Intelligent Control Algorithm Research Articles

Design of Intelligent Connected Cruise Control With Vehicle-to-Vehicle Communication Delays

Connected cruise control (CCC) refers to a type of advanced driver assistance system combined with wireless vehicle-to-vehicle (V2V) communication technology to improve control stability and driving safety. However, it is urgent to investigate intelligent control algorithms to improve the adaptability of CCC in complex traffic environment. In addition, the effect of communication delays attributed to shared wireless communications on the performance degradation of the intelligent CCC design cannot be ignored. In this study, the design of deep reinforcement learning (DRL) controller for CCC system in high-dynamic traffic scenarios is investigated, which considers both time-varying leading velocity and communication delays. To be more specific, an intelligent CCC algorithm based on deep deterministic policy gradient (DDPG) is developed. According to the training samples obtained from interacting with the traffic environment, the actor network and the critic network are trained to maximize the quadratic reward function determined by state errors and control inputs for generating intelligent control strategies. In particular, the effect of previous control strategies is considered in vehicle dynamics analysis, optimization problem formulation and Markov decision process (MDP) definition to compensate the performance degradation attributed to communication delays. Lastly, the effectiveness and convergence of the proposed DRL-based CCC controller are verified through numerical simulations with various conditions. The superior performance of the proposed algorithm is shown by comparing with existing traditional algorithms and state-of-the-art DRL algorithms.

Practical design of a time-varying iterative learning control law using fuzzy logic

Iterative Learning Control (ILC) is an intelligent control algorithm that can effectively handle a tracking error in any system that operates in a repetitive manner. In practice, it is hardly possible to implement a single gain learning control law to improve the tracking performance due to the existence of large transient growth. To prevent the growth, this paper proposes a time-varying learning control design using the unique concept of fuzzy logic control to track the desired trajectory as well as the desired control input signal. The proposed control design is developed on both serial and parallel ILC configurations. The two configurations are initially constructed and implemented on a robotic manipulator with the use of a single gain learning control law. To avoid bad transients, the gain adjustment mechanism based on fuzzy logic control is introduced to vary the learning gain in each time step for enhancing the robustness of the system. According to the simulation and experiment on a robotic manipulator, both ILC structures with the proposed mechanism achieve the desired learning performance without bad transients.

Numerical Simulation of Intelligent Fuzzy Closed-Loop Control Method for Radial–Axial Ring Rolling Process of Super-Large Rings

During the radial–axial ring rolling (RARR) process of super-large rings, abnormal deformation states such as instability and out of circularity often lead to rolling termination and quality fluctuation of ring products. In this work, an intelligent fuzzy closed-loop control method for RARR process of super-large rings is proposed, i.e., the ring’s offset adaptive fuzzy control (ROAFC) based on the regulation of the axial roll’s rotational speed and the ring’s circularity fuzzy control (RCFC) based on the regulation of the mandrel’s feed speed. In addition, a recursive average filtering algorithm is added to smooth the axial roll’s rotational speed and the mandrel’s feed speed according to the actual situation. Using the ABAQUS/Explicit software and its subroutine VUAMP, the intelligent fuzzy controller of the ring’s offset and circularity in the RARR process is designed, and the finite element (FE) model for RARR process of a Φ10 m super-large ring with an integrated intelligent fuzzy control algorithm is established. The variation laws of the ring’s offset and circularity error in the RARR process are studied with regard to different control methods such as conventional planning control (CPC), ROAFC, RCFC, and comprehensive control of ROAFC combined with RCFC (ROAFC + RCFC). The results obtained show that, compared with the CPC, the ring’s offset is reduced by 84.6% and the circularity error is decreased by 51.9% in the RARR process utilizing comprehensive control of ROAFC + RCFC. The research results provide methodological guidance for realizing the intelligent forming of super-large rings.

Long tunnel lighting environment improvement method based on multiple-parameter intelligent control: Considering dynamic changes in luminance difference

The difference between luminance in design versus road surface in the lighting environment of long tunnels often results in the road surface luminance not reaching safety standards. To solve this problem, this study proposes a method to improve the lighting environment of long tunnels. First, based on field tests and mathematical statistics, the main influencing factors of the luminance difference phenomenon are found to be (in descending order) the visibility level, traffic flow, interior zone luminance, and vehicle speed. Second, the relationship between tunnel visibility and road surface luminance is quantified, and the effect of luminance difference on road surface luminance is further investigated. Considering the characteristics of luminance difference, a dynamic optimization model of lighting luminance is established. Based on the typical lighting scenes of long highway tunnels, the optimization model is then integrated with two intelligent control algorithms to establish and optimize the lighting control model. Finally, based on a combination of the dynamic optimization model and intelligent control model, a method is introduced to improve the lighting environment of long tunnels, and its effectiveness is verified. The research results show that in different lighting scenes involving time periods and weather conditions, the proposed method can dynamically adjust the lighting luminance in a long tunnel to ensure that the average road surface luminance meets the safety luminance. It is shown that the safety luminance qualified rate increases by 39.58 %, 26.04 %, and 5.21 % on sunny, cloudy, and rainy days, respectively, and energy usage can be reduced by 21.59 %, 32.13 %, and 33.03 %, respectively.

