Fuzzy Controlled Anti Lock Braking System Mechanical Engineering Project
Fuzzy Controlled Anti Lock Braking System Project Abstract:
This thesis describes an intelligent approach to control an Antilock Braking System (ABS) employing a fuzzy controller. Stopping a car in a hurry on a slippery road can be very challenging. Anti-lock braking systems (ABS) take a lot of the challenge out of this sometimes nerve-wracking event. In fact, on slippery surfaces, even professional drivers can’t stop as quickly without ABS as an average driver can with ABS. Anti-lock Brake improves the controllability of vehicles in compare with brake systems lacking ABS. Fuzzy is a multi-valued logic developed to deal with imprecise or vague data. Classical logic holds that everything can be expressed in binary terms: 0 or 1; in terms of Boolean algebra, everything is in one set or another but not in both. Fuzzy logic allows for partial membership in a set, values between 0 and 1. When the approximate reasoning of fuzzy logic is used with an expert system, logical inferences can be drawn from imprecise relationships.
Fuzzy anti-lock Braking systems were developed to reduce skidding and maintain steering control when brakes are used in an emergency situation. Fuzzy controllers are potential candidates for the control of non-linear, time variant and complicated systems. There are many control algorithms for ABS systems and they are partially responsible for their performance. Here, in this paper we have discussed how Anti-Lock Braking system is controlled using Fuzzy Logic.
INTRODUCTION TO FUZZY LOGIC
Fuzzy logic is a mathematical technique for dealing with imprecise data and problems that have many solutions rather then one. Although it is implemented in digital computers which ultimately makes yes-no decisions, fuzzy logic works with ranges of values, solving problems in a way that more resembles human logic.
Fuzzy logic, a more generalized data set, allows for a “class” with continuous membership gradations. It is rigorously structured in mathematics. One advantage is the ability to describe systems linguistically through rule statements.
The fuzzy controller takes input values from real world. These values referred to as “crisp” values. Since they are represented as single number, not a fuzzy one. In order for the fuzzy controller to understand the input, the crisp input has to be converted to a fuzzy number. This process is called fuzzification.
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