Fuzzy Rule Table Research Articles

Intelligent Prevent the Risk of Carcinoma of the Lung Progression

Smog hanging over cities is the most familiar and obvious form of air pollution. The effects of inhaling particulate matter have been studied in humans and animals and include asthma, lung cancer, cardiovascular issues, and premature death. There are, however, some additional products of the combustion process that include nitrogen oxides and sulfur and some un-combusted hydrocarbons, depending on the operating conditions and the fuel-air ratio. Tuning the fuel to air ratio caused to control the lung cancer. Lung cancers are tumors arising from cells lining the airways of the respiratory system. Design of a robust nonlinear controller for automotive engine can be a challenging work. This research paper focuses on the design and analysis of a high performance PID like fuzzy controller for automotive engine, in certain and uncertain condition. The proposed approach effectively combines of design methods from linear Proportional-Integral-Derivative (PID) controller and fuzzy logic theory to improve the performance, stability and robustness of the automotive engine. To solve system’s dynamic nonlinearity, the PID fuzzy logic controller is used as a PID like fuzzy logic controller. The PID like fuzzy logic controller is updated based on gain updating factor. In this methodology, fuzzy logic controller is used to estimate the dynamic uncertainties. In this methodology, PID like fuzzy logic controller is evaluated. PID like fuzzy logic controller has three inputs, Proportional (P), Derivative (D), and Integrator (I), if each inputs have N linguistic variables to defined the dynamic behavior, it has N × N × N linguistic variables. To solve this challenge, parallel structure of a PD-like fuzzy controller and PI-like fuzzy controller is evaluated. In the next step, the challenge of design PI and PD fuzzy rule tables are supposed to be solved. To solve this challenge PID like fuzzy controller is replaced by PD-like fuzzy controller with the integral term in output. This method is caused to design only PD type rule table for PD like fuzzy controller and PI like fuzzy controller.

Design of Fuzzy Logic Control System for Segway Type Mobile Robots

Studies on the control of inverted pendulum type systems have been widely reported. This is because this type of system is a typical complex nonlinear system and may be a good model to verify the performance of a proposed control system. In this paper, we propose the design of two fuzzy logic control systems for the control of a Segway mobile robot which is an inverted pendulum type system. We first introduce a dynamic model of the Segway mobile robot and then analyze the system. We then propose the design of the fuzzy logic control system, which shows good performance for the control of any nonlinear system. In this paper, we here design two fuzzy logic control systems for the position and balance control of the Segway mobile robot. We demonstrate their usefulness through simulation examples. We also note the possibility of simplifying the design process and reducing the computational complexity. This possibility is the result of the skew symmetric property of the fuzzy rule tables of the system.

A Mobile Robot Path Tracking Method Based on Fuzzy Logic and Artificial Emotion

Design a subsection fuzzy controller for autonomous mobile robots based on location sensors. First section the controller produced some fuzzy output and part of the output was used for setting up an emotion factor called E. In the second section, E has an effect on the controller, and the mobile robot can move more like a car being driven by human. As the fuzzy controller is down to subsections, each of the section has a simpler fuzzy rule table. The simulation experiment data showed that this kind of control method for path tracking could fit the path better with short response time and strong robustness. It was more flexible and effective on processing different corners during tracking compared to conventional fuzzy control method.

Fuzzy Control Analysis with Back-EMF in Washing Machines

This paper puts forward a method of detecting material and quantity of the cloth in washing machine based on detection motor back EMF, not only can obtain the pulse number of reporting the information quality and quantity of the cloth, but also can collect information of pulse width, the pulse cycle , help to improve the detection accuracy of the material and quantity of the cloth. On this basis, This article also gives a specific control rules of the fuzzy control of automatic washing machine , With this fuzzy control rule table, off-line Fuzzy control response can be obtained. Finally enquiry form used to Program of assembly language, determined material and quantity and dirty degree of the cloth, to achieve the best effect of clothes washing by selecting water level, flow intensity, put the amount of detergent and washing time DOI : http://dx.doi.org/10.11591/telkomnika.v12i5.5098

The Design of Temperature and Humidity Control System of Fresh-Keeping Storage Based on Fuzzy PID Inference Algorithm

At present, the preservation methods of fresh fruits and vegetables to be stored are mainly to cold storage in our country, so the lowed temperature would tend to undermine the inside nutritional ingredient of the fruits and vegetables, and the perishable and other shortcomings. In order to overcome the shortcomings of the traditional fresh-keeping storage, we developed a control system based on fuzzy PID (Proportion Integration Differentiation) inference algorithm for temperature and humidity. Fuzzy PID inference algorithm, by changing the parameters of fuzzy control rule table and adjust the PID system, can adapt to the different requirements of fruits and vegetables in different periods of storage. The experimental result and matlab/simulink simulation show that this method has high precision and rapid responses. It can meet the demand of temperature and humidity control through Fresh-keeping Storage and achieve control precision of 0.4°C and less than ±3%RH for temperature and relative humidity in practical application, respectively.