Review on control systems and control strategies for excavators

The product performance of excavators continuously improves with industrial development. However, unsighted improvements in the performance of excavators cannot meet the emission standards. Therefore, considerable research on energy conservation, high efficiency, and accurate control strategy should be conducted. At present, power matching research is performed because of the inconvenience of energy transmission. The technical advantages of hybrid power provide a better research direction for the energy-saving research of excavators. Intelligent control algorithms, such as sliding mode control and time-delay control, are added to the control system to improve the trajectory accuracy in excavator trajectory control. The intelligent operation of excavators reduces the requirements of the operating environment and improves operation economy in remote, complex, and dangerous operation spaces. The research statuses of excavators in power matching, hybrid control, trajectory control, and intelligent autonomous operation are reviewed. Finally, six conclusions about the current research status are drawn.

AUTONOMOUS ROBOTIC SYSTEMS FOR PIPE INSPECTION USING MAPPING AND NONDESTRUCTIVE TESTING

This paper presents the development of a mapping and nondestructive technique (NDT) for the assessment of concrete pipeline networks. It focuses on the inspection of open-end culverts and storm drain systems using sensor networks. The network consists of 3D Lidar/3D camera/radar with NDT sensors. These sensors are mounted on autonomous vehicles that are set to reduce the extent of human operator involvement through intelligent control algorithms. More accurate remote monitoring is achieved by using smart robots and sensor networks equipped with advanced AI-based techniques. In the systems, ground robots are used with onboard sensing to enable environmental observation. The ground robots have the capability of remote communication, teleoperation, and the transmission of collected data. Effective automated mapping requires the flexibility to allow real-time changes in planning to accommodate unmodeled/unexpected uncertainties and disturbances. The autonomous system can be used repeatedly with reduced cost, primarily for maintenance. In addition, making the process autonomous can reduce the risk to human inspectors and save time during inspections. Also, it can be operated by personnel who require less training to obtain the data than standard inspection processes. The developed technology is demonstrated by actual field and lab testing. It provides accurate assessment tools for underground pipeline networks.

Joint Modeling of a Fuzzy Two-Connected Longitudinal Turning Control System in MSC. Adams and Matlab

Purpose of research. The article considers the possibility of developing and modeling a fuzzy bipartite control system of the turning process based on the joint application of the program for creating virtual models MSC.Adams and mathematical analysis package Matlab. The relevance of the research topic is related to the trend of application of artificial intelligence functions in domestic and foreign machine tool industry for compensation of temperature deformations, force and vibration disturbances, tool condition monitoring, adaptive control taking into account the actual state of the cutting process in real time. In designing new control systems of the turning process, an important task is to create mathematical and virtual models and synthesis of intelligent control algorithms, providing solutions to problems under conditions of uncertain perturbations.Methods. For the development of the mathematical model and its analysis basics of the theory of fuzzy sets in the problems of control, theory of metalworking, methods of mathematical modeling of control systems have been applied. The synthesis of fuzzy controller and virtual model have been developed with the help of modern applied software packages Matlab and MSC.Adams.Results. The fuzzy cutting control algorithm and its implementation in the Simulink environment with the data transfer to MSC.Adams, as well as the virtual prototype of the lathe in MSC.Adams are presented in the article. Graphs of the vibration movements of the cutting edge of the tool, changes in temperature and cutting force are given and analyzed.Conclusion. The results of model testing show that the use of joint modeling of fuzzy two-connected turning control system is possible to solve the problem of improving the efficiency of machining on the operating equipment under the influence of uncertain disturbances.

Electronic Controller Automatic Test System Based on Intelligent Control Algorithm

In order to improve the automatic test effect of the electronic controller, this paper combines the intelligent control algorithm to construct the automatic test system of the electronic controller. Moreover, the paper makes a detailed theoretical analysis of several basic problems relating to signal integrity and gives a general method to solve the main SI problems. In addition, this paper analyzes the principle of the microstrip transmission line parameter model and its relationship with the signal integrity and constructs an automated test system for an intelligent electronic controller. The simulation test results show that the electronic controller automated test system based on the intelligent control algorithm proposed in this paper can play an important role in the electronic controller automated test.

