A QoS-provisioning neural fuzzy connection admission controller formultimedia high-speed networks

This paper proposes a neural fuzzy approach for connection admission control (CAC) with QoS guarantee in multimedia high-speed networks. Fuzzy logic systems have been successfully applied to deal with traffic-control-related problems and have provided a robust mathematical framework for dealing with real-world imprecision. However, there is no clear and general technique to map domain knowledge on traffic control onto the parameters of a fuzzy logic system. Neural networks have learning and adaptive capabilities that can be used to construct intelligent computational algorithms for traffic control. However, the knowledge embodied in conventional methods is difficult to incorporate into the design of neural networks. The proposed neural fuzzy connection admission control (NFCAC) scheme is an integrated method that combines the linguistic control capabilities of a fuzzy logic controller and the learning abilities of a neural network. It is an intelligent implementation so that it can provide a robust framework to mimic experts' knowledge embodied in existing traffic control techniques and can construct efficient computational algorithms for traffic control. We properly choose input variables and design the rule structure for the NFCAC controller so that it can have robust operation even under dynamic environments. Simulation results show that compared with a conventional effective-bandwidth-based CAC, a fuzzy-logic-based CAC, and a neural-net-based CAC, the proposed NFCAC can achieve superior system utilization, high learning speed, and simple design procedure, while keeping the QoS contract     

Fuzzy/neural congestion control for integrated voice and dataDS-CDMA/FRMA cellular networks

The paper proposes congestion control using fuzzy/neural techniques for integrated voice and data direct-sequence code division multiple access/frame reservation multiple access (DS-CDMA/FRMA) cellular networks. The fuzzy/neural congestion controller is constituted by a pipeline recurrent neural network (PRNN) interference predictor, a fuzzy performance indicator, and a fuzzy/neural access probability controller. It regulates the traffic input to the integrated voice and data DS-CDMA/FRMA cellular system by determining proper access probabilities for users so that congestion can be avoided and the throughput can be maximized. Simulation results show that the DS-CDMA/FRMA fuzzy/neural congestion controllers perform better than conventional DS-CDMA/PRMA with channel access function in terms of voice packet dropping ratio, corruption ratio, and utilization. In addition, the neural congestion controller outperforms the fuzzy congestion controller     

A real-time dynamic connection admission controller based ontraffic modeling, measurement, and fuzzy logic control

Broadband networks based on asynchronous transfer mode (ATM) have to support traffic with widely different traffic characteristics and quality of service requirement. In this paper, we extend our earlier work in and develop a dynamic connection admission controller (CAC) that supports cell loss requirements. The CAC algorithm explicitly computes the equivalent bandwidth required to support each class of connections based on on-line observations of aggregate traffic statistics as well as the declared parameters. We use Gaussian and diffusion approximations to characterize the aggregate traffic stream, and use fuzzy control strategy to combine model and measurement results to derive simple closed-form formulas to estimate the equivalent bandwidth in real time. We validate the proposed algorithms for various variable bit-rate traffic profiles and show that the system utilization can be substantially improved by appropriately tuning the fuzzy logic controller to combine traffic characteristics deduced from the declared parameters and traffic measurements     

On the Equivalence of a Table Lookup (TL) Technique and Fuzzy Neural Network (FNN) With Block Pulse Membership Functions (BPMFs) and Its Application to Water Injection Control of an Automobile

This paper presents an alternative method to design a fuzzy neural network (FNN) using a set of nonoverlapped block pulse membership functions (BMPFs), and this FNN with nonoverlapped BPMFs will be shown to be equivalent to the conventional table lookup (TL) technique. Therefore, the hidden links between TL and FNN techniques are revealed in this paper that provides a methodology to design a TL controller based on the FNN design concept. In order to do so, a new direct formula is first developed to generate the fuzzy rules from the premise part in FNN. This direct formula not only guarantees a one-to-one mapping that maps the fuzzy membership functions onto the fuzzy rules, but also alleviates the coding effort during hardware implementation. It is further elaborated that the FNN with nonoverlapped BPMFs has the advantage of faster online training that requires less computation time, but at the cost of more memory requirement to store the fuzzy rules. The application of this new approach has been applied successfully in the water injection control of a turbo-charged automobile with excellent results.     

