application note ap-700 october 1994 intel fuzzy logic tool simplifies abs design charly gullett automotive technical marketing, intel corporation david elting and robert kowalczyk applications engineers, intel corporation mohammed fennich engineer, intel corporation bert hellenthal fuzzy logic engineer, inform software corporation order number: 272595-001
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ap-700 introduction in recent years fuzzy logic control techniques have been applied to a wide range of systems. many electronic control systems in the automotive industry such as au- tomatic transmissions, engine control and anti-lock brake systems (abs) are currently being pursued in the united states. these electronically controlled auto- motive systems realize superior characteristics through the use of fuzzy logic based control rather than tradi- tional control algorithms. 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 perform- ance. intel corporation is the leading supplier of microcon- trollers for abs and enjoys a technology agreement with inform software corporation the leading supplier of fuzzy logic tools and systems. the combination of intel abs architecture and fuzzy logic is a result of long term investment and exploration of new technolo- gies and ideas. the increasing automotive customer awareness of abs has greatly increased the demand for this technology. improving abs capability is a mutual goal of automotive manufacturers and intel corpora- tion. the growing interest in the automotive community to implement fuzzy logic control in automotive systems has produced several major automotive product intro- ductions. the 1993 mitsubishi gallant, introduced in the spring of 1992 uses fuzzy logic to control four of its automotive systems 1 . general motors highly successful saturn utilizes fuzzy logic for automatic transmission shift control. fuzzy logic overview formal control logic is based in the teachings of aris- totle, where an element either is or is not a member of a particular set. since many of the objects encountered in the real world do not fall into precisely defined mem- bership criteria, some experimentation was inevitable. l. a. zadeh was one of those who investigated alterna- tive forms of data classification. the result of this inves- tigation was the introduction of fuzzy sets and fuzzy theory at the university of california berkeley in 1965. fuzzy logic, a more generalized data set, allows for a ``class'' with continuous membership gradations. this form of classification with degrees of membership offers a much wider scope of applicability, especially in con- trol applications. although fuzzy logic is rigorously structured in mathe- matics, one advantage is the ability to describe systems linguistically through rule statements. one such control rule statement for an air conditioning unit might be: ``if temperature is hot and time of day is noon then air conditioning equals very high.'' several rules, similar to the example, could be used to describe a system and controlled response. the parame- ters of hot, time and very high are defined by mem- bership functions. as linguistic descriptions of a system are much easier to produce than complex mathematical models, fuzzy logic has great appeal for controlling complex systems as changes in the system have little if any effect upon the algorithm. fuzzy abs would re- quire more complex control constructs than simple ``if- then'' rules. in this type of control system, input vari- ables map directly to output variables. this simple mapping does not provide enough flexibility to encode a complex system such as an abs system. however, more complex techniques are available which can be applied to fuzzy logic systems. for example, it is possi- ble to build a control with intermediate fuzzy variables, or systems which have memory. with these constructs, 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 break pressure.'' such rules lend themselves to development of an abs braking system based on fuzzy logic. the output of a fuzzy logic system is determined in one of several ways. the center of gravity (cog) technique will be dis- cussed in this paragraph. once all rules are evaluated, their outputs are combined in order to provide a single value that will be defuzzified. this output calculation is performed as follows. the control rule output value is multiplied by its position along the x-axis, yielding po- sition times weight for the rule. this calculation is re- peated for all control rules. these position/weight products are combined to form the sum of products. this sum of the products is divided by the sum of out- put values to determine the cog output along the x-axis. cog is the final system output in a control algorithm. 1