The Research of Air Bags Overturning Device Based on Fuzzy Control

The fuzzy control algorithm to work out the fuzzy control rule table and query tables, combined with the Matlab simulation systems established fuzzy to control airbag flip Simulation experimental system, the simulation data obtained through this system, the ultimate combination of the S7-200 programmable controller and force sensorsexperimental hardware system verification of experimental procedures.

Modeling and Fuzzy Control Research of Power Plant Boiler Combustion System

According to the multivariable coupling, large delay, nonlinear, time-varying and other difficulties of a thermal power plant boiler combustion system, a kind of technology based on fuzzy control of a thermal power plant boiler combustion system was presented. Fuzzy control technology was used to research and determine fuzzy control rule tables. Multivariable static and dynamic models were given to boiler combustion system. Results of simulation show that boiler combustion system control precision was effectively improved and good effects in production and application were got.

Design of Backing-Up Fuzzy Controllers Based on Variable Universe of Discourse

Abstract Fuzzy controllers with variable universe of discourse (VUD) have been applied in many fields of intelligent controlling because of their high-accuracy performance. This paper provides a lookup table method to design backing-up fuzzy controllers based on VUD. By setting a set of random start points, input–output data pairs are obtained using test-driving method. One data pair defines one fuzzy rule and also assigns the strength of every fuzzy rule. Conflicting fuzzy rule groups are integrated into one fuzzy rule by selecting the one with the maximum strength. A fuzzy rule table is built by the fuzzy rules deduced from input–output data pairs. Simulation experiments show that the VUD fuzzy controller outperforms the general fuzzy controller in accuracy at the final position of the parking lot.

Design and Implementation of High Power DC Motor Control System

The paper designs a high power DC motor closed-loop control system, mainly including design of H bridge drive circuit, speed detection circuit and signal conditioning circuit. In order to improve the performance of motor running, one proposes a fuzzy adaptive PID control algorithm. Firstly, one determines the degree of membership of the input values to defined fuzzy sets, then constructs the fuzzy rule table according to experience and experiments, finally, makes defuzzification for every parameter of PID controller, that is, Kp, Ki, Kd. Experiments show that both hardware implementation and software control algorithms are reliable, the running performance of the system is robust before or after adding load.

Fuzzy based Optimization for Power Management in Wireless Sensor Networks

Wireless Sensor Networks (WSN), sensor node deployment is essential for maximizing the coverage and detection probabilities. But the existing optimization solution suffers from limited energy storage, node death, increased network traffic etc. To solve these issues, we propose a fuzzy based optimization model for power management in wireless sensor networks. The objectives considered in the paper include maximizing network coverage, connectivity, network lifetime and minimizing traffic load. A fuzzy rules table is constructed with the input parameters such as node degree, link quality, residual energy and traffic rate. Depending upon the outcome of the fuzzy logic, the nodes are categorized into good, normal and bad. After the initial deployment of good nodes, the SLEEP and WAKEUP Procedure is applied to maximize the lifetime of wireless sensor networks. By putting nodes to sleep when there are no events, the energy consumption of the sensor nodes can be significantly reduced. In SLEEP and WAKEUP Procedure, asynchronous type is used; it allows each node in the wireless sensor network to set its own SLEEP and WAKEUP schedule independently in order to save energy. Simulation result shows that the proposed Procedure provides maximization of the lifetime of Wireless Sensor Network (WSN).

Research and Application of Fuzzy Control in Boiler Combustion System Based on ACA

In the complex control processes of the industrial boiler combustion system with non-linear, time-varying and multivariable, aim at the original control parameters fixed, slow response, delay regulation and other problems, this paper introduces a control method that combines ant colony algorithm with fuzzy PID control, improves the design of fuzzy control rules table and optimizes the PID control parameters. Simulation results show that this control scheme is superior to conventional PID control and fuzzy PID control.

Simulation and Application on the Adaptive Fuzzy PI Control in Low-Rise Elevator Speed Regulation System

The elevator has become an important vertical transport tool, its control effect of speed regulation attracted much attention. The structure of control system about low-level elevator was introduced. For starting and braking frequently and the load changes greatly, the traditional PI control strategy is difficult to achieve the desired control effect of the elevator. The fuzzy inference rule table was established which based on fuzzy control theory. The adaptive fuzzy PI controller was designed. The simulation research and application using the adaptive fuzzy PI control strategy in brushless DC motor systems was performed. The proposed method was validated through simulation control experiments and application that the adaptive fuzzy PI can satisfy the elevator high-performance speed requirements and have very good using value.

