13-07-2012, 12:04 PM
Fuzzy Logic and Anti-Lock Braking System
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Introduction
In establishing a non-complex and practical application of the phenomenon of fuzzy logic it is important to consider a trifling situation in everyday life which is applicable to all, thus the event of anti-lock braking system was considered as a prime example of how we could demonstrate the implementation of fuzzy logic.
The result of this literature review will show the implementation of fuzzy logic in this relatively low-level control of some machinery as opposed to a high-level artificial intelligence application. This application is not uncommon as it has been seen where fuzzy logic has been implemented in relatively simple systems such as washing machines, traffic control, truck speed limiter, aircraft flight path and air conditioning to name a few. These implementations can be seen as an underachievement for the technology as fuzzy logic was originally developed to solve the complexities involved in the discipline of Artificial Intelligence. Mimicking the thought process of humans as well as doing language translations, were key areas that fuzzy logic was developed to be implemented.
Fuzzy Logic
Fuzzy set theory, from whence fuzzy logic comes, was developed in 1965 by Lotfi Zadeh to combat the imprecision and uncertainties that exists in the everyday world. Its applications are geared towards solving non-mathematically distinct problems and allow statements to be answered with more than a YES or NO. Its derivative from traditional logic theorems, allows it to include all the properties of that system in addition to the new properties that were developed and hence, mapping functions, ordering and arithmetic operations all apply to fuzzy logic.
As opposed to its classical counterpart, fuzzy logic possesses exceedingly greater capabilities to capture uncertainties in their various forms, and as a result improves the gap between mathematical models and the associated physical reality. Fuzzy logic is capable of capturing the vagueness of linguistic terms in statements that are expressed in natural languages. Modeling human common sense reasoning, decision-making and other aspects of human cognition are enhanced with the use of fuzzy logic. These capabilities are essential in acquiring knowledge from human experts, representing and manipulating knowledge in expert systems in a human-like manner, and, generally in designing and building systems, which exhibit high levels of intelligence. Behavior, which is associated with perception rather than measurements, is an intriguing basis for fuzzy logic explorations.
Electronic control systems in the automotive industry are currently being pursued in the United States, and the reality of superior performance through the use of fuzzy logic based control rather than traditional control algorithms. Fuzzy logic strives to establish a value for linguistic expressions like “fast”, “slow” and “long” by finding an interval between 0% and 100% to accurately express the truthfulness of an expression. It also uses “if, then” rules to determine outcomes for particular input data. With this construct, it is possible to build rules such as:
“If the rear wheels are turning slowly and a short time ago the vehicle speed was high, then reduce rear brake pressure".
Such rules provide themselves to the development of an ABS braking system based on fuzzy logic, and as such we shall proceed to describe this development as best as possible, given the predefined limit of this research document.
The Fuzzy ABS System
ABS is implemented in automobiles to ensure optimal vehicle control and minimal stopping distances during hard or emergency braking. The number of cars equipped with ABS has been increasing continuously in the last few years. ABS is now accepted as an essential contribution to vehicle safety. The methods of control utilized by ABS are responsible for system performance.
Since ABS systems are nonlinear and dynamic in nature, they are a prime candidate for fuzzy logic control. For most driving surfaces, as vehicle braking force is applied to the wheel system, the longitudinal relationship of friction between vehicle and driving surface rapidly increases. Wheel slip under these conditions is largely considered to be the difference between vehicle velocity and a reduction of wheel velocity during the application of braking force. Brakes work because friction acts against slip. The more slip, given enough friction, the more braking force is brought to bear on the vehicles momentum. Unfortunately, slip can and will work against itself during cornering or on wet or icy surfaces where the coefficient of surface friction varies. If braking force continues to be applied beyond the driving surface’ useful coefficient of friction, the brake effectively begins to operate in a non-friction environment. Increasing brake force in a decreasing frictional environment often results in full wheel lockup. It has been both mathematically and empirically proven a sliding wheel produces less friction than a moving wheel.
How does it work?
Conventional ABS control algorithms must account for non-linearity in brake torque due to temperature variation and dynamics of brake fluid viscosity. Also, external disturbances such as changes in frictional coefficient and road surface must be accounted for, not to mention the influences of tire wear and system components aging. These influential factors increase system complexity, in turn, affecting mathematical models used to describe systems. As the model becomes increasingly complex, equations required to control ABS also become increasingly complicated. Due to the highly dynamic nature of ABS many assumptions and initial conditions are used to make control achievable. Once control is achieved the system is physically implemented and tested. The system is then modified to achieve the desired control status.