Fuzzy Logic Ticket Rate Predictor for Congestion Control in Vehicular Networks

Cooperative vehicular systems are currently being investigated to design innovative intelligent transportation systems (ITS) solutions for road traffic management and safety. This paper proposes a preventive congestion control mechanism applied at highway entrances and devised for ITS systems. Our mechanism integrates different types of vehicles and copes with vehicular traffic fluctuations due to an innovative fuzzy logic ticket rate predictor. The proposed mechanism efficiently detects road traffic congestion and provides valuable information for the vehicular admission control. When we apply an authentic enhanced mobility model, the results demonstrate the mechanism capability to accurately characterize road traffic congestion conditions, shape vehicular traffic and reduce travel time.     

Distributed and cooperative fuzzy controllers for trafficintersections group

This paper presents a fuzzy traffic controller for a set of intersections and its simulation results. The controller of an intersection controls its own traffic and cooperates with its neighbors. It gets information from its traffic detectors and its neighbors. Using this information, the fuzzy rule base system gives optimal signals. It manages phase sequences and phase lengths adaptively to its neighbors' as well as its own traffic conditions. To carry out the performance evaluation of the controller, a simulator for intersection groups has been developed. The proposed method is compared with the vehicle actuated method which is one of the typical conventional methods. The average delay time of a vehicle is used as a performance index. The simulation results show good performance in the case of time-varying traffic patterns and heavy traffic conditions     

A Genetic Fuzzy Controller for Vehicle Automatic Steering Control

This paper presents the design and experimental implementation of a genetic fuzzy controller for automatic steering of a small-scaled vehicle. We first derive a dynamic model of the vehicle via system identification and show that the model exhibits similar characteristics to full-sized vehicles. Subsequently, a stable fuzzy proportional-derivative controller is designed and optimized by genetic algorithms. The control system is transformed into a Lureacute system, and Lyapunov's direct method is used to guarantee the stability of the control system. Experimental studies suggest that the control system is insensitive to parametric uncertainty, load, and disturbances. The performance of the proposed controller is also compared against a conventional proportional derivative (PD) controller. Experimental results confirm that it outperforms the conventional PD controller, particularly in terms of robustness     

Adaptive Congestion Prediction in Vehicular Ad-hoc Networks (VANET) Using Type-2 Fuzzy Model to Establish Reliable Routes

Urban areas are more prone to accidents and traffic congestions due to ever-increasing vehicles and poor traffic management. The increase in the emission of harmful gases is another important issue associated with vehicular traffic. Attaining a level of QOS is often challenging as it has to meet the eco-friendly factors along with reliable and safe transportation. Smart and accurate congestion management systems in VANET can significantly reduce the risk of accidents and health issues. To fulfil the requirements of QOS the congestion control methods should consider the properties such as fairness, decentralization, network characteristics, and application demands in VANET. We proposed an Adaptive Congestion Aware Routing Protocol (ACARP) for VANET using the dynamic artificial intelligence (AI) technique. The ACARP presents the adaptive congestion detection algorithm using the type-2 fuzzy logic AI technique. The fuzzy model detects the congestion around each vehicle using three fuzzy inputs viz. bandwidth occupancy, link quality, and moving speed. This is followed by inference model to estimate congestion probability for each vehicle. Finally, defuzzification determines status of congestion detection using the pre-defined threshold value for each vehicle. The status of congestion and its probability values were utilized to establish safe and reliable routes for data transmission. It also saves significant communication overhead and hence congestions in the network. The simulation results provide the evidence that the proposed protocol improves the QOS and assist in reduction of traffic congestions significantly.

     

Comparison of admission control schemes in ATM networks

Asynchronous transfer mode (ATM) offers an efficient means of carrying a wide spectrum of BISDN traffic provided that network congestion is prevented. Unfortunately, efficient congestion control is difficult to achieve in integrated broadband networks, owing to the wide range of traffic characteristics and quality of service (QOS) requirements. We have implemented a network simulator that allows us to evaluate many proposed admission control schemes using many different traffic models. We present the results of several simulation studies, including one study of the performance of the admission control schemes in the presence of traffic sources that exhibit long-term dependence.     

