Congestion Control Algorithms for the Internet-A Secondary Publication

In the last five years,there has been a V-shaped recovery in the number of papers on congestion control algorithms on the Internet.In this paper,congestion problems on the Internet are discussed,such as congestion collapse and bufferbloat from the perspective of the necessity of congestion control algorithms.The typical congestion control algorithms are introduced,and the research areas and methods of congestion control algorithms are described.Recent research trends and future prospects of congestion control algorithms are also presented.

Traffic flow of connected and automated vehicles at lane drop on two-lane highway: An optimization-based control algorithm versus a heuristic rules-based algorithm

This paper investigates traffic flow of connected and automated vehicles at lane drop on two-lane highway. We evaluate and compare performance of an optimization-based control algorithm(OCA) with that of a heuristic rules-based algorithm(HRA). In the OCA, the average speed of each vehicle is maximized. In the HRA, virtual vehicle and restriction of the command acceleration caused by the virtual vehicle are introduced. It is found that(i) capacity under the HRA(denoted as C_(H)) is smaller than capacity under the OCA;(ii) the travel delay is always smaller under the OCA, but driving is always much more comfortable under the HRA;(iii) when the inflow rate is smaller than C_(H), the HRA outperforms the OCA with respect to the fuel consumption and the monetary cost;(iv) when the inflow rate is larger than C_(H), the HRA initially performs better with respect to the fuel consumption and the monetary cost, but the OCA would become better after certain time. The spatiotemporal pattern and speed profile of traffic flow are presented, which explains the reason underlying the different performance. The study is expected to help for better understanding of the two different types of algorithm.

Enhanced Perturb and Observe Control Algorithm for a Standalone Domestic Renewable Energy System

The generation of electricity,considering environmental and eco-nomic factors is one of the most important challenges of recent years.In this article,a thermoelectric generator(TEG)is proposed to use the thermal energy of an electric water heater(EWH)to generate electricity independently.To improve the energy conversion efficiency of the TEG,a fuzzy logic con-troller(FLC)-based perturb&observe(P&O)type maximum power point tracking(MPPT)control algorithm is used in this study.An EWH is one of the major electricity consuming household appliances which causes a higher electricity price for consumers.Also,a significant amount of thermal energy generated by EWH is wasted every day,especially during the winter season.In recent years,TEGs have been widely developed to convert surplus or unused thermal energy into usable electricity.In this context,the proposed model is designed to use the thermal energy stored in the EWH to generate electricity.In addition,the generated electricity can be easily stored in a battery storage system to supply electricity to various household appliances with low-power-consumption.The proposed MPPT control algorithm helps the system to quickly reach the optimal point corresponding to the maximum power output and maintains the system operating point at the maximum power output level.To validate the usefulness of the proposed scheme,a study model was developed in the MATLAB Simulink environment and its performance was investigated by simulation under steady state and transient conditions.The results of the study confirmed that the system is capable of generating adequate power from the available thermal energy of EWH.It was also found that the output power and efficiency of the system can be improved by maintaining a higher temperature difference at the input terminals of the TEG.Moreover,the real-time temperature data of Abha city in Saudi Arabia is considered to analyze the feasibility of the proposed system for practical implementation.

Identification of time-varying system and energy-based optimization of adaptive control in seismically excited structure

The combination of structural health monitoring and vibration control is of great importance to provide components of smart structures.While synthetic algorithms have been proposed,adaptive control that is compatible with changing conditions still needs to be used,and time-varying systems are required to be simultaneously estimated with the application of adaptive control.In this research,the identification of structural time-varying dynamic characteristics and optimized simple adaptive control are integrated.First,reduced variations of physical parameters are estimated online using the multiple forgetting factor recursive least squares(MFRLS)method.Then,the energy from the structural vibration is simultaneously specified to optimize the control force with the identified parameters to be operational.Optimization is also performed based on the probability density function of the energy under the seismic excitation at any time.Finally,the optimal control force is obtained by the simple adaptive control(SAC)algorithm and energy coefficient.A numerical example and benchmark structure are employed to investigate the efficiency of the proposed approach.The simulation results revealed the effectiveness of the integrated online identification and optimal adaptive control in systems.

Two Adaptive Control Algorithms in Semi-Active Suspension System

To reduce the vibration in the suspension, semi active suspension system was employed. And its CARMA model was built. Two adaptive control schemes, the minimum variance self tuning control algorithm and the pole configuration self tuning control algorithm, were proposed. The former can make the variance of the output minimum while the latter can make dynamic behavior satisfying. The stability of the two schemes was analyzed. Simulations of them show that the acceleration in the vertical direction has been reduced greatly. The purpose of reducing vibration is realized. The two schemes can reduce the vibration in the suspension and have some practicability.