Design and analysis of a load frequency control system based on improved artificial intelligence control algorithm

In actual industrial control, the performance of the control system may be undermined or destabilized by the various complex nonlinear control processes. To ensure the stability of the control system, this paper improves the generalized active disturbance rejection control (GADRC). Firstly, the dead zone of the governor was converted to obtain the total transfer function of the control system, and the system stability was analyzed, considering the applicability of GADRC to non-minimum phase processes. Then, an error compensation strategy was proposed to control the system performance. Simulation results show that the improved GADRC can fully utilize the known information of the plant model, and obtain proper gains of the controller and the observer. In addition, the load frequency control (LFC) system can work effectively, when the dead zone of the governor has nonlinear processes. Finally, the proposed error compensation strategy can ensure the control performance of the system.

Research on Intelligent Algorithm of the AC Motor Speed Regulation System Based on the Neural Network

One of the most challenging control problems has been the speed control of an alternating current (AC) motor due to the highly nonlinear characteristics and many uncertain parameters including the magnetic flux, temperature-dependent rotor resistance, and the variable load. In this study, an intelligent control algorithm is proposed for the AC motor speed control using a PID controller-based backpropagation neural network (BP-NN). The momentum factor adaptive learning rate algorithm is introduced to improve the PID-based BP-NN. A simulation model representing the complete AC motor speed regulation system is established and tested using the MATLAB program. The simulation results show that this control strategy has strong adaptability and robustness when the controlled object is unknown or the parameters change. Compared with other PID and neural network parameter adjustment methods, the model has the potential to intelligently regulate the speed of the AC motor.

AUTONOMOUS ROBOTIC SYSTEMS FOR PIPE INSPECTION USING MAPPING AND NONDESTRUCTIVE TESTING

This paper presents the development of a mapping and nondestructive technique (NDT) for the assessment of concrete pipeline networks. It focuses on the inspection of open-end culverts and storm drain systems using sensor networks. The network consists of 3D Lidar/3D camera/radar with NDT sensors. These sensors are mounted on autonomous vehicles that are set to reduce the extent of human operator involvement through intelligent control algorithms. More accurate remote monitoring is achieved by using smart robots and sensor networks equipped with advanced AI-based techniques. In the systems, ground robots are used with onboard sensing to enable environmental observation. The ground robots have the capability of remote communication, teleoperation, and the transmission of collected data. Effective automated mapping requires the flexibility to allow real-time changes in planning to accommodate unmodeled/unexpected uncertainties and disturbances. The autonomous system can be used repeatedly with reduced cost, primarily for maintenance. In addition, making the process autonomous can reduce the risk to human inspectors and save time during inspections. Also, it can be operated by personnel who require less training to obtain the data than standard inspection processes. The developed technology is demonstrated by actual field and lab testing. It provides accurate assessment tools for underground pipeline networks.

Experimental investigation and comparative harmonic optimization of AMLI incorporate modified genetic algorithm using for power quality improvement

A super-lift mechanism has made tremendous progress in DC/DC conversion technology. In comparison to the asymmetrical form of MLI, the novel Asymmetric Multilevel Inverter (AMLI) technology proposes a minimized number of components. The Fuzzy-PI (Proportional integral) and Modified Genetic Algorithm (MGA) utilizes to minimize the harmonic content considerably using a variety of modulation index and firing angle values in open-loop and closed-loop control. This architecture for designing single-phase 7-level AMLI with an intelligent algorithm proposed for Renewable Energy (RE) applications. This circuit uses a single MOSFET switch with less switching stress and a single DC source. The effectiveness of the proposed MGA optimization eliminates the lower-order harmonics. MGA and Fuzzy-PI based Distributed Power Flow Intelligent Control (DPFIC) algorithms are applied with multilevel structures while maintaining the fundamental frequency for both MATLAB platform and hardware implementation. During this analysis, the losses is also find to investigate the influence of modulation index and output power factor on inverter efficiency. Simulations and experimental findings confirm the proposed inverter capacity to create high-quality multilayer output voltage. However, the proposed closed loop simulation circuit gives 0.47% minimum THD level, and 10.4% in experimental results.

