In the last few years microcontrollers found a big place in industrial applications. It had been

used in many applications such as washing machines, textile machines and many other fields.

Microchip PIC microcontrollers are one of the most used types of microcontroller. In this paper

we consider a method which can be used to implement a fuzzy control system and the

description of this implementation technique. The proposed method will be applied to

implement the fuzzy guidance systems  on a microchip PIC16F877A

microcontroller system

El-Sayed, Ahmed

Rizk, Mohamed

Sobh, Tarek M.

In the last few years microcontrollers found a big place in industrial applications. It had been
used in many applications such as washing machines, textile machines and many other fields.
Microchip PIC microcontrollers are one of the most used types of microcontroller. In this paper
we consider a method which can be used to implement a fuzzy control system and the
description of this implementation technique. The proposed method will be applied to
implement the fuzzy guidance systems  on a microchip PIC16F877A
microcontroller system

UB ScholarWorks

Microcontroller Implementation of Fuzzy Guidance

Abstract Microcