Fuzzy Control Model Research Articles

Study on Soft Sensor Modeling and Optimization Control of Biological Fluidized Bed Swage Disposal

To solve the problem that outlet water index Biochemistry Oxygen Demand (BOD) of the Biological Fluidized Bed (BFB) sewage treatment process is difficult to online measure and optimization control of sewage treatment process, Water quality index neural network soft measurement method and the fuzzy control strategy was put forward in this paper. Considering the sewage treatment process exists nonlinear and time-varying characteristic, the effluent water BOD soft sensor model was established employing the process neural network. On the base of this, the optimization control was realized adopting Dissolved Oxygen (DO) for fuzzy control variable Through the soft measurement and fuzzy control model’s simulation training, Shows that the effectiveness and feasibility of these models, an effective soft measurement and optimization control way for BFB sewage treatment have been provided.

On the Basis of Fuzzy and PID Double Control of Honing Machine Reciprocating Motion

As the improvement of honing velocity, acceleration and accuracy, corresponding requirements about control technology are put forward to meet the improvement of high machining efficiency. While the honing machine spindle reciprocating reversing control is the key control link, directly affects the ability of the machine abilities and quality. Aim at the traditional honing machine reciprocating motion cannot satisfy the requirement of high speed, high acceleration and high reversing accuracy. According to the characteristics of honing machine mechanism of reciprocating movement, establish its mathematic model and the system of fuzzy and PID double control model, and then the control system was conducted by using the tool of Simulink. The simulation result showed that using fuzzy and PID double control can make overshoots reduced compared with conventional single fuzzy control and PID control, and can well satisfied the system performance index.

An integrated simulation-based fuzzy logic model for real-time traffic signal control

Fuzzy logic has been recognised in the literature as an effective methodology for real-time signal control. The majority of the fuzzy controllers in the literature depend on simple logic which in turn depends on the raw data of a single detector. Their input variables are usually simple estimates of traffic measures such as flow, speed or occupancy, estimated from such single-detector readings. A room for improvement is sought in this article by developing a fuzzy logic model (FLM) that could be integrated with smarter ‘processing’ tools to estimate several traffic measures from multiple detectors on each approach. The estimates obtained from this processing tool are integrated as input knowledge into the FLM. The mathematical formulation of these traffic measures is presented. The fuzzy logic structure is addressed in detail. A simulation model is devised to test the effectiveness of the FLM. The results are presented and discussed in depth.

Safety Following Space Modeling and Speed Control on Highway

Due to the rapid development of economics and society, China has witnessed increasing highway accidents, especially rear end collisions that mainly caused by over-speeding or too close following distance. Based on the vehicle dynamic analysis, therefore, a 3 step-based safety following space identification model according to Mazda vehicle is proposed to describe the brake performance and three driving states are taken into considering, including total following state, part following state, and three-kind extreme states. Then, a self-adaptive fuzzy reasoning model is used to control the traveling speed so as to adjust the traffic flow as smoothly as possible. The Matlab simulation results shows this proposed fuzzy control model helps minimize the occurrence probability of rear-end collision on highway and improve the overall traffic safety performance and management level.

Fuzzy Logic Control of an Induction Motor Compared with PI Contro

In this work, a model of fuzzy logic controller (FLC) was designed to regulate the speed of a 3-phase squirrel cage induction motor. The model was created in Simulink and simulated in MATLAB environment. The motor was tested at speeds of 120 rad/sec and 140 rad/sec under no-load condition and simulation results were obtained. The speed of the motor was tested under the same condition but using Proportional + Integral (PI) controller. The results show that the PI controller could be replaced with the fuzzy logic controller due to the advantages of relatively short settling time and small overshoot. The settling time for the speed and torque for the FLC were 0.7s and 1.0s, while that of the PI controller were 1.0s and 2.1s respectively. Similarly, the overshoot was higher in the simulation results obtained for the torque and speed curves when the motor was tested with the PI controller than with the FLC. Finally, for the motor considered, it was found that the PI controller introduces nonminimum phase behavior in the system while the FLC does not. Keywords: Induction motor, Simulink model, fuzzy logic control, proportional + integral control, nonminimum phase.

하이브리드 자기베어링 시스템의 강인 안정도 해석

This paper propose the robust stability algorithm for controlling a hybrid magnetic bearing system. The control object in the magnetic bearing system enables the rotor to rotate without any physical contact by using magnetic force. Generally, the system dynamics of the magnetic bearing system has severe nonlinearity and uncertainty so that it is not easy to obtain the control objective. For solving these problems, we propose the fuzzy modelling and robust control algorithm for hybrind magnetic bearing system. The sufficient conditions for robust controller are obtained in terms of solutions to linear matrix inequalities (LMIs). Simulation results for HMB are demonstrated to visualize the feasibility of the proposed method.