Construction and Application of Rehabilitation Training System Based on Intelligent Measurement and Control

Rehabilitation training plays a very important role in the treatment of movement disorders. In order to meet the needs of modern rehabilitation training, this paper designs a rehabilitation training system based on intelligent measurement and control. The system realizes software and data construction through C/S architecture and B/S architecture. The intelligent measurement and control technology is used to identify and compare the patient’s movements, obtain the matching degree of the movements, correct the movements, and evaluate the training results. The rehabilitation system is constructed through the customized subsystem of rehabilitation specialist, patient training subsystem, and background management subsystem. Through experimental tests, the conclusions are drawn: first, the intelligent measurement and control algorithm has good convergence speed and network generalization ability. The average correct rate is 93.9% for the patient group and 94.5% for the healthy people, and the output results obtained are close to the ideal value. Second, the pressure test results show that the system can run stably when the concurrency is 100, 200, 500, and 1000. Third, the software design of the rehabilitation training system can meet the requirements in terms of usability, the accuracy rate is above 80%, and the user satisfaction is high. Fourth, compared with before training, the time for subjects to maintain balance during and after training increased significantly, indicating that the rehabilitation exercise training system has the ability to train standing static balance, and the training effect is significant. The system improves the effect and efficiency of rehabilitation training and has broad application prospects in the field of limb rehabilitation.

OTCS: An Online Target Close-Up Shooting Method Based on the UAV Image System

Unmanned aerial vehicles (UAV) equipped with intelligent gimbaled cameras can take images and identify specific targets from them in real-time. However, the targets in the images are generally small and difficult to be seen. This paper proposes an Online Target Close-up Shooting (OTCS) method to solve the problem of taking high-definition (HD) close-up images of the targets. Firstly, we build an online target close-up shooting model, which uses deep learning (DL) algorithm to identify the targets from the images taken online. Then, an intelligent control algorithm is presented to control the gimbaled camera to take a close-up shot of the target, which considered the target being fixed on the ground statically and moving at an ununiform speed. Finally, we build a UAV-based prototype system and conduct a series of experiments to verify our proposal. Experimental results show that our proposed method is feasible and outperforms traditional methods in effectiveness.

OXIDATION PROCESSES OF HIGH-ALLOYED ALLOYS OF WORKING LAYER AND THE INTERPHASE SURFACES OF BIMETALLIC PAIRS

Methods of improving the quality of regulation of technological parameters by combining various intelligent control algorithms in one automation system, which helps to reduce energy costs by 10-13%, are analyzed. It has been established that heating and ventilation systems have the highest energy consumption for indoor buildings (on average, more than 4,000 m3 of natural gas and almost 1,000 kWh of electricity are consumed per day for heating and ventilation in an industrial greenhouse. Correlation analysis of links between external disturbances and energy costs that ensure compliance with the technology of plant production, confirmed the hypothesis of conditions of uncertainty in the operation of industrial greenhouses are formed by random disturbances, incomplete information about the biological component, with linear correlation coefficients not exceeding r<0.35. both for forecasting energy costs and for the formation of energy efficient management strategies. Based on the use of fuzzy logic methods and genetic algorithm, models for finding and using optimal parameters of PI controller settings adapted to changes in the operating conditions of the automation system have been developed and studied. This provides better regulation in conditions of uncertainty, the time of regulation, over-regulation is reduced by two to three times. To create an energy-efficient microclimate management system in industrial greenhouses, operating in conditions of uncertainty, a neural network model for predicting the energy consumption of natural gas and electricity has been developed. The input parameters of the neural network forecasting model are: the value of external and internal air temperatures of the greenhouse, the value of relative humidity, the solar radiation absorbed by the greenhouse and the level of carbon dioxide in the greenhouse. The outputs of the forecasting model are the values of natural gas and electricity costs. The structural and functional scheme of the temperature and humidity control automation system in industrial greenhouses has been improved by combining intelligent algorithms for stabilizing the operation of technological equipment at the lower management level and optimizing energy costs by forecasting them at the upper level. The introduction of such a system saves up to 13% on natural gas for heating and up to 10% on electricity.

Automatic Intelligent Control System Based on Intelligent Control Algorithm

In order to improve the effect of automatic intelligent control, this paper improves the intelligent vector control algorithm, analyzes the performance of the algorithm with common power pulses as an example, and obtains an improved intelligent vector control algorithm suitable for modern intelligent systems. By making two mutually adjacent vectors nonidentical combination in time, the two vectors have the difference between zero and nonzero, and the vector can be rotated into a certain area. In addition, with the support of improved algorithms, this paper gets an automated intelligent control system based on intelligent control algorithms. Finally, this paper verifies the effect of the intelligent control system through experimental research. The analysis of the test results shows that the automated intelligent control system proposed in this paper has a better effect in intelligent control.