Capacity control of a heat pump system applying a fuzzy control method

Abstract This paper presents the capacity control of a heat pump system using the discharge superheating of the compressor, concerns the design of a fuzzy controller for a heat pump at the outlet of a compressor and compares an optimized fuzzy controller with one that is not optimized. The design of a controller is based on a proper system identification model which determines the system modeling from inputs and outputs and completes the state-space system modeling. The type of input signals is pseudo-random binary sequence and the order of system is 7. The fuzzy controller is selected to modulate the discharge superheating of the compressor, and then, genetic algorithm is used as a searching tool for obtaining the optimized fuzzy rule table. The experiment results show that the optimized fuzzy controller makes undershoot and overshoot alleviated significantly in the discharge superheating, outlet temperature of secondary fluid and refrigerant mass flow rate.

DSP-based self-organising fuzzy controller for active suspension systems

This study proposed a self-organising fuzzy controller (SOFC) for controlling an active suspension system to evaluate its control performance. During the control process, the SOFC continually updated the learning strategy in the form of fuzzy rules. The fuzzy rule table of this SOFC could be initially set to zero. This not only overcame the difficulty in finding appropriate membership functions and control rules for designing a fuzzy controller, but also solved the database problem where the fuzzy rules of a fuzzy controller, once determined, remained fixed and could not suitably regulate them in real time to optimise the dynamic response of the system required to gain the desired control performance. To demonstrate the applicability of the proposed SOFC for active suspension systems, a quarter-car hydraulic-servo suspension system was designed and constructed to evaluate the feasibility of active suspension control. Additionally, to conform to real-time application requirements in the vehicular industry, the SOFC was implemented with a digital signal processor to control the hydraulic-servo suspension system so that the control performance could be determined. The SOFC has shown a better control performance in suppressing the vibration amplitude of the vehicle body for enhancing the structural safety of the vehicle and increasing the life of the suspension system. It also effectively suppressed the amplitude of the vehicle body acceleration and reduced the tire deflection for improving the ride and the handling quality of a vehicle better than a passive control, as verified in experimental results.

Suggest a format for intelligence control and structure of holographic data storage system

Data storage related with writing and retrieving requires high storage capacity, fast transfer rate and less access time. Today any data storage system cannot satisfy all of these conditions, however holographic data storage system (HDSS) can perform faster data transfer rate because it is a page oriented memory system using volume hologram in writing and retrieving data. System can be constructed without mechanically actuating part therefore fast data transfer rate and high storage capacity about 1Tb/cm3 can be realized. In this research, to reduce errors of binary data stored in HDSS, a new method for bit error reduction is suggested. Firstly, find fuzzy rule using test bed system for Element of Holographic Digital Data System and make fuzzy rule table using subtractive clustering algorithm and genetic algorithm and Reduce prior error element and recording digital data. Secondly, Reduce prior error element and recording digital data using the particle filter method. Finally, Recording ratio and reconstruction ratio show good performance and we suggest intelligence control method and filter method. Our format table include intelligence control algorithm and filter method.

A simple method to compute a periodic solution of the Poisson equation with no boundary conditions

We consider the poisson equation where the functions involved are periodic including the solution function. Let <TEX>$R=[0,1]{\times}[0,l]{\times}[0,1]$</TEX> be the region of interest and let <TEX>$\phi$</TEX>(x,y,z) be an arbitrary periodic function defined in the region R such that <TEX>$\phi$</TEX>(x,y,z) satisfies <TEX>$\phi$</TEX>(x+1, y, z)=<TEX>$\phi$</TEX>(x, y+1, z)=<TEX>$\phi$</TEX>(x, y, z+1)=<TEX>$\phi$</TEX>(x,y,z) for all x,y,z. We describe a very simple method for solving the equation <TEX>${\nabla}^2u(x, y, z)$</TEX> = <TEX>$\phi$</TEX>(x, y, z) based on the cubic spline interpolation of u(x, y, z); using the requirement that each interval [0,1] is a multiple of the period in the corresponding coordinates, the Laplacian operator applied to the cubic spline interpolation of u(x, y, z) can be replaced by a square matrix. The solution can then be computed simply by multiplying <TEX>$\phi$</TEX>(x, y, z) by the inverse of this matrix. A description on how the storage of nearly a Giga byte for <TEX>$20{\times}20{\times}20$</TEX> nodes, equivalent to a <TEX>$8000{\times}8000$</TEX> matrix is handled by using the fuzzy rule table method and a description on how the shape preserving property of the Laplacian operator will be affected by this approximation are included.

