THE INTERNET OF TRAFFIC LIGHTS (IOTL). AN EVALUATION OF SELF-ADJUSTING FUZZY LOGIC HYBRID VEHICULAR TRAFFIC CONTROL BY RNN IN COLONIAL CITIES

Vehicular traveling is increasing throughout the world, particularly in large urban areas. By the increasing use of automobiles in cities, traffic congestion occurs. Thus, there is a requirement for optimizing traffic control methods for better accommodating the increasing demand. Therefore, the transportation system will continue to grow, and intelligent traffic controls have to be employed to face the road traffic congestion’s problems. Fuzzy controllers have been widely used in many consumer products and industrial applications successfully over the past two decades. For traffic control, however, fuzzy controllers have not been widely applied. This research presents an application of fuzzy logic for multi-agent based autonomous traffic lights control system using wireless sensors to overcome problems like congestion, accidents, speed, and traffic irregularity. The real time parameters such as traffic density and queue length are obtained by using image-processing techniques. Thus, On and Off timings for the green, red and or amber lights are adjusted to the actual road conditions. Fuzzy logic has been widely used to develop a traffic signal controller because it allows qualitative modeling of complex systems. This paper describes a fuzzy logic signal controller for a four–way intersection suitable for mixed traffic, including a high proportion of motorcycles. The proposed agent-based approach can provide a preferred solution by minimizing the vehicles’ waiting time especially the emergency vehicles using fuzzy logic control under the situations that normally occur during emergencies. The effectiveness of this approach is tested by taking two traffic junctions. Keyword: Traffic lights control system, application of fuzzy logic, autonomous systems, congestion control

Vehicular travel is increasing throughout the world, particularly in large urban areas. With the increasing use of automobiles in cities traffic congestion occurred. So as the transportation system will continue to grow, intelligent traffic controls have to be employed to face road traffic congestion problems. Fuzzy controllers have been widely used in many consumer products and industrial applications with success over the past two decades. For traffic control, however, fuzzy controllers have not been widely applied. This paper proposes a fuzzy traffic lights controller to be used at a complex traffic junction. The real time parameters such as traffic density and queue length are obtained by image processing techniques. So the on and off timings for the green, red and orange lights are adjusted as per the actual road conditions. Fuzzy logic has been widely used to develop a traffic signal controller because it allows qualitative modeling of complex systems. This paper describes a fuzzy logic signal controller for a four--way intersection suitable for mixed traffic, including a high proportion of motorcycles. This paper discusses the traffic control strategy, which dictates the design criteria for the fuzzy logic controller. The components of fuzzy logic controller-the fuzzifier, the fuzzy rule base formulated by human experts, the fuzzy inference engine and the defuzzifier.

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.

After the development of fuzzy logic, an important application of it was developed in control systems and it is known as fuzzy PID controllers. They represent interest in order to be applied in practical applications instead of the linear PID controllers, in the feedback control of a variety of processes, due to their advantages imposed by the non-linear behavior. The design of fuzzy PID controllers remains a challenging area that requires approaches in solving non-linear tuning problems while capturing the effects of noise and process variations. In the literature there are many papers treating this domain, some of them being presented as references in this chapter. Fuzzy PID controllers may be used as controllers instead of linear PID controller in all classical or modern control system applications. They are converting the error between the measured or controlled variable and the reference variable, into a command, which is applied to the actuator of a process. In practical design it is important to have information about their equivalent input-output transfer characteristics. The main purpose of research is to develop control systems for all kind of processes with a higher efficiency of the energy conversion and better values of the control quality criteria. What has been accomplished by other researchers is reviewed in some of these references, related to the chapter theme, making a short review of the related work form the last years and other papers. The applications suddenly met in practice of fuzzy logic, as PID fuzzy controllers, are resulted after the introduction of a fuzzy block into the structure of a linear PID controller (Buhler, 1994, Jantzen, 2007). A related tuning method is presented in (Buhler, 1994). That method makes the equivalence between the fuzzy PID controller and a linear control structure with state feedback. Relations for equivalence are derived. In the paper (Moon, 1995) the author proves that a fuzzy logic controller may be designed to have an identical output to a given PI controller. Also, the reciprocal case is proven that a PI controller may be obtained with identical output to a given fuzzy logic controller with specified fuzzy logic operations. A methodology for analytical and optimal design of fuzzy PID controllers based on evaluation approach is given in (Bao-Gang et all, 1999, 2001). The book (Jantzen, 2007) and other papers of the same author present a theory of fuzzy control, in which the fuzzy PID controllers are analyzed. Tuning fuzzy PID controller is starting from a tuned linear PID controller, replacing it with a linear fuzzy controller, making the fuzzy controller nonlinear and then, in the end, making a fine tuning. In the papers (Mohan & Sinha, 2006, 2008), there are presented some mathematical models for the simplest fuzzy PID controllers and an approach to design