Fuzzy neural control of voice cells in ATM networks

This paper presents the design of a fuzzy controller for managing cells generated by voice sources in asynchronous transfer mode (ATM) networks. Typical voice cells, characterized by a high degree of burstiness, complicate any attempt to use classical control theory in the design of an ATM cell rate controller. The fuzzy control approach presented in this paper overcomes this limitation by appealing to the linguistic ability of fuzzy set theory and logic to handle the complexity. Specifically, the cell rate control problem is linguistically stated but treated mathematically via fuzzy set manipulation. In particular, the ATM voice cell controller being proposed is an improved and intelligent implementation of the leaky bucket cell rate control mechanism extensively studied in the literature. This intelligent implementation of the leaky bucket mechanism uses a channel utilization feedback via the QoS parameters to improve its performance. This ATM fuzzy controller takes the form of an organized set of linguistic rules quantitatively expressed and manipulated by means of fuzzy set theory and fuzzy logic. The fuzzy control rules are stored in fuzzy associative memory to permit parallel executions     

Fuzzy Q-Learning Admission Control for WCDMA/WLAN Heterogeneous Networks with Multimedia Traffic

In this paper, admission control by a fuzzy Q-learning technique is proposed for WCDMA/WLAN heterogeneous networks with multimedia traffic. The fuzzy Q-learning admission control (FQAC) system is composed of a neural-fuzzy inference system (NFIS) admissibility estimator, an NFIS dwelling estimator, and a decision maker. The NFIS admissibility estimator takes essential system measures into account to judge how each reachable subnetwork can support the admission request's required QoS and then output admissibility costs. The NFIS dwelling estimator considers the Doppler shift and the power strength of the requested user to assess his/her dwell time duration in each reachable subnetwork and then output dwelling costs. Also, in order to minimize the expected maximal cost of the user's admission request, a minimax theorem is applied in the decision maker to determine the most suitable subnetwork for the user request or to reject. Simulation results show that FQAC can always maintain the system QoS requirement up to traffic intensity of 1.1 because it can appropriately admit or reject the users' admission requests. Also, the FQAC can achieve lower blocking probabilities than conventional JSAC proposed in and can significantly reduce the handoff rate by 15-20 percent.     

Joint admission/congestion control for wireless CDMA systemssupporting integrated services

A new data traffic control scheme is developed for maintaining the packet error rate (PER) of real-time voice traffic while allowing nonreal-time data traffic to utilize the residual channel capacity of the multi-access link in an integrated service wireless CDMA network. Due to the delay constraint of the voice service, voice users transmit their packets without incurring further delay once they are admitted to the system according to the admission control policy. Data traffic, however, is regulated at both the call level (i.e., admission control) and at the burst level (i.e., congestion control). The admission control rejects the data calls that will otherwise experience unduly long delay, whereas the congestion control ensures the PER of voice traffic being lower than a specified quality of service (QoS) requirement (e.g., 10 ^-2). System performance such as voice PER, voice-blocking probability, data throughput, delay, and blocking probability is evaluated by a Markovian model. Numerical results for a system with a Rician fading channel and DPSK modulation are presented to show the interplay between admission and congestion control, as well as how one can engineer the control parameters. The tradeoff of using multiple CDMA codes to reduce the transmission time of data messages is also investigated     

Robust loop shaping-fuzzy gain scheduling control of a servo-pneumatic system using particle swarm optimization approach

In this paper, a new technique called robust loop shaping-fuzzy gain scheduled control (RLS-FGS) is proposed to design an effective nonlinear controller for a long stroke pneumatic servo system. In our technique, a nonlinear dynamic model of a long stroke pneumatic servo plant is identified by the fuzzy identification method and is used as the plant for our design. The structure of local controllers is selected as PID control which is proven by many research works that this type of control has many advantages such as simple structure, well understanding, and high performance. The proposed technique uses particle swarm optimization (PSO) to find the optimal local controllers which maximize the average stability margin. In addition, performance weighting function which is normally difficult to obtain is automatically determined by PSO. By the proposed technique, the RLS-FGS controller can be designed, and the structure of local controllers is still not complicated. As seen in the simulation and experimental results, our proposed technique is better than the classical gain scheduled PID controller tuned by pole placement and the conventional fuzzy PID controller tuned by ISE method in terms of robust performance.     