New modifying self-organizing fuzzy controller for robotic motion control

The precise motion control of robotic manipulators is important in improving productivity and quality. However, robotic manipulators are multivariable nonlinear dynamic systems. Designing a model-based controller for robotic system control is difficult because its mathematical model is hard to accurately establish. This study proposed a self-organizing fuzzy controller (SOFC) to control a robotic system and evaluate its control performance. The SOFC continually updates the learning strategy in the form of fuzzy rules during the control process. The learning rate and the weighting distribution value of the controller are hard to regulate, so its fuzzy control rules may be modified to such an extent that the system response generally causes oscillatory phenomena. Two fuzzy logic controllers were designed according to the system output error and the error change, and introduced to the SOFC to determine the appropriate parameters of the learning rate and the weighting distribution, in order to eliminate this oscillation. This new modifying self-organizing fuzzy controller (NMSOFC) can effectively improve the control performance of the system, reduce the time consumed to establish a suitable fuzzy rule table, and support practical and convenient fuzzy controller applications. To confirm the applicability of the proposed intelligent controllers, this work retrofitted an old robot for a control system to evaluate the feasibility of motion control. Experiment results indicate the NMSOFC has better control performance in reducing the tracking errors of the joint-space trajectories and the positions, and requires less computational time than does the traditional fuzzy controller.

SIGNIFICANT FUZZY RULES EXTRACTION BY AN SVD–QR-BASED APPROACH

ABSTRACT In this article, an SVD–QR-based approach is proposed to extract the important fuzzy rules from a rule base with several fuzzy rule tables to design an appropriate fuzzy system directly from some input-output data of the identified system. A fuzzy system with fuzzy rule tables is defined to approach the input-output pairs of an identified system. In the rule base of the defined fuzzy system, each fuzzy rule table corresponds to a partition of an input space. In order to extract the important fuzzy rules from the rule base of the defined fuzzy system, a firing strength matrix determined by the membership functions of the premise fuzzy sets is constructed. According to the firing strength matrix, the number of important fuzzy rules is determined by the Singular Value Decomposition SVD, and the important fuzzy rules are selected by the SVD–QR-based method. Consequently, a reconstructed fuzzy rule base composed of significant fuzzy rules is determined by the firing strength matrix. Furthermore, the recursive least-squares method is applied to determine the consequent part of the reconstructed fuzzy system according to the gathered input-output data so that a fine fuzzy system is determined by the proposed method. Finally, three nonlinear systems illustrate the efficiency of the proposed method.

Self-organizing fuzzy control of constant cutting force in turning

Constant force control is gradually becoming an important technique in the modern manufacturing process. Especially, constant cutting force control is a useful approach in increasing the metal removal rate and the tool life for turning systems. However, turning systems generally have nonlinear with uncertainty dynamic characteristics. Designing a model-based controller for constant cutting force control is difficult because an accurate mathematical model in the turning system is hard to establish. Hence, this study employed a model-free fuzzy controller to control the turning system in order to achieve constant cutting force control. Nevertheless, the design of the traditional fuzzy controller (TFC) presents difficulties in finding control rules and selecting an appropriate membership function. Moreover, the database and fuzzy rules of a TFC are fixed after the design step and then cannot appropriately regulate ones real time according to the system output response and the desired control performance. To solve the above problem, this work develops a self-organizing fuzzy controller (SOFC) for constant cutting force control to evaluate control performance of the turning system. The SOFC continually updates the learning strategy in the form of fuzzy rules, during the turning process. The fuzzy rule table of this SOFC can be begun with zero initial fuzzy rules which not only overcome the difficulty in the TFC design, but also establish a suitable fuzzy rules table, and support practically convenient fuzzy controller applications in turning systems control. To confirm the applicability of the proposed intelligent controllers, this work retrofitted an old lathe for a turning system to evaluate the feasibility of constant cutting force control. The SOFC has a better control performance in constant cutting force control than does the TFC, as verified in experimental results.

Self-organizing fuzzy control of active suspension systems

A self-organizing fuzzy controller (SOFC) is proposed to control an active suspension system and evaluate its control performance. In complicated nonlinear system control, the SOFC continually updates the learning strategy in the form of fuzzy rules during the control process. The learning rate and the weighting distribution value of the controller are hard to regulate, so its fuzzy control rules may be excessively modified such that the system response generally causes an oscillatory phenomenon. Two fuzzy-logic controllers were designed according to the system output error and the error change, and introduced to the SOFC to determine the appropriate parameters of the learning rate and the weighting distribution, to eliminate this oscillation. This new modifying self-organizing fuzzy-control approach can effectively improve the control performance of the system, reduce the time consumed to establish a suitable fuzzy rule table, and support practically convenient fuzzy-controller applications in an active suspension control system, as verified experimentally.