Self-adaptive Control System Research Articles

Improved scheme of membership function optimisation for fuzzy air‐fuel ratio control of GDI engines

This study researches an improved scheme of membership function optimisation (MFO) for fuzzy air–fuel ratio (AFR) control of gasoline direct injection (GDI) engines based on correspondence analysis (CA). This proportional–integral-like fuzzy knowledge-based controller (FKBC) optimised by the proposed scheme can further optimise AFR control performance while maximising conversion efficiency of the three-way catalyst to eliminate the exhaust emissions in real time. Different from the conventional experience-based membership function (MF) design method for an FKBC, the proposed MFO scheme uses CA approach and can visualise the relationship between engine step gain scenarios and designed MF patterns to precisely determine its scalar parameters for AFR regulation of GDI engines. Within this context: (i) specialised MFs for self-adaptive AFR control system of a GDI engine are designed with weight distribution; (ii) based on designed scalar parameters, the CA model with taxonomic dimensions is built for acquiring a customised MF to counter transient scenario changes more effectively; (iii) the engine controller with the proposed scheme is real time validated in a production V6 GDI engine, and its advantage in terms of engine transient control performance is further demonstrated by comparing with a benchmark controller designed based on experience.

A Review of the Self-Adaptive Traffic Signal Control System Based on Future Traffic Environment

The self-adaptive traffic signal control system serves as an effective measure for relieving urban traffic congestion. The system is capable of adjusting the signal timing parameters in real time according to the seasonal changes and short-term fluctuation of traffic demand, resulting in improvement of the efficiency of traffic operation on urban road networks. The development of information technologies on computing science, autonomous driving, vehicle-to-vehicle, and mobile Internet has created a sufficient abundance of acquisition means for traffic data. Great improvements for data acquisition include the increase of available amount of holographic data, available data types, and accuracy. The article investigates the development of commonly used self-adaptive signal control systems in the world, their technical characteristics, the current research status of self-adaptive control methods, and the signal control methods for heterogeneous traffic flow composed of connected vehicles and autonomous vehicles. Finally, the article concluded that signal control based on multiagent reinforcement learning is a kind of closed-loop feedback adaptive control method, which outperforms many counterparts in terms of real-time characteristic, accuracy, and self-learning and therefore will be an important research focus of control method in future due to the property of “model-free” and “self-learning” that well accommodates the abundance of traffic information data. Besides, it will also provide an entry point and technical support for the development of Vehicle-to-X systems, Internet of vehicles, and autonomous driving industries. Therefore, the related achievements of the adaptive control system for the future traffic environment have extremely broad application prospects.

Coordinated actor model of self-adaptive track-based traffic control systems

Self-adaptation is a well-known technique to handle growing complexities of software systems, where a system autonomously adapts itself in response to changes in a dynamic and unpredictable environment. With the increasing need for developing self-adaptive systems, providing a model and an implementation platform to facilitate integration of adaptation mechanisms into the systems and assuring their safety and quality is crucial. In this paper, we target Track-based Traffic Control Systems (TTCSs) in which the traffic flows through pre-specified sub-tracks and is coordinated by a traffic controller. We introduce a coordinated actor model to design self-adaptive TTCSs and provide a general mapping between various TTCSs and the coordinated actor model. The coordinated actor model is extended to build large-scale self-adaptive TTCSs in a decentralized setting. We also discuss the benefits of using Ptolemy II as a framework for model-based development of large-scale self-adaptive systems that supports designing multiple hierarchical MAPE-K feedback loops interacting with each other. We propose a template based on the coordinated actor model to design a self-adaptive TTCS in Ptolemy II that can be instantiated for various TTCSs. We enhance the proposed template with a predictive adaptation feature. We illustrate applicability of the coordinated actor model and consequently the proposed template by designing two real-life case studies in the domains of air traffic control systems and railway traffic control systems in Ptolemy II.

Privacy-preserving smart IoT-based healthcare big data storage and self-adaptive access control system

In this paper, a privacy-preserving smart IoT-based healthcare big data storage system with self-adaptive access control is proposed. The aim is to ensure the security of patients’ healthcare data, realize access control for normal and emergency scenarios, and support smart deduplication to save the storage space in big data storage system. The medical files generated by the healthcare IoT network are encrypted and transferred to the storage system, which can be securely shared among the healthcare staff from different medical domains leveraging a cross-domain access control policy. The traditional access control technology allows the authorized data users to decrypt patient’s sensitive medical data, but also hampers the first-aid treatment when the patient’s life is threatened because the on-site first-aid personnel are not permitted to get patient’s historical medical data. To deal with this dilemma, we propose a secure system to devise a novel two-fold access control mechanism, which is self-adaptive for both normal and emergency situations. In normal application, the healthcare staff with proper attribute secret keys can have the data access privilege; in emergency application, patient’s historical medical data can be recovered using a password-based break-glass access mechanism. To save the storage overhead in the big data storage system, a secure deduplication method is designed to eliminate the duplicate medical files with identical data, which may be encrypted with different access policies. A highlight of this smart secure deduplication method is that the remaining medical file after the deduplication can be accessed by all the data users authorized by the different original access policies. This smart healthcare big data storage system is formally proved secure, and extensive comparison and simulations demonstrate its efficiency.

Self-adaptive control of shearer based on cutting resistance recognition

With the rapid development of automation technology, self-adaptive control methods have been widely used for drum shearers for the rapid development of automation technology. The main objective of this paper is to propose a control system wherein the motion parameters can be adjusted adaptively with the variation in the cutting resistance. The cutting resistance recognition is achieved by using the theory of wavelet packet decomposition and back propagation neural network. The motion parameters comprising hauling and drum rotation speeds are optimized by using particle swarm optimization algorithm. Different speed-control strategies are proposed to meet the variations of the cutting resistance. A self-adaptive control system model is developed to realize the self-adaptive control of the drum shearer. Finally, a simulation test is conducted to validate the effectiveness of the cutting resistance recognition method. The speed-control strategies are validated through an experimental bench test.

A Semiactive and Adaptive Hybrid Control System for a Tracked Vehicle Hydropneumatic Suspension Based on Disturbance Identification

The riding conditions for a high-speed tracked vehicle are quite complex. To enhance the adaptability of suspension systems to different riding conditions, a semiactive and self-adaptive hybrid control strategy based on disturbance velocity and frequency identification was proposed. A mathematical model of the semiactive, self-adaptive hybrid suspension control system, along with a performance evaluation function, was established. Based on a two-degree-of-freedom (DOF) suspension system, the kinematic relations and frequency zero-crossing detection method were defined, and expressions for the disturbance velocity and disturbance frequency of the road were obtained. Optimal scheduling of the semiactive hybrid damping control gain (csky, cground, chybrid) and self-adaptive control gain (cv) under different disturbances were realized by exploiting the particle swarm multiobjective optimization algorithm. An experimental study using a carefully designed test rig was performed under a number of typical riding conditions of tracked vehicles, and the results showed that the proposed control strategy is capable of accurately recognizing different disturbances, shifting between control modes (semiactive/self-adaptive), and scheduling the damping control gain according to the disturbance identification outcomes; hence, the proposed strategy could achieve a good trade-off between ride comfort and ride safety and efficiently increase the overall performance of the suspension under various riding conditions.

Study on Self-Adaptive Control System - View of Internal Adaptation

Study on Self-Adaptive Control System - View of Internal Adaptation

Special issue: emerging technologies for intelligent transportation

Special issue: emerging technologies for intelligent transportation

Study on Intelligent Self-Adaptive Control System

With the rapid development of China's industry, the use of the control system has become more and more extensive. However, with the complicating of the production system, the traditional control system has been unable to meet the needs of the current industry. Effectively bring the genetic algorithm of the neural network into the control system can solve this problem. Here, it firstly describes the neural network, genetic algorithm principle, operation procedures and the characteristics; secondly, analyzes the principle and lack of conventional PID controller; finally, effectively combines genetic algorithm and controller together, forming a closed loop, strengthening the control of parameters, and giving a code description of the genetic algorithm. This paper plays a certain positive role for industrial engineers and programmers.

Design and Dynamic Analysis of Magnetorheological Shock Absorber Based on the Piezoelectric Technology

Based on the piezoelectric effect and magnetorheological technology, a parameter adaptive shock absorber was designed. Considering the practical working conditions of landing gear, mathematical model of the parameter adaptive shock absorber was built. Then on the basis of LMS Imagine.Lab AMESim software, the whole combined simulation model of self-adaptive control system was constructed and the drop dynamic simulation was conducted. Results showed that compared with passive control system, parameter adaptive shock absorber reduced the peak of axial force by 10.5% and increased the absorber efficiency by 5.6%.

Research on the Elongation Control System of Tension Leveller for Continuous Pickling Line

Elongation control played a vital role for the production of cold-rolled strip. In the production process, especially during tension disturbances or parameter variations, the conventional PID control method can not meet the actual demand well. Therefore, the intelligent control algorithm was introduced in this paper. A fuzzy self-adaptive PID closed-loop control strategy which combines the fuzzy control algorithm with the conventional PID control algorithm was applied to elongation control system. It is proved in the simulation study that the fuzzy self-adaptive PID control system has both high dynamic performance and static performance as well as strong robustness, which can greatly improve control accuracy and anti-jamming capability of elongation control system of the tension leveller.

Study on Fuzzy Self-Adaptive PID Control System of Biomass Boiler Drum Water

The paper discusses the features of the Biomass Boiler drum water level. Conventional PID Control System can not reach a satisfaction result in nonlinearity and time different from Biomass Boiler Drum Water Control System. In this study, a kind of fuzzy self-adaptive PID controller is described and this controller is used in biomass boiler’s drum water level control system. Using the simulink tool of MATLAB simulation software to simulate the fuzzy adaptive PID and conventional PID control system, the result of the comparison shows that the fuzzy self-adaptive PID has the strong anti-jamming, flexibility and adaptability as well as the higher control precision in Biomass Boiler Drum Water.

Self-adaptive control of a bluff body wake by means of porous flaps

The effects resulting from the use of an innovating self-adaptive passive control system are investigated. The system studied here, is constituted of a couple of hinged porous flaps positioned symmetrically on each side of a bluff body. Its originality consists on its design which relies on a biomimetic approach, as well as its ability to self-adapt to the flow conditions. The comparison between the results obtain for the controlled model and the uncontrolled one, over the range of Reynolds numbers 2.10 4 to 8.10 4 , lead to an average mean drag reduction of 22%. An investigation of the mean pressure distribution in the near wake of the body confirmed that flow topology at large scale is strongly modified. This study focus on the attenuation of pressure fluctuations observed at the centre of the cylinder base. This parameter is considered a valid indicator of a strong reduction of the drag fluctuations.

The Research of Induction Motor Vector Control System Based on Fuzzy Self-Adaptive Pi Complex Control Algorithm

According to disadvantages that traditional PID control is difficult to adjust PID parameter for nonlinear and time-variant induction motor speed control system, this paper designs fuzzy self-adaptive PI speed control system with Double Closed-Loop Fuzzy Controller by combining fuzzy control and PID control. The MATLAB simulation results show that this controller has better stability and self-adaptive property and well improved the dynamic performance of the system compared with conational controller.

Study of Self-Adaptive Control System in CNC Machine

A set of solving scheme of full-closed loop adaptive servocontrol control system, which can on-line monitoring and real-time control ,was proposed based on the analysis of a number of factors that affect the processing quality of high-precision slender shaft during CNC machining process.Adopt the "PC+motion controller" mode,on-line detection accuracy index of the workpiece, and feed bake the detection signal to CNC system, through the automatic processing of CNC system, timely adjust cutting parameters, so that the detected error factors remain within a reasonable limits, to ensure the stability of the system and improve the accuracy of workpiece.

The Construction Control of Long-Pan Continuous Beam Bridge Based on Self-Adaptive Control Method

The construction control is very important to the construction quality of long-pan continuous beam bridge, so a self-adaptive construction control analysis method is proposed. A construction self-adaptive control system is established in this paper, which is based on the self-adaptive control theory and the characteristic of long-pan continuous beam bridge construction. The sensitivity analysis method is used to choose the most important parameters. The least squares method is utilized to parameter identification. The construction forecast control is also included in the construction self-adaptive control system. The analysis result of a beam bridge shows that the self-adaptive construction control analysis method is feasible and reasonable.

Self-Adaptive Control System of Grain Drying Device

A kind of self-adaptive control system for grain dryer was developed based on grain deep-bed drying process analytical theory and precise detection technology of high-moisture-content grain. It can change the system automatically to ensure the optimization of working conditions according to real-time input moisture content and output status. The control system was reliable, stable, accurate, and practical in industrial application.

Design of self-adaptive control system for grain dryer based on deep-bed analytical ventilating drying theory

Design of self-adaptive control system for grain dryer based on deep-bed analytical ventilating drying theory

Experimental results of a self-adaptive integrated control system in buildings: a pilot study

An innovative integrated system for building energy and comfort management has been implemented on two occupied offices of the LESO-PB building. The measurements concern both energy consumption and comfort assessment. The results show that the system saves 19% of total energy consumption in comparison with a conventional heating controller with night setback. The thermal comfort level has been kept at a high level and the visual comfort has even been improved by the integrated control system. These good results are mainly explained by the integration concept of the overall controller and the energy efficient control of the blinds, which allow an optimal use of the passive solar gains. The user’s satisfaction has been studied; it highlights the need of a long-term adaptation of the controllers to the user’s wishes and preferences for a better acceptance of automatic control systems.

Self-adaptive vibration control of smart composite beams using recurrent neural architecture

A self-adapting vibration control system is developed for damping augmentation in smart composite beams. The conventional vibration control approaches are limited by the requirement of an explicit and often accurate identification of the system dynamics and subsequent offline design of an optimal controller. In the present study a self-adapting vibration control system is developed. A hybrid system comprised of a dynamic diagonal recurrent neural network (DRNN) and an adaptable feed forward neural network is used to control the beam vibrations. Sensing and actuation are achieved using piezoelectric sensors and actuators. A finite element model based on a higher-order shear deformation theory is used to simulate the vibration response of laminated composite beams with integrated piezoelectric sensors and actuators. The dynamic effects of mass and stiffness of the piezoelectric patches are considered in the model. The performance of the DRNN controller is verified for arbitrary initial conditions and loadings. A robustness study including the effects of tip mass, structural parameter variation and partial loss of sensor output is performed. The performance with partial failure of control actuation is also examined. It is seen that the robustness and control capabilities of the hybrid control system are excellent.