The Algorithms of Adaptive Active Noise Control Systems in a Duct

On the basis of the theory of adaptive active noise control(AANC) in a duct, this article discusses the algorithms of the adaptive control, compares the algorithm characteristics using LMS, RLS and LSL algorithms in the adaptive filter in the AANC system, derives the recursive formulas of LMS algorithm. and obtains the LMS algorithm in computer simulation using FIR and IIR filters in AANC system. By means of simulation, we compare the attenuation levels with various input signals in AANC system and discuss the effects of step factor, order of filters and sound delay on the algorithm's convergence rate and attenuation level.We also discuss the attenuation levels with sound feedback using are and IIR filters in AANC system.

A PID Tuning Approach for Inertial Systems Performance Optimization

In the practice of control the industrial processes, proportional-integral-derivative controller remains pivotal due to its simple structure and system performance-oriented tuning process. In this paper are presented two approaches for synthesis the proportional-integral-derivative controller to the models of objects with inertia, that offer the procedure of system performance optimization based on maximum stability degree criterion. The proposed algorithms of system performance optimization were elaborated for model of objects with inertia second and third order and offer simple analytical expressions for tuning the PID controller. Validation and verification are conducted through computer simulations using MATLAB, demonstrating successful performance optimization and showcasing the effectiveness PID controllers’ tuning. The proposed approaches contribute insights to the field of control, offering a pathway for optimizing the performance of second and third-order inertial systems through robust controller synthesis.

Design and Analysis of Power and Transmission System of Downhole Pure Electric Command Vehicle

Electric vehicles use electric motors, which turn electrical energy into mechanical energy. As electric motors are conventionally used in all the industry, it is an established development site. It’s a mature technology with ideal power and torque curves for vehicular operation. Conventional vehicles use oil and gas as fuel or energy storage. Although they also have an excellent economic impact, the continuous use of oil and gas threatened the world’s reservation of total oil and gas. Also, they emit carbon dioxide and some toxic ingredients through the vehicle’s tailpipe, which causes the greenhouse effect and seriously impacts the environment. So, as an alternative, electric car refers to a green technology of decarbonization with zero emission of greenhouse gases through the tailpipe. So, they can remove the problem of greenhouse gas emissions and solve the world’s remaining non-renewable energy storage problem. Pure electric vehicles (PEV) can be applied in all spheres, but their special implementation can only be seen in downhole operations. They are used for low noise and less pollution in the downhole process. In this study, the basic structure of the pure electric command vehicle is studied, the main components of the command vehicle power system, namely the selection of the drive motor and the power battery, are analyzed, and the main parameters of the drive motor and the power battery are designed and calculated. The checking calculation results show that the power and transmission system developed in this paper meets the design requirements, and the design scheme is feasible and reasonable.

Novel TPN control algorithm for exoatmospheric intercept

A novel control algorithm with fixed pulse thrust, based on true proportional navigation（TPN）, is proposed for exoatmospheric intercept.According to the TPN guidance law and pulse thrust characteristics, the principle of control command is presented, the control stability and precision are analyzed.With the help of the TPN guidance law, the algorithm can automatically modulate the turn-on time and duration of the thrust, which could effectively limit the impact of measure noise of the line-of-sight（LOS） angle and rate on the interception miss-distance.At last, the number-theoretic method（NTM） is introduced to acquire the relation between control algorithm and miss-distance, even as it simulates the intercept process with initial state noise.And the reliability of the algorithm is demonstrated with the simulation result.

Control Algorithm of Electric Vehicle in Coasting Mode Based on Driving Feeling

Coasting in gear is a common driving mode for the conventional vehicle equipped with the internal combustion engine（ICE）, and the assistant braking function of ICE is utilized to decelerate the vehicle in this mode. However, the electric vehicle（EV） does not have this feature in the coasting mode due to the relatively small inertia of the driving motor, so it will cause the driver cannot obtain the similar driving feeling to that of the conventional vehicle, and even a traffic accident may occur if the driver cannot immediately adapt to the changes. In this paper, the coasting control for EV is researched based on the driving feeling. A conventional vehicle equipped with continuously variable transmission（CVT） is taken as the reference vehicle, and the combined simulation model of EV is established based on AVL CRUISE and MATLAB/Simulink. The torque characteristic of the CVT output shaft is measured in coasting mode, and the data are smoothed and fitted to a polynomial curve. For the EV in coasting mode, if the state of charge（SOC） of the battery is below 95%, the polynomial curve is used as the control target for the torque characteristic of the driving motor, otherwise, the required torque is replaced by hydraulic braking torque to keep the same deceleration. The co-simulation of Matlab/Simulink/Stateflow and AVL CRUISE, as well as the hardware-in-loop experiment combined with d SPACE are carried out to verify the effectiveness and the real-time performance of the control algorithm. The results show that the EV with coasting braking control system has similar driving feeling to that of the reference vehicle, meanwhile, the battery SOC can be increased by 0.036% and 0.021% in the initial speed of 100 km/h and 50 km/h, respectively. The proposed control algorithm for EV is beneficial to improve the driving feeling in coasting mode, and it also makes the EV has the assistant braking function.