ap-700 fuzzy abs abs systems were introduced to the commercial vehi- cle market in the early 1970's to improve vehicle brak- ing irrespective of road and weather conditions. how- ever, due to the technical difficulties and high cost of early systems, abs was not recognized by auto makers as an advantage until the mid-1980's. the abs market has rapidly grown and is forecast to be $5 billion yearly by 1995 and $10 billion or more by the year 2000. ex- perts predict that 35% to 50% of all cars built world- wide in five years will have abs as standard equip- ment. 2 electronic control units (ecus), wheel speed sensors, and brake modulators are major components of an abs module. wheel speed sensors transmit pulses to the ecu with a frequency proportional to wheel speed. the ecu then processes this information and regulates the brake accordingly. the ecu and control algorithm are partially responsible for how well the abs system performs. this paper will focus on using the intel 8xc196kx product family, as the ecu, to implement a fuzzy logic control algorithm for use in an abs sys- tem. since abs systems are nonlinear and dynamic in na- ture 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 relation- ship of friction between vehicle and driving surface rap- idly 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 fric- tion acts against slip. the more slip given enough fric- tion, 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 environ- ment. 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 a moving wheel. inputs to the intel fuzzy abs are derived from wheel speed. acceleration and slip for each wheel may be cal- culated by combining the signals from each wheel. these signals are then processed in the intel fuzzy abs system to achieve the desired control. unlike ear- lier 8-bit microcontroller architectures with limited math capability, the intel fuzzy abs example utilizes a high performance, low cost, 16-bit 8xc196kx architec- ture to take advantage of improved math execution tim- ing. note the minimal cpu loading (7.64%) due to a complex matrix multiply in the next figure. modelbuilder unlike a conventional abs system, performance of the intel fuzzy abs system can be optimized with less detailed knowledge of the internal system dynamics. this is due to the process used to refine the rule base and in the initial development of the system using in- form software corporation fuzzytech * 3.0 mcu-96 software tuned for the intel architecture with opti- mized code output and the associated real time cross debugger. the software tool set combined with a lin- guistic approach to control implemented in the intel fuzzy abs solution allows for rapid development. a cornerstone of this rapid development is the intel fuzzy logic modeling software kit called fuzzybuilder. the development system, called fuzzytech mcu-96, is specifically optimized for the mcs 96 controller architecture. it contains: # a fully graphical case tool that supports all design steps for fuzzy system engineering. # a simulation and optimization tool for fuzzy sys- tems. this tool displays system performance and can be interfaced to conventional simulators to ob- tain performance data. # a code generator which generates complete c-code for the fuzzy system. the c-code calls optimized assembly routines on the target controller for fast performance. table 1 shows the performance of several test systems on a 20 mhz 8xc196kx device. all times show are worst case execution results. note fam rules are indi- vidually weighted as opposed to a system in which all rules have identical weight. table 1. test system performance 7 rules 20 rules 20 fam rules 80 fam rules 2 in/1 out 2 in/1 out 2 in/1 out 3 in/1 out 0.22 ms 0.33 ms 0.34 ms 0.50 ms conventional abs control algorithms must account for non-linearity in brake torque due to temperature varia- tion and dynamics of brake fluid viscosity. also, exter- nal disturbances such as changes in frictional coeffi- cient and road surface must be accounted for, not to 2
ap-700 272595 1 mention the influences of tire wear and system compo- nents aging. these influential factors increase system complexity, in turn effecting mathematical models used to describe systems. as the model becomes increasingly complex equations required to control abs also be- come increasingly complicated. due to the highly dy- namic nature of abs many assumptions and initial conditions are used to make control achievable. once control is achieved the system is implemented in-vehi- cle and tested. the system is then modified to attain the desired control status. however, due to the nature of fuzzy logic, influential dynamic factors are accounted for in a rule based de- scription of abs. this type of ``intelligent'' control al- lows for faster development of system code. a recent article entitled ``fuzzy logic anti-lock brake system for a limited range coefficient of friction surface,'' 1993 ieee, addresses some of the issues associated with initial development of fuzzy abs from the per- spective of a system manufacturer. 3