This paper presents an analysis of multi stage fuzzy logic control application for load frequency control of isolated wind-diesel hybrid power system. Due to the sudden load changes and intermittent wind power, large frequency fluctuation problem can occur. An effective controller for stabilizing frequency oscillations and maintaining the system frequency within acceptable range is significantly required. The load frequency control (LFC) deviates the frequency deviation and maintains dynamic performance of the system. As fuzzy logic control approach can be easily implemented in practical systems, the fuzzy logic control has been applied to design LFC system. In this paper, multi stage Fuzzy logic PID controller is proposed for Load Frequency Control (LFC) of an isolated wind-diesel hybrid power system. Simulations are performed for this hybrid system with the proposed multi stage Fuzzy Logic PID controller, conventional PI controller and Fuzzy logic controller with different load disturbances and wind input disturbances. The performance of the proposed approach is verified from simulations and comparisons. Simulation results explicitly show that the performance of the proposed multi stage Fuzzy Logic PID Controller is superior to the conventional PI controller and Fuzzy logic controller in terms of overshoot, settling time and steady state error against various load changes and variations of wind inputs.

This paper presents a new approach of maximum power point tracking (MPPT) for partial shaded total cross tied (TCT) photovoltaic array based on fuzzy logic controller. The proposed method employs MPPT based on fuzzy logic controller comprising two inputs coming from voltage and current sensor. In addition, the photovoltaic array uses total cross tied configuration (TCT) in which it is superior to other configuration such as serial (S), parallel (P), and serial-parallel (SP) configuration. Here, TCT configuration consists of 10 photovoltaic modules by using 5×2 arrangements. Meanwhile, the fuzzy logic controller itself is used to drive boost dc-dc converter through pulse width modulation (PWM). The comparative study of two topologies, TCT (without fuzzy control) and proposed TCT (using fuzzy logic control) is carried out in MATLAB using SIMULINK , Fuzzy Logic Toolbox, and Power System Toolbox. The simulation result shows that TCT photovoltaic array using fuzzy logic controller (FLC) provides both higher power compared with TCT photovoltaic array without fuzzy logic controller.

In this paper, a new type fuzzy logic control for EDM servo system is presented. The effects of quantitative factors and proportionality factors on the performance of a system with fuzzy controller are discussed. A three-dimensional fuzzy controller based on on-line adjustment of the factors to cope with the variation of the controlled system is developed. Computer simulation shows that the controller may improve the controlling quality in the non-linear and time-varying control system. The performance is superior to conventional fuzzy control.