[attachment=27530]
Introduction
In establishing a non-complex and practical application of the phenomenon of fuzzy logic it is important to consider a trifling situation in everyday life which is applicable to all, thus the event of anti-lock braking system was considered as a prime example of how we could demonstrate the implementation of fuzzy logic.
The result of this literature review will show the implementation of fuzzy logic in this relatively low-level control of some machinery as opposed to a high-level artificial intelligence application. This application is not uncommon as it has been seen where fuzzy logic has been implemented in relatively simple systems such as washing machines, traffic control, truck speed limiter, aircraft flight path and air conditioning to name a few. These implementations can be seen as an underachievement for the technology as fuzzy logic was originally developed to solve the complexities involved in the discipline of Artificial Intelligence. Mimicking the thought process of humans as well as doing language translations, were key areas that fuzzy logic was developed to be implemented.
Fuzzy Logic
Fuzzy set theory, from whence fuzzy logic comes, was developed in 1965 by Lotfi Zadeh to combat the imprecision and uncertainties that exists in the everyday world. Its applications are geared towards solving non-mathematically distinct problems and allow statements to be answered with more than a YES or NO. Its derivative from traditional logic theorems, allows it to include all the properties of that system in addition to the new properties that were developed and hence, mapping functions, ordering and arithmetic operations all apply to fuzzy logic.
As opposed to its classical counterpart, fuzzy logic possesses exceedingly greater capabilities to capture uncertainties in their various forms, and as a result improves the gap between mathematical models and the associated physical reality. Fuzzy logic is capable of capturing the vagueness of linguistic terms in statements that are expressed in natural languages. Modeling human common sense reasoning, decision-making and other aspects of human cognition are enhanced with the use of fuzzy logic. These capabilities are essential in acquiring knowledge from human experts, representing and manipulating knowledge in expert systems in a human-like manner, and, generally in designing and building systems, which exhibit high levels of intelligence. Behavior, which is associated with perception rather than measurements, is an intriguing basis for fuzzy logic explorations.
Electronic control systems in the automotive industry are currently being pursued in the United States, and the reality of superior performance through the use of fuzzy logic based control rather than traditional control algorithms. Fuzzy logic strives to establish a value for linguistic expressions like “fast”, “slow” and “long” by finding an interval between 0% and 100% to accurately express the truthfulness of an expression. It also uses “if, then” rules to determine outcomes for particular input data. With this construct, it is possible to build rules such as:
“If the rear wheels are turning slowly and a short time ago the vehicle speed was high, then reduce rear brake pressure".
Such rules provide themselves to the development of an ABS braking system based on fuzzy logic, and as such we shall proceed to describe this development as best as possible, given the predefined limit of this research document.
The Fuzzy ABS System
ABS is implemented in automobiles to ensure optimal vehicle control and minimal stopping distances during hard or emergency braking. The number of cars equipped with ABS has been increasing continuously in the last few years. ABS is now accepted as an essential contribution to vehicle safety. The methods of control utilized by ABS are responsible for system performance.
Since ABS systems are nonlinear and dynamic in nature, they are a prime candidate for fuzzy logic control. For most driving surfaces, as vehicle braking force is applied to the wheel system, the longitudinal relationship of friction between vehicle and driving surface rapidly increases. Wheel slip under these conditions is largely considered to be the difference between vehicle velocity and a reduction of wheel velocity during the application of braking force. Brakes work because friction acts against slip. The more slip, given enough friction, the more braking force is brought to bear on the vehicles momentum. Unfortunately, slip can and will work against itself during cornering or on wet or icy surfaces where the coefficient of surface friction varies. If braking force continues to be applied beyond the driving surface’ useful coefficient of friction, the brake effectively begins to operate in a non-friction environment. Increasing brake force in a decreasing frictional environment often results in full wheel lockup. It has been both mathematically and empirically proven a sliding wheel produces less friction than a moving wheel.
How does it work?
Conventional ABS control algorithms must account for non-linearity in brake torque due to temperature variation and dynamics of brake fluid viscosity. Also, external disturbances such as changes in frictional coefficient and road surface must be accounted for, not to mention the influences of tire wear and system components aging. These influential factors increase system complexity, in turn, affecting mathematical models used to describe systems. As the model becomes increasingly complex, equations required to control ABS also become increasingly complicated. Due to the highly dynamic nature of ABS many assumptions and initial conditions are used to make control achievable. Once control is achieved the system is physically implemented and tested. The system is then modified to achieve the desired control status.