Nonlinear Model Algorithmic Control of a pH Neutralization Process

Control of pH neutralization processes is challenging in the chemical process industry because of their inherent strong nonlinearity. In this paper, the model algorithmic control (MAC) strategy is extended to nonlinear processes using Hammerstein model that consists of a static nonlinear polynomial function followed in series by a linear impulse response dynamic element. A new nonlinear Hammerstein MAC algorithm (named NLH-MAC) is presented in detail. The simulation control results of a pH neutralization process show that NLH-MAC gives better control performance than linear MAC and the commonly used industrial nonlinear propotional plus integral plus derivative (PID) controller. Further simulation experiment demonstrates that NLH-MAC not only gives good control response, but also possesses good stability and robustness even with large modeling errors.

Study on LQR control algorithm using superelement model

The conventional linear quadratic regulator(LQR) control algorithm is one of the most popular active control algorithms.One important issue for LQR control algorithm is the reduction of structure's degrees of freedom(DOF). In this work, an LQR control algorithm with superelement model is intended to solve this issue leading to the fact that LQR control algorithm can be used in large finite element(FE) model for structure. In proposed model, the Craig-Bampton(C-B) method, which is one of the component mode syntheses(CMS), is used to establish superelement modeling to reduce structure's DOF and applied to LQR control algorithm to calculate Kalman gain matrix and obtain control forces. And then, the control forces are applied to original structure to simulate the responses of structure by vibration control. And some examples are given. The results show the computational efficiency of proposed model using synthesized models is higher than that of the classical method of LQR control when the DOF of structure is large. And the accuracy of proposed model is well. Meanwhile, the results show that the proposed control has more effects of vibration absorption on the ground structures than underground structures.

Design of the pressure regulation algorithm for active braking in vehicle ACC system

To develop the pressure control algorithm for active braking of adaptive cruise control（ACC） system,a test bench with real parts of the tested vehicle is built.With the dynamic analysis of the active braking actuators,it is demonstrated that different duty of pulse-width modulation（PWM） signals could control the pressure changing rate of the wheel cylinder.To obtain that signal,a modified proportional-integral-differential（PID） control algorithm is developed using the variable parameter method,the control value reset method,the dead zone method and the integral saturation method.Experimental results show that the delay and overshoot of the pressure response could be reduced considerably using the modified PID algorithm compared with the conventional one.The proposed pressure control algorithm could be used for the further development of the ACC＇s controller.

Optimization of a Heat Exchanger Using an ARM Core Intelligent Algorithm

In order to optimize heat transfer in a heat exchanger using an ARM(advanced RISC machine)core intelligent computer algorithm,a new type of controller has been designed.The whole control structure of the heat exchange unit has been conceived on the basis of seven functional modules,including data processing and output,human-computer interaction,alarm,and data communication.The main controller and communication controller have been used in a combined fashion and a new MCU(micro control unit)system scheme has been proposed accordingly.A fuzzy controller has been designed by using a fuzzy control algorithm,and a new mode of heat transfer for the heat exchanger has been implemented by combining the fuzzy controller and the PID(proportioning integral derivative)controller.Finally,the model has been applied to an actual heat exchange station to test and verify the performances of the new approach.

Model algorithm control using neural networks for input delayed nonlinear control system

The performance of the model algorithm control method is partially based on the accuracy of the system＇s model. It is difficult to obtain a good model of a nonlinear system, especially when the nonlinearity is high. Neural networks have the ability to ＂learn＂the characteristics of a system through nonlinear mapping to represent nonlinear functions as well as their inverse functions. This paper presents a model algorithm control method using neural networks for nonlinear time delay systems. Two neural networks are used in the control scheme. One neural network is trained as the model of the nonlinear time delay system, and the other one produces the control inputs. The neural networks are combined with the model algorithm control method to control the nonlinear time delay systems. Three examples are used to illustrate the proposed control method. The simulation results show that the proposed control method has a good control performance for nonlinear time delay systems.