State Estimate-based Friction Fuzzy Modeling and Robust Adaptive Control

This paper develops a fuzzy state estimate-based robust adaptive control scheme for a class of mechanical systems with multiple degrees of freedom. The unmeasurable state variables used in both modeling friction force and adaptive controller design are estimated by a fuzzy estimator. By strictly positive real lemma and Lyapunov stability theory, the ultimately uniform boundedness of the state estimation errors is ensured. This paper also proposes a multivariable robust control algorithm using the estimated states and adaptive fuzzy friction mode, where the updating rule for parameter adjustment in the fuzzy friction model is derived by Lyapunov theory. It is shown that the tracking error of the closed-loop system is ultimately uniformly bounded (UUB). A mass-spring-damping system with multiple degrees of freedom is employed in the simulation studies. Simulation results demonstrate the effectiveness of our proposed state-estimate based fuzzy friction modeling and adaptive control strategy.

Polynomial Fuzzy-Model-Based Control Systems: Stability Analysis Via Piecewise-Linear Membership Functions

This paper presents the stability analysis of polynomial fuzzy-model-based (PFMB) control systems using the sum-of-squares (SOS) approach. The PFMB control system under consideration requires that the polynomial fuzzy model and polynomial fuzzy controller share neither the same premise membership functions nor the same number of fuzzy rules. This class of PFMB control systems offers a greater design flexibility to the polynomial fuzzy controller. However, due to the imperfectly matched membership functions, it usually produces more conservative stability conditions by following the traditional stability-analysis approach for the FMB control systems. To facilitate the stability analysis, piecewise-linear membership functions (PLMFs) are proposed, which offer a nice property that the grades of membership are governed by a finite number of sample points. Thus, it allows the PLMFs to be brought to the SOS-based stability conditions, which are applied to the PFMB control systems with the specified PLMFs rather than any shapes. The system stability can be examined by checking only the PFMB control system at the sample points. It is worth mentioning that the PLMFs, which are not necessarily implemented physically, are a mathematical tool to carry out the stability analysis. To verify the stability-analysis result, a simulation example is given to demonstrate the effectiveness of the proposed approach.

LMI-Based Stability Analysis for Fuzzy-Model-Based Control Systems Using Artificial T–S Fuzzy Model

This paper investigates the stability of fuzzy-model-based (FMB) control systems. An alternative stability-analysis approach using an artificial fuzzy system based on the Lyapunov stability theory is proposed. To facilitate the stability analysis, the continuous membership functions of the Takagi-Sugeno (T-S) fuzzy model are represented by the staircase ones. With the nice property of the staircase membership functions, it turns the set of infinite number of linear-matrix-inequality (LMI) based stability conditions into a finite one. Furthermore, the staircase membership functions carrying system information can be brought to the stability conditions to relax the stability conditions. The stability of the original FMB control systems is guaranteed by the satisfaction of the LMI-based stability conditions. The proposed stability analysis is applied to the FMB control systems of which the T-S fuzzy model and fuzzy controller do not share the same premise membership functions and, thus, is able to enhance the design flexibility of the fuzzy controller. A simulation example is given to illustrate the merits of the proposed approach.

Fuzzy Control Model Study on Precision Irrigation System for Water Stress in Crops

Precision irrigation in fuzzy control system based on crop water stress acoustic monitoring was designed in the paper. Moreover, conventional double fuzzy control model was found to carry out two work mode, normal irrigation and precision irrigation, which aims to both ensure normal growth of crops and achieve effective water-saving. Since five input signals affect output in different degree, three layer BP neural network was employed to compute weight. Based on conventional fuzzy model, self learning fuzzy model for crop growth was built to tackle with normal irrigation and precision irrigation. In order to testify the control strategy, an experiment platform including virtual instruments was founded. It shows that the system can effectively adjust to water and control valve speed under signals in AE sensor and environmental information for the corp growth to some extent, and not only save water in the normal irrigation, but also realize safety and efficiency in precision irrigation.