Intelligent Optimization Control Strategy for Secondary Pollution of Flue Gas in Municipal Solid Waste Incineration

Aiming at the problem of serious secondary pollution caused by improper control strategy in the waste incineration process, this paper proposes an intelligent optimization control strategy for secondary pollution of flue gas in municipal solid waste incineration. Firstly, the control difficulties of waste incineration and cybernetic characteristics of combustion process in incinerators are analyzed, and the basic algorithm based on human simulated intelligent control is proposed accordingly. Then, genetic algorithm is used to improve particle swarm algorithm and search ability in wide space, which can avoid the premature phenomenon of local optimum. Finally, improved particle swarm algorithm is utilized to optimize the parameters in human simulated intelligent control algorithm to realize intelligent optimization control in the waste incineration process. Based on MATLAB simulation platform, experimental results show that when delay parameters and time constant change significantly, there are large disturbances, and the process response of proposed strategy is fast and stable, which is superior to other comparison strategies.

Thrust Prediction Method of Jet Propulsion for Transformer Internal Inspection Robot

Abstract Robot technology has been successfully applied to the internal inspection of large power transformers. In order to further improve the level of robot intelligent control and realize robot motion control accuracy of the floating state, this paper analyzes the factors that affect the jet thrust of the robot propulsion system on the developed spherical transformer internal inspection robot platform based on jet propulsion, and builds an experimental platform. Combining experimental data to establish jet thrust equation. On this basis, the working state of the robot power system is further analyzed, the equivalent discharge model of the robot battery is established, the battery state parameters are introduced into the jet thrust equation. Finally, a mathematical model of jet thrust prediction is established that comprehensively considers the robot power system, control system, and environmental parameters. The establishment of this model lays a good theoretical foundation for the establishment of the robot’s precise dynamics model, fine motion control and intelligent control algorithm.

A Novel RF Energy Harvester for Event-Based Environmental Monitoring in Wireless Sensor Networks

Perpetual energy source is the major research issue in the research community for energy-constrained wireless sensor network (WSN) and IoT applications. Radio-frequency (RF) energy harvesting has become a technology available round the clock due to increased communication activities. However, efficient RF energy harvesting for low-power regions [ <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$PRF \leq (0 {\mathrm{ dBm}})$ </tex-math></inline-formula> ] is still a challenge. This article proposes a novel RF energy harvesting technique along with an intelligent dynamic energy flow control algorithm. The algorithm works with dedicated energy flow control hardware. It is also able to provide perpetual life time to the ultralow power sensor nodes. A close approximation of the size of the RF energy harvester array is proposed for the perpetual lifetime of the WSN node deployed with wake-up radio for event-based monitoring applications. The model is verified by mathematical analysis, simulation, and fabricated prototype results for the operability in the given system. It is found that the circuit provides improved dc output voltage over the previous benchmark at low power in the range of −20 to 0 dBm. The system can charge a 2.7 V, 350 F supercapacitor with the rate <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.16~\mu \text{V}$ </tex-math></inline-formula> /s at 0 dBm, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.125~\mu \text{V}$ </tex-math></inline-formula> /s at −6 dBm, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.08~\mu \text{V}$ </tex-math></inline-formula> /s at −20 dBm. To prove the utility of the proposed idea, an RF field survey is also conducted and found that the proposed circuit can harvest energy from high power region (≥ −6 dBm) for LTE850 and LTE 900 bands.

A Comparative Study of DC-DC Buck, Boost, and Buck-boost Converters with Proportional-integral, Sliding Mode, and Fuzzy Logic Controllers

Background: DC-DC converters are utilized in a wide range of industrial and commercial applications such as automobiles, renewable energy systems, DC motor control, portable chargers, uninterruptable power supply, etc. because of their efficiency, ease of use, simpler circuits, and cheaper solution. The most common ones are the buck, boost, and buck-boost converters. Methodology: In this paper, the implementation of these converters has been proposed using advanced controllers: Proportional plus Integral (PI), Sliding Mode Control (SMC), and Fuzzy Logic Control (FLC). The controllers have been implemented with practical circuit values in MATLAB Simulink. Results: The results show that all proposed controllers can track the input set-point voltage. The PI controller response was superior in eliminating the steady-state error in all converters. However, it showed greater overshoot and ripple factor in boost operation. The SMC controller response was superior in terms of the shortest settling time. However, it showed the greatest peak time in buck converter and boost mode of the buck-boost converter. The FLC controller showed the highest settling time and steady-state error in all operations. Novelty and Significance: The proposed work is novel as compared to others available in the literature such that no such comprehensive study was found with all types of DC-DC converters together up to our best knowledge. The proposed work is significant as it will give complete guidance regarding simulation implementation of DC-DC converters with advanced intelligent control algorithms.