模糊控制在异步电机直接转矩控制系统中的应用

为提高异步电机直接转矩控制系统的低速性能,本文中提出了一种模糊控制和PI闭环校正磁链观测器相结合的控制方法。该方法采用PI闭环校正磁链观测器代替传统U-I模型,给磁链和转矩误差分级,用模糊控制代替传统滞环控制选择合适的电压矢量．为减少模糊控制规则和加快模糊推理,定义了定子磁链角映射。仿真结果表明,此方法能够提高磁链的观...     

Stable Fuzzy State Space Controller for Lateral Control of an AGV

The primary focus of the paper is on the development of an intelligent control scheme, which is insensitive to parametric uncertainty, load and parameter fluctuations and most importantly amenable for real time implementation. In this paper, we present a stable Lateral Fuzzy Controller (LAFC) for an outdoor Autonomously Guided Vehicle (AGV), which is a converted electrically powered golf-car. The controller performance is assessed both through simulations and experimental results. It is established that the fuzzy logic controller (FLC) yields good performance even under uncertain and variable parameters in the model, unlike the computed torque technique (CTT) or conventional PID control. In terms of real-time implementation the reduced computational complexity of the fuzzy controller as compared to the CTT, makes the fuzzy controller, an ideal choice amongst the two schemes.     

Intelligent Vehicle Power Control Based on Machine Learning of Optimal Control Parameters and Prediction of Road Type and Traffic Congestion

Previous research has shown that current driving conditions and driving style have a strong influence over a vehicle's fuel consumption and emissions. This paper presents a methodology for inferring road type and traffic congestion (RT&TC) levels from available onboard vehicle data and then using this information for improved vehicle power management. A machine-learning algorithm has been developed to learn the critical knowledge about fuel efficiency on 11 facility-specific drive cycles representing different road types and traffic congestion levels, as well as a neural learning algorithm for the training of a neural network to predict the RT&TC level. An online University of Michigan-Dearborn intelligent power controller (UMD_IPC) applies this knowledge to real-time vehicle power control to achieve improved fuel efficiency. UMD_IPC has been fully implemented in a conventional (nonhybrid) vehicle model in the powertrain systems analysis toolkit (PSAT) environment. Simulations conducted on the standard drive cycles provided by the PSAT show that the performance of the UMD_IPC algorithm is very close to the offline controller that is generated using a dynamic programming optimization approach. Furthermore, UMD_IPC gives improved fuel consumption in a conventional vehicle, alternating neither the vehicle structure nor its components.      

Toward a broadband congestion control strategy

The congestion control problem in asynchronous transfer mode (ATM) based broadband networks is defined. In general, a suitable set of congestion controls will include features for admission control, buffer and queue management, traffic enforcement, and reactive control. The leading alternatives for each of these congestion control features are summarized. An approach for choosing the best of these alternatives is presented, and a reasonable set of such alternatives that captures the increased utilization due to statistical multiplexing is suggested. It uses separate and static bandwidth pools for each service category; a statistical multiplexing gain determined for each bandwidth pool that supports a variable-bit-rate (VBR) service category; traffic enforcement on a virtual circuit basis using a leaky bucket algorithm with parameters set to accommodate anticipated levels of cell transfer delay variation; and multilevel loss priorities as well as a reactive control for appropriate VBR service categories based on multithreshold traffic enforcement and explicity congestion notification     

电流模式模糊控制Boost变换器的研究与建模

基于Boost变换器,介绍了一种新的控制方法一电流模式模糊控制。这种新的控制方法属于双环控制,外环由模糊控制器构成,内环是电流环。该控制方法不同于传统的以模糊控制器作控制环路的单环控制。这种新的控制方法结合了传统的模糊控制和电流模式控制的优点,能改善变换器系统的性能。本文建立了电流模式模糊控制的Boost变换器的小信号模型,推导了传递函数。在Matlab／Simulink环境下,做了基于传递函数的仿真和基于电路模块的仿真。仿真结果显示基于传递函数的仿真和基于电路模块的仿真结果一致,证实了本文所建立模型的正确性。