Comparison between iterative wavefront control algorithm and direct gradient wavefront control algorithm for adaptive optics system

Among all kinds of wavefront control algorithms in adaptive optics systems, the direct gradient wavefront control algorithm is the most widespread and common method. This control algorithm obtains the actuator voltages directly from wavefront slopes through pre-measuring the relational matrix between deformable mirror actuators and Hartmann wavefront sensor with perfect real-time characteristic and stability. However, with increasing the number of sub-apertures in wavefront sensor and deformable mirror actuators of adaptive optics systems, the matrix operation in direct gradient algorithm takes too much time, which becomes a major factor influencing control effect of adaptive optics systems. In this paper we apply an iterative wavefront control algorithm to high-resolution adaptive optics systems, in which the voltages of each actuator are obtained through iteration arithmetic, which gains great advantage in calculation and storage. For AO system with thousands of actuators, the computational complexity estimate is about O(n2) ～ O(n3) in direct gradient wavefront control algorithm, while the computational complexity estimate in iterative wavefront control algorithm is about O(n) ～(O(n)3/2), in which n is the number of actuators of AO system. And the more the numbers of sub-apertures and deformable mirror actuators, the more significant advantage the iterative wavefront control algorithm exhibits.

Optimizing control of coal flotation by neuro-immune algorithm

Coal flotation is widely used to separate commercially valuable coal from the fine ore slurry, and is an industrial process with nonlinear, multivariable, time-varying and long time-delay characteristics. The online detection of ash content of products as the operation performance evaluation in the flotation system is extraordinarily difficult because of the low solid content and numerous micro-bubbles in the slurry. Moreover, it is time-consuming by manual analysis. Consequently, the optimal separation is not usually maintained. A novel technique, called the neuro-immune algorithm (NIA) inspired by the biological nervous and immune systems, is presented in this paper for predicting the ash content of clean coal and performing the optimizing control to the coal flotation system. The proposed algorithm integrates the deeply-studied artificial neural network (ANN) and the developing artificial immune system (AIS). A two-layer back-propagation network was constructed offline based on the historical process data under the best system situation, using five parameters: the flow and the density of raw slurry, the input flows of water, the kerosene and the GF oil, as the inputs and the ash content of clean coal as the output. The immune cell of AIS is made up of six parameters above as the antigen. The cytokine based clone selection algorithm is used to produce the relative antibody. The detailed computation procedures about the hybrid neuro-immune algorithm are minutely discussed. The ash content of clean coal was predicted by NIA using the practical process data s: (308.6 174.7 146.1 43.6 4.0 9.4), and the absolute difference between the actual and computed ash content values was 0.0967%. The optimizing control on NIA was simulated considering two different situations where the ash content of clean coal was controlled downward from 10.00% or upward from 9.20% predicted by ANN to the target value 9.50%. The results indicate that the target ash content and the value of controlling parameters are obtained after several control cycles.

Nonlinear model predictive control based on support vector machine and genetic algorithm

This paper presents a nonlinear model predictive control(NMPC) approach based on support vector machine(SVM) and genetic algorithm(GA) for multiple-input multiple-output(MIMO) nonlinear systems.Individual SVM is used to approximate each output of the controlled plant Then the model is used in MPC control scheme to predict the outputs of the controlled plant.The optimal control sequence is calculated using GA with elite preserve strategy.Simulation results of a typical MIMO nonlinear system show that this method has a good ability of set points tracking and disturbance rejection.

Optimization of MAC algorithm based on IEEE 802.15.4 in indoor positioning system

The mobility of the targets asks for high requirements of the locating speed in indoor positioning systems.The standard medium access control(MAC)algorithm will often cause lots of packet conflicts and high transmission delay if multiple users communicate with one beacon at the same time,which will severely limit the speed of the system.Therefore,an optimized MAC algorithm is proposed based on channel reservation to enable users to reserve beacons.A frame threshold is set to ensure the users with shorter data frames do not depend on the reservation mechanism,and multiple users can achieve packets switching with relative beacon in a fixed sequence by using frequency division multiplexing technology.The simulation results show that the optimized MAC algorithm proposed in this paper can improve the positioning speed significantly while maintaining the positioning accuracy.Moreover,the positioning accuracy can be increased to a certain extent if more channel resources can be obtained,so as to provide effective technical support for the location and tracking applications of indoor moving targets.

Application of PI Control Algorithm to Discrete Manufacturing Systems

PI （proportional-integral） control algorithm is applied to control WlP （work-in-progress） in a discrete manufacturing system, where the cascade control of PI controllers is presented. It is in the frequency domain that the PI controller is designed with constraints on sensitivity options to ensure the stability and robustness of its parameters. A case is evaluated on a motorcycle engine crankcase production system, whose simulation results confirm that demand fluctuations can be compensated by PI controllers under a normal demand. PI controllers also possess low sensitivity to the distribution of production times.