Learning Fuzzy Logic Controller Research Articles

Analysis of vibration high-frequency dynamic characteristics of wheelless rail vehicle system based on fuzzy logic control

Track irregularity is the main source of vibration excitation of vehicle-track weighing system, which has an important impact on the safety, stability and comfort of train operation, and it is also the main factor limiting the speed of train operation. Higher requirements are put forward for track smoothness with the rapid development of China’s railways. Therefore, it has a great theoretical and practical significance to study the relationship between track irregularity and random vibration of vehicle-track cooker-in system and the evaluation method of track smoothness. In this paper, a model construction method is proposed based on machine learning fuzzy logic control and neural network algorithm to analyze the high frequency dynamic characteristics of ballastless track wheel-rail vehicle system. Firstly, a numerical analysis model of high frequency dynamic characteristics of ballastless track wheel-rail vehicle system is established by using linear discrete elastic-yellow damper element connection model; Secondly, the Rough Set Block Neural Network is introduced to optimize the dynamic characteristic analysis model, and the intelligent model of model optimization analysis is established. Finally, the validity of the proposed algorithm is verified by simulation experiments of practical examples.

A cognitive/intelligent resource provisioning for cloud computing services: opportunities and challenges

In cloud computing, resources could be provisioned in a dynamic way on demand for cloud services. Cloud providers seek to realize effective SLA execution mechanisms for avoiding SLA violations by provisioning the resources or applications and timely interacting to environmental changes and failures. Sufficient resource provisioning to cloud’s services relies on the requirements of the workloads to achieve a high performance for quality of service. Therefore, deciding the suitable amount of cloud’s resources for these services to achieve is one of the main works in cloud computing. During the runtime of services, the amount of cloud’s resources can be specified and provisioned based on the actual workloads changes. Determining the correct amount of cloud’s resources needed for running the services on clouds is not easy task, and it depends on the existing workloads of services. Consequently, it is required to predict the future workloads for dynamic provisioning of resources in order to meet the changes in workloads and demands of services in cloud computing environments. In this paper, we study the possibility of using a cognitive/intelligent approach for cloud resource provisioning which is a combination of the autonomic computing concept, deep learning technique and fuzzy logic control. Deep learning technique is a state-of-the-art in the machine learning field. It achieved promising results in many other fields like image classification and speech recognition. For these reasons, deep learning is proposed in this work to tackle the workload prediction in cloud computing. Additionally, we also propose to use a fuzzy logic-based method in order to make a decision in the case of uncertainty of the workload prediction. We study various exiting works on autonomic cloud resource provisioning and show that there is still an opportunity to improve the current methods. We also present the challenges that may exist on this domain.

A Self Learning Fuzzy Logic Controller for Ship Steering System

A Self Learning Fuzzy Logic Controller for Ship Steering System

A Self Learning Fuzzy Logic Controller for Ship Steering System

A self learning fuzzy logic controller for ship steering systems is proposed in this paper. Due to the high nonlinearity of ship steering system, the performances of traditional control algorithms are not satisfactory in fact. An intelligent control system is designed for controlling the direction heading of ships to improve the high efficiency of transportation, the convenience of manoeuvring ships, and the safety of navigation. The design of fuzzy controllers is usually performed in an ad hoc manner where it is hard to justify the choice of some fuzzy control parameters such as the parameters of membership function. In this paper, self tuning algorithm is used to adjust the parameters of fuzzy controller. Simulation results show that the efficiency of proposed algorithm to design a fuzzy controller for ship steering system.

Coordinated secondary voltage control to eliminate voltage violations in power system contingencies

In order to achieve more efficient voltage regulation in a power system, coordinated secondary voltage control has been proposed, bringing in the extra benefit of enhancement of power system voltage stability margin. This paper investigates a new potential application of coordinated secondary voltage control by multiple FACTS voltage controllers in eliminating voltage violations in power system contingencies. The study is presented by the example New England ten-machine power system with two SVCs and two STATCOMs installed. The coordinated secondary voltage control is assigned to the SVCs and STATCOMs in order to eliminate voltage violations in system contingencies. In the paper, it is proposed that the secondary voltage control is implemented by a learning fuzzy logic controller. A key parameter of the controller is trained by P-type learning algorithm via offline simulation with the assistance of injection of artificial loads in controller's adjacent locations. A multiagent collaboration protocol, which is graphically represented as a finite-state machine, is proposed in the paper for the coordination among multiple SVCs and STATCOMs. As an agent, each SVC or STATCOM can provide multilocation coverage to eliminate voltage violations at its adjacent nodes in the power system. Agents can provide collaborative support to each other which is coordinated according to the proposed collaboration protocol.

INVERSE DYNAMICS AND FUZZY REPETITIVE LEARNING FLEXIBLE ROBOT CONTROL

Tracking of a square trajectory 12.6 m x 12.6 m by a two-link flexible robot manipulator is performed repetitively for both inverse dynamics control (IDC) and fuzzy logic control (FLC). Repetitive learning inverse dynamics control (RLIDC) achieves no improvement in tracking but repetitive learning fuzzy logic control (RLFLC) achieves greater precision where cyclic tracking enables the fuzzy inference system to self-adapt and further reduce tracking errors.

Self Learning Fuzzy Logic Control of Dissolved Oxygen Concentration in a Fermentation Process

Biotechnological processes are notoriously difficult to control using conventional methods owing to their time-variant and non-linear characteristics. An alternative control method is to utilise artificial intelligence such as fuzzy logic. A problem with the development of fuzzy logic controllers is rule-base generation. A Self-Learning Fuzzy Logic Control (SLFLC) methodology offers a possible alternative solution. A study is presented illustrating the performance of a SLFLC by demonstrating its ability to generate a rule-set based on a predetermined criteria. The SLFLC described has been successfully applied to simulations of a Bakers Yeast fed-batch fermentation. Results demonstrate how the learning mechanism produces a rule-set which controls the dissolved oxygen concentration throughout the fermentation process duration.

Vibration Control of a Hydraulic System using a Learning Fuzzy Logic Controller.

This paper describes a pump-controlled hydraulic system with a variable displacement pump to control the acceleration of a hydraulic cylinder. The system has non-linearity due to viscosity and compressibility of oil, packing friction, etc. So it is difficult to find a mathematical model of the process as opposed to conventional control systems and control them. Fortunately this system can be controlled by Fuzzy Logic, needless to find the troublesome mathematical model that has been proved applicable by our experimental results. However, it is not easy to acquire the control rule from our experimental results. For simple and effective tuning the fuzzy controller, we apply the Fuzzy Neural Network. It is shown that good results are achieved by use of the learning fuzzy logic controller.

ASAFES2: a novel, neuro-fuzzy architecture for fuzzy computing, based on functional reasoning

The functional reasoning or Takagi-Sugeno fuzzy reasoning method is a very promising approach to the task of multi-variable, non-linear function approximation, and the design of a fuzzy model and fuzzy controller. The proposed architecture, ASAFES2, is a neuro-fuzzy function approximator, and is the first attempt to combine this reasoning method with stochastic reinforcement learning. It can “learn” a function to a high degree of accuracy, at a very great speed, and furthermore, expresses it in a set of linear relations. The main ideas are the fuzzy partitioning of the input space into fuzzy subspaces (each corresponding to a possible fuzzy rule), and the use of a separate neural unit for every fuzzy subspace, in order to calculate the optimum consequence parameters. Simulation results prove its superiority over back-propagation in simple non-linear function approximation tasks, and over all previous approaches to the Box and Jenkins gas furnace modelling. Comparisons are also made with Generalized Radial Basis Function Networks. Finally, ASAFES2, as a building block in a learning fuzzy logic controller, is applied to the well-known cart-pole stabilization problem, with satisfactory results. A new, flexible membership function is also presented.

Application Using VLSI Hardware Realization of Self–Learning Recursive Fuzzy Model

This paper presents a hardware application of a self—learning fuzzy logic controller on industrial gas furnace data. The fuzzy algorithm is based upon a unique probability theory approach to fuzzy identification and estimation. This method was chosen because of an inherent filtering operation making it highly immune to noise. In the past the majority of fuzzy inference processors have been designed as application-specific circuits or custom IC’s. Using semi—custom programmable IC’s resolves the problems of development costs, adaptability of designs and execution speed.