no-load permanent magnet flux, and no-load back electromotive force. A doubly fed bearingless magnetic flux reverse motor system is designed and constructed, and inductance experiments are conducted.

Passive Shunted Piezoelectric Systems for Vibration Control of Wind Turbine Towers: A Feasibility Study

Many countries have a variety of offshore and onshore wind turbines that face extreme aging challenges. Issues with harmful vibrations that must be minimized are addressed in this paper. A new method of wind turbine tower vibration control using piezoelectricity and shunt circuits is proposed in this paper. The passive vibration control method is shown to improve the tower’s structural performance under various environmental loads, like wind and seismic excitations. To examine the effectiveness of the suggested shunted piezoelectric system, a simple surrogate finite element model of a wind turbine tower is considered, and various investigations at the second eigenfrequency are carried out. An alternative way of modeling the studied structure is considered and the results demonstrate better performance. The advantages of setting up structural damping systems for decreasing tower vibrational loads and boosting their structural stability and resilience against extreme events are highlighted throughout this work.

Investigation of rank order centroid method for optimal generation control.

Multi-criteria decision-making (MCDM) presents a significant challenge in decision-making processes, aiming to ascertain optimal choice by considering multiple criteria. This paper proposes rank order centroid (ROC) method, MCDM technique, to determine weights for sub-objective functions, specifically, addressing issue of automatic generation control (AGC) within two area interconnected power system (TAIPS). The sub-objective functions include integral time absolute errors (ITAE) for frequency deviations and control errors in both areas, along with ITAE of fluctuation in tie-line power. These are integrated into an overall objective function, with ROC method systematically assigning weights to each sub-objective. Subsequently, a PID controller is designed based on this objective function. To further optimize objective function, Jaya optimization algorithm (JOA) is implemented, alongside other optimization algorithms such as teacher-learner based optimization algorithm (TLBOA), Luus-Jaakola algorithm (LJA), Nelder-Mead simplex algorithm (NMSA), elephant herding optimization algorithm (EHOA), and differential evolution algorithm (DEA). Six distinct case analyses are conducted to evaluate controller's performance under various load conditions, plotting data to illustrate responses to frequency and tie-line exchange fluctuations. Additionally, statistical analysis is performed to provide further insights into efficacy of JOA-based PID controller. Furthermore, to prove the efficacy of JOA-based proposed controller through non-parametric test, Friedman rank test is utilized.