Fuzzy dynamic characteristic modeling and adaptive control of nonlinear systems and its application to hypersonic vehicles

In this paper, a fuzzy dynamic characteristic modeling and adaptive control method is proposed for a class of nonlinear systems. By employing fuzzy dynamic characteristic model, the controlled plant is described as a slowly time-varying fuzzy system, wherein the parameters are estimated online by using recursive Least-Squares algorithm. Under this framework, a fuzzy adaptive controller is constructed, and the stability condition of the closed-loop system is also derived. The main advantage of the proposed method lies in no requirement for the prior knowledge of system model and less parameters to tune, which allows engineers to operate it in a simple, straightforward manner. The proposed method is applied to the control of hypersonic vehicle, and simulation results are given to demonstrate the effectiveness of the obtained results.

Fuzzy logic controller to improve parameters affecting gas turbines power generation

In order to improve the performance of the gas turbine power plant and to generate electricity at the best cost, a fuzzy logic controller model was used to show the effect of different parameters on the power generation output of gas turbines. The proposed methodology was applied to certain parameter values collected from Rehab power station in Jordan—as a case study—for validation purposes. Relative weights were used, i.e., very very low power generation “extremely low power generation,” very low power generation, low power generation, normal power generation, high power generation, and very high power generation. The study reveals that the major factors that affect yield are ambient temperature (T1), compressor’s exit temperature (T2), turbine’s inlet temperature (T3), turbine exit temperature (T4), pressure ratio (Rp), mass of fuel (Mf), relative humidity (H), turbine efficiency (ηt), and compressor efficiency (ηc). Based on the increase of productivity, the results show that different factors are found to affect the yield of a power generator. Therefore, using fuzzy logic controller model has lead the researchers to focus on the highest priority parameters that should be enhanced and developed to increase the power output productivity.

Speed Control of Fluid Coupling Based on Fuzzy Control Theory

Based on fuzzy control theory and algorithms, the speed control of fluid coupling was presented. According to variable speed principles and methods, the system of fuzzy variable speed controller was introduced; fuzzy control models were established for fluid coupling. Variable speed process was controlled intelligently by computer. Parameter adjustment was fixed, deviation value was determined and input and output scale factors were modified based on service conditions. The target was approximated by successively reducing the control range.

Development of Precision Profile Control System with Fuzzy Model and Correction Function for Tube Dieless Drawing

Dieless drawing offers several advantages not only lack of the expensive dies and lubricant but also the ability to draw difficult material since the process is carried out at elevated temperature. However dimensional stability and accuracy is still unsolved issue. A finite element method (FEM) is utilized to examine a valid deformation behavior. As the result, a correction function is formulated from deformation sequence of dieless drawing to predict elongation of desired profile. A new approach to control desired profile of dieless drawing parts using a fuzzy model and correction function is presented in this paper. The result shows that by applying fuzzy control and correction function model, dimensional accuracy of drawn tube profile increases significantly.

Two-Stage Fuzzy Logic Controller for Signalized Intersection

Traffic efficiency is commonly regarded as the most important target for the control of signalized intersections. However, from the fairness point of view, it can be argued that all vehicles at a signalized intersection should have equal passing opportunities. In this correspondence paper, a two-stage fuzzy logic control model for an isolated signalized intersection has been proposed, where both traffic efficiency and fairness have been considered simultaneously. At the first stage, a green-phase selector has been developed to select the subsequent green phase. At the second stage, a green-time adjustor has been proposed to determine the green time for the selected phase. An offline genetic algorithm (GA) has been developed to optimize the fuzzy rules and membership functions of the two controllers. The simulation results demonstrate that the proposed model outperforms the vehicle-actuated control model and the model proposed by Pappis and Mamdani in 1977 in terms of both traffic efficiency and fairness. The performance of the proposed model can be further improved after its rules and membership functions are optimized by using GA.

Learnable Data-Oriented Controller for ABS in Brake By Wire Vehicles

A fuzzy controller is suited to control Antilock Brake System (ABS) however time complexity of fuzzy controller is high order. Problem Solution Data Structure (PSDS) has been introduced as a data-oriented model of fuzzy controller to reduce the response time. Locally learning of PSDS controller has been left as an open issue. Fuzzy Learning Mechanism (FLM) has been introduced by using fuzzy inference as learning function, but it needs considerable long process time and steady state errors. In this paper an Adaptive Neuro Fuzzy Inference System (ANFIS) Learning Mechanism (ALM) to learn PSDS is presented. The simulation results show better performance of ALM PSDS comparing to FLM PSDS controller in terms of slip ratio control, stop distance, process time and reduction in steady state errors.