Design of a Fuzzy Logic Controller for Regulating the Temperature in Industrial Polyethylene Fluidized Bed Reactor

Attempts are being made to enhance the drive performance by intelligent control using fuzzy logic (FL) and neural network techniques. One of the frequently discussed applications of artificial intelligence in control is the replacement of a standard proportional plus integral (PI) speed controller with an FL or artificial neural network (ANN) speed controller. Regardless of all the work, it appears that a thorough comparison of the drive behavior under PI, FL, and ANN speed control is necessary. This article attempts to compare PI, fuzzy, and ANN controllers that are implemented in an embedded system for closed-loop speed control of DC drive fed by a buck-type DC–DC power converter. The PI controller is designed based on the small signal modeling of the system. The PI-like fuzzy controller structure is considered for comparison. Two ANN controllers are designed. One controller uses training data obtained from the simulation of a fuzzy controller and the other uses training data from the simulation of a PI controller. The performance of the controllers is studied for a variety of operating conditions, such as step change in speed command and step change in load torque. The parameters selected for the comparison are the steady-state error and the rise time of the response. It is shown that ANN speed controllers provide a superior speed response in terms of rise time and the steady-state error compared to PI and FL controllers. This advantage arises from the fact that the neural network has the property of generalization and the control surface of the neural controller is smooth. The designed neural network controller is simple, with three neurons only, and so it is best suited for embedded system implementation. It is also found that the ANN controller trained with the training data from a PI controller has a better response compared to the ANN controller trained with data from a fuzzy controller.

Intelligent traffic signal control (TSC) system is important for the alleviation of traffic congestion. Usually, most of the researches about TSC focused on single intersection based on type-1 fuzzy set. Compared with type-1 fuzzy logic controller (FLC), type-2 FLC can deal with more uncertainties in the road traffic control system. Therefore, a type-2 FLC optimized by NSGAII (T2-NSGAII) is designed for TSC in a complex road network, in which the intersection’s traffic signal time is dynamically adjusted by its own and adjacent intersections’ traffic volumes to reduce global delay time and traffic congestion. In T2-NSGAII, the expert rule set and the parameters of the fuzzy membership functions are simultaneously optimized by NSGAII to achieve less time delay and traffic congestion. In the simulations of a six-intersection traffic network with different vehicular arrival rates, it is demonstrated that T2-NSGAII has better performance compared with vehicle actuated controller based on fixed-time control (FTC), type-1 FLC, type-2 FLC and isolatedly optimized Type-2 FLC and the total delay time could be reduced by 76.3% , 65.1% , 58.3% and 35.4% respectively.

The fuzzy systems and control are regarded as the most widely used application of fuzzy logic systems in recent years (Jang, 1993; John & Coupland, 2007; Lin & Lee, 1006; Mendel, 2001; Wang, 1994). The structure of traditional fuzzy system models that is characterized by using type 1 fuzzy sets, which are defined on a universe of discourse, map an element of the universe of discourse onto a precise number in the unit interval [0, 1]. The concept of type-2 fuzzy sets was initially proposed by Zadeh as an extension of typical fuzzy sets (called type1) (Zadeh, 1975). Mendel and Karnik developed a complete theory of interval type-2 fuzzy logic systems (iT2FLSs) (Karnik et al, 1999; Liang & Mendel, 2000; Mendel, 2001). Recently, T2FLSs have attracted more attention in many literatures and special issue of IEEE Transactions on Fuzzy systems (Baldwin & Karake, 2003; John & Coupland, 2007; Lee & Lin, 2005; Liang & Mendel, 2000; Mendel, 2001, Hagras, 2007; Ozen & Garibaldi, 2004; Pan et al, 2007; Wang et al, 2004). T2FLSs are more complex than type-1 ones, the major difference being the present of typeis their antecedent and consequent sets. T2FLSs result better performance than type-1 Fuzzy Logic Systems (T1FLSs) on the applications of function approximation, modeling, and control. In addition, neural networks have found numerous practical applications, especially in the areas of prediction, classification, and control (Lee & Teng, 2000; Lin & Lee, 1996; Narendra & Parthasarathy, 1990). The main aspect of neural networks lies in the connection weights which are obtained by training process. Based on the advantages of T2FLSs and neural networks, the type-2 neural fuzzy systems are presented to handle the system uncertainty and reduce the rule number and computation (Castillo & Melin, 2004; Lee & Lin, 2005; Mendel, 2001; Pan et al, 2007; Wang et al, 2004). Besides, recurrent neural network has the advantages of store past information and speed up convergence (Lee & Teng, 2000). The design of a fuzzy partition and rules engine normally affects system performance. To simplify the design procedure, we usually use the symmetric and fixed membership functions (MFs), such as Gaussian, triangular. However, a large rule number should be used to achieve the specified approximation accuracy (or result larger approximated error) (Lee & Teng, 2001; Lotfi & Tsoi, 1996). Several approaches have been introduced to optimize fuzzy MFs and choose an efficient scheme for structure and parameter learning. Nevertheless, asymmetric fuzzy MFs (AFMFs) has been discussed and analyzed for this problem (Baldwin O pe n A cc es s D at ab as e w w w .ite ch on lin e. co m