ap-700 abs block diagram figure 1 shows the abs block diagram. 272595 3 figure 1 inputs the inputs to the intel fuzzy abs are represented in figure 1 and consist of: 1. the brake: this block represents the brake pedal deflection/assertion. this information is acquired in a digital or analog format. 2. the 4 w.d.: this indicates if the vehicle is in the 4- wheel-drive mode. 3. the ignition: this input registers if the ignition key is in place, and if the engine is running or not. 4. feed-back: this block represents the set of inputs concerning the state of the abs system. 5. wheel speed: in a typical application this will repre- sent a set of 4 input signals that convey the informa- tion concerning the speed of each wheel. this infor- mation is used to derive all necessary information for the control algorithm. 4
ap-700 fuzzytech * 3.0 272595 2 the proposed system shown above has two types of outputs. the pwm signals to control abs braking, and an error lamp signal to indicate a malfunction if one exists. intel fuzzy abs features in the intel fuzzy abs an embedded 87C196JT micro- controller (a member of the 8xc196kx family) is used in conjunction with inform software corporation fuz- zytech software. rules constitute the base of the algorithm and are evaluated in sequence, one after the other. upon completion of all rule processing the final system output is calculated as previously described. in contrast, if a custom dedicated fuzzy parallel proces- sor were to be used, rules could be evaluated in parallel. the parallel processing method suggests a fast process- ing cycle. however, in this case data acquisition and data output continues using conventional peripherals. the time gained in parallel rule processing can be lost in acquiring and manipulating data via external periph- erals. 5
ap-700 the best solution continues to use a software fuzzy al- gorithm on a microcontroller with fast internal periph- erals. in this case, sequential rule processing is trans- parent to the system and the process appears to have been done in parallel. the mcs 96 family of microcon- trollers is equipped with high performance internal pe- ripherals that make data acquisition and data condi- tioning of outputs fast and easy to handle. this, and the wide range of addressing modes, broad availability of interrupts and a powerful set of instructions make intel microcontrollers immanently suitable for fuzzy logic applications. 8xc196kxea perfect match for an abs implementation, the mcs 96 microcon- troller family is also a perfect match. the high speed input output unit can be used to effectively handle i/o without impacting precious on-chip timer resources. most microcontrollers in the intel 16-bit family have also incorporated on-chip analog-to- digital converters with 1024 discrete codes (10-bit resolution). the use of on-chip a/d reduces chip count. the a/d can be used to sense braking action taken by the driver. in addition, there is a large set of both direct and indirect interrupts to deal with real-time events and exceptions. the prior- ity scheme of the interrupts can be modified dynamical- ly in software. for outputs the on-chip pulse width modulator (pwm) unit is available for use in providing variable output signals to the individual wheels. changing the frequency and/or the duty cycle of the pwm can be done simply with a very fast register write operation. in addition to the peripherals, microcontrollers in the intel 16-bit mcs 96 microcontroller family have inter- nal ram and rom. program instructions and data can be stored on-chip for optimized execution. no long external bus cycles are required to read data due to the large register based architecture. this feature is ex- tremely beneficial to fuzzy logic. the knowledge base, i.e., the rules and the membership functions can be stored on-chip. thus, rules can be evaluated in a very short amount of time. conclusion the use of fuzzy-logic in conjunction with microcon- trollers is a fairly new development in automotive ap- plications. intel is not currently aware of any projects in production for abs applications, but there have been numerous papers presented on using fuzzy logic and or neural networks to control such automotive applica- tions as abs, automatic braking for collision avoid- ance, adaptive cruise control and chassis control. fuzzy sets and systems is an excellent journal devoted to fuzzy logic and control systems based on fuzzy logic. the authors charly gullett has been an applications engineer in- volved in technical marketing with intel corporation for twelve years. david elting and robert kowalczyk are applications engineers with intel corporation's automotive opera- tion in chandler, arizona. mohammed fennich is an engineer with intel corpora- tion's embedded microcomputer division fuzzy logic operation. bert hellenthal is a fuzzy logic engineer with inform software corporation. intel is a manufacturer of microprocessor, microcon- troller and networking solutions. references 1. ``mitsubishi unveils new gallant'' , automotive news, 18 may 1992. 2. ``the abs race is on'' , ward's autoworld, may 1989, pg. 61. 6
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