Design of stable fuzzy controller for non-linear systems subject to imperfect premise matching based on grid-point approach

This study investigates the systems stability of fuzzy-model-based (FMB) control systems. Based on the T-S fuzzy model representing the non-linear system, a fuzzy controller using grid-point (GP) technique is proposed to close the feedback loop. A GP is defined as the sub-operating domain of the non-linear system. For each GP, a corresponding GP fuzzy controller is employed to control the system. As the non-linearity in each GP is lower compared to that of the full operating domain, it is in favour of yielding relaxed stability analysis result using the GP control technique of which the nature of the membership functions and operating domain are taken into account. Furthermore, unlike most of the fuzzy control approaches, the proposed one can be applied to FMB control systems subject to imperfect premise matching that the fuzzy model and fuzzy controller do not share the same premise membership functions. As a result, some simple membership functions can be employed for the fuzzy controllers to lower the implementation cost. Based on the Lyapunov stability theory, stability conditions in terms of linear matrix inequalities are derived to guarantee the system stability and facilitate the controller synthesis. Simulation examples are given to demonstrate the merits of the proposed FMB control scheme using the proposed GP technique.

Application of fuzzy control method in a tunnel lighting system

In order to meet the requirements of driving safety and energy-saving of lighting in tunnel, a fuzzy control method is adopted to design the tunnel lighting control system. A fuzzy control model for the tunnel lighting control system is established with tunnel exterior environment luminance, traffic volume and vehicle speed information as inputs and tunnel interior light luminance as output. Membership functions of environmental parameters and fuzzy control rules are designed based on “Specifications for the Design of Ventilation and Lighting of Highway Tunnels (China)” and experts’ relevant experiences. The tunnel exterior environment luminance fit-curve and the tunnel interior luminance fit-curve are constructed by experimental data with the fuzzy control model on sunny days. Errors between theoretical luminance data and simulation luminance data with the fuzzy control model are less than 5 %. Comparison between luminance data from fit-curves and luminance data from original curves (no light-tuning or class light-tuning) shows that the fuzzy control system has a notable energy-saving effect (saving more than 50 % energy to no light-tuning and more than 20 % to four-steps light-tuning), and nice adaptability.

LMI-Based Stability Analysis of Fuzzy-Model-Based Control Systems Using Approximated Polynomial Membership Functions

Relaxed linear-matrix-inequality-based stability conditions for fuzzy-model-based control systems with imperfect premise matching are proposed. First, the derivative of the Lyapunov function, containing the product terms of the fuzzy model and fuzzy controller membership functions, is derived. Then, in the partitioned operating domain of the membership functions, the relations between the state variables and the mentioned product terms are represented by approximated polynomials in each subregion. Next, the stability conditions containing the information of all subsystems and the approximated polynomials are derived. In addition, the concept of the S-procedure is utilized to release the conservativeness caused by considering the whole operating region for approximated polynomials. It is shown that the well-known stability conditions can be special cases of the proposed stability conditions. Simulation examples are given to illustrate the validity of the proposed approach.

SOS-Based Stability Analysis of Polynomial Fuzzy-Model-Based Control Systems Via Polynomial Membership Functions

This paper presents stability analysis of polynomial fuzzy-model-based (FMB) control systems using the sum-of-squares (SOS) approach. Recently, stability analysis of the polynomial fuzzy-control systems, which is a generalized form of the well-known Takagi-Sugeno (T-S) FMB control systems, has been reported in the form of SOS-based stability conditions. Lack of information on the relations between membership functions and premise variables, in the existing stability analysis approaches, causes conservatism of their results. In this paper, to derive relaxed stability conditions for polynomial FMB control systems, membership functions which are approximated with polynomials and carrying relations between membership functions and premise variables are brought into the stability analysis. Considering a polynomial FMB control system and based on the Lyapunov stability theory, stability conditions in the form of fuzzy summations are derived, where each term contains product of membership functions of the polynomial fuzzy model and polynomial fuzzy controller. Each product term is approximated by a polynomial. In order to obtain better approximation, the operating domain of membership functions is partitioned to subregions. Then, SOS-based stability conditions for all subregions are derived. Unlike some published stability-analysis approaches, the proposed one can be employed for stability analysis of polynomial fuzzy-control systems under imperfect premise matching of which the fuzzy model and fuzzy controller do not share the same membership functions. The solution of the SOS-based stability conditions can be found numerically using SOSTOOLS, which is a free third-party MATLAB Toolbox. Numerical examples are given to illustrate the effectiveness of the proposed stability conditions.