In order to improve efficiency of control strategy about traffic signal timing in single intersection, in this paper, a reckoning model about traffic signal control strategy is proposed. This model is built according to the number of delayed vehicles in one unit time, and an improved FLC (fuzzy logic controller) is designed, this controller's values of initial parameter are established according to historical traffic flows, who can adjusts the parameters of standardization according to the reckoning model, and produces an optimal control strategy. Experimental results demonstrate this method is effective.

<div class="htmlview paragraph">Since the nonlinearity and uncertainties which inherently exist in vehicle system need to be considered in active suspension control law design, a new control strategy is proposed for active vehicle suspension systems by using a combined control scheme, i.e., respectively using a genetic algorithm (GA) based self-tuning PID controller and a fuzzy logic controller in two loops. The PID controller is used to minimize vehicle body vertical acceleration and the fuzzy logic controller is to minimize pitch acceleration and meanwhile to attenuate vehicle body vertical acceleration further by tuning weighting factors. In order to achieve optimal vehicle performances and adaptability to the changes of plant parameters, based on the defined objectives, a genetic algorithm is introduced to tune the parameters of PID controller, the scaling factors, gain values and the membership function of fuzzy logic controller on-line. By a four degree-of-freedom nonlinear vehicle model, the proposed control scheme is implemented and simulations are carried out in different road disturbance input conditions. Simulation results show that the present control scheme is very effective in reducing peak values of vehicle body accelerations, especially within the most sensitive frequency range of human response, and attenuating the excessive tire deflection to enhance road holding performance. It also shows good stability and adaptability even if the system is subject to adverse road conditions, such as a pothole, an obstacle or a step input. Compared with conventional passive suspensions and active vehicle suspension systems by using different control schemes, i.e., a linear and fuzzy logic control, the combined PID and fuzzy control without parameters self-tuning, the new proposed control system with GA-based self-learning ability in this paper can improve vehicle ride comfort performance significantly and offer better system robustness.</div>

This paper studies traffic signal control method based on intelligent techniques such as agent, fuzzy logic system (FLS), neural network-fuzzy (NNF) and multi-objective genetic algorithms (MOGA) for intersection. The traffic signal control system of intersections in local area can be built up by using the term of agent, and it comprises four levels: centre command layer, local area coordination layer, isolated intersection control layer, and optimizing layer. This paper focus on discussing isolated intersection control layer and optimizing layer. In an isolated intersection layer, fuzzy logic system is used to control traffic signal, and input parameters of fuzzy system can be forecasted or calculated by neural network-fuzzy. In optimizing layer, parameters in fuzzy system can be optimized by MOGA. The proposed method has the adaptive signal timing ability, and can make adjustments to signal timing in response to observed changes for intersections in local area. Our proposed has the ability to adjust its signal timing in response to changing traffic conditions on a real-time basis.

Different Fuzzy Logic Control Strategies for Traffic Signal Timing Control with State Inputs