Fuzzy Logic Maximum Structure and State Feedback Control Strategies of the Electrical Car

AbstractThis paper treats the design and control of different models and control strategies for an Electric Vehicle (EV). An hybrid controller is designed using a fuzzy logic integrated in Maximum Control Structure (FL-MCS), the FL nonlinear controller involves online estimation of the total reference force which corresponds to a torque reference to be applied to MCS. The second proposed regulator is a states feedback controller using the Linear Quadratic Regulation (LQR) to optimise and to determine the feedback control parameters. The LQR allows reducing the consumption of the energy according to the desired EV's dynamic performances, these lasts can be changed depending on the choice of Q and R matrices. In this work, we apply and validate the proposed control strategies by a comparison between our simulation results and the results of the classical MCS, which has been developed by L2EP (Lille, France) to control the EV speed under Matlab/Simulink

HYBRID MAXIMUMCONTROL STRUCTUREUSING FUZZY LOGICOF ELECTRIC VEHICLE

This paper presents a Modelling of traction control system of an Electric Vehicle (EV) based on the Energetic Macroscopic Representation (EMR) and the Maximum Control Structure (MCS). This last is using Fuzzy Logic Control(FLC) toinvert the EMR accumulation element for the control task. A developed combination of fuzzy control strategy with SMC combines the advantages of these two approaches and facilitates the inversion of the accumulation elements. In order to validate the simulation results, a comparison between the results obtained by MCS using IP controller which has already been developed by L2EP laboratory (Lille, France) and the presented MSC-FLC obtained by Matlab/Simulink software tool is include

An Adaptive Fuzzy Rule Extraction Using Hybrid Model of the Fuzzy Self-Organizing Map and the Genetic Algorithm with Numerical Chromosomes

In this paper, we propose a hybrid model of the fuzzified Kohonen's Self-Organizing Map and the GA with numerical chromosomes, and automatic fuzzy rule extraction method that uses our model. It is shown that our hybrid model is superior to both of the individual models in cases where there is a tendency for data to change dynamically and quickly. 1 Introduction Though fuzzy inference rules have usually been constructed through trial and error by humans, many methods with machine learning such as neural networks and genetic algorithms have recently been proposed for automatic rule extraction from a given set of input-output data examples. For the purpose of extracting fuzzy If-Then rules from input-output data, we have proposed the Fuzzy Self-Organizing Map (FSOM) [2], which has both the architecture of Kohonen 's Self-Organizing Map (SOM) [1] and the structure of fuzzy If-Then rules. In addition, we have also proposed a genetic algorithm using numerical chromosomes and an appropriate ..

IMPROVING UNDERSTANDABILITY AND UNCERTAINTY MODELING OF DATA USING FUZZY LOGIC SYSTEMS

The need for automation, optimality and efficiency has made modern day control and monitoring systems extremely complex and data abundant. However, the complexity of the systems and the abundance of raw data has reduced the understandability and interpretability of data which results in a reduced state awareness of the system. Furthermore, different levels of uncertainty introduced by sensors and actuators make interpreting and accurately manipulating systems difficult. Classical mathematical methods lack the capability to capture human knowledge and increase understandability while modeling such uncertainty. Fuzzy Logic has been shown to alleviate both these problems by introducing logic based on vague terms that rely on human understandable terms. The use of linguistic terms and simple consequential rules increase the understandability of system behavior as well as data. Use of vague terms and modeling data from non-discrete prototypes enables modeling of uncertainty. However, due to recent trends, the primary research of fuzzy logic have been diverged from the basic concept of understandability. Furthermore, high computational costs to achieve robust uncertainty modeling have led to restricted use of such fuzzy systems in real-world applications. Thus, the goal of this dissertation is to present algorithms and techniques that improve understandability and uncertainty modeling using Fuzzy Logic Systems. In order to achieve this goal, this dissertation presents the following major contributions: 1) a novel methodology for generating Fuzzy Membership Functions based on understandability, 2) Linguistic Summarization of data using if-then type consequential rules, and 3) novel Shadowed Type-2 Fuzzy Logic Systems for uncertainty modeling. Finally, these presented techniques are applied to real world systems and data to exemplify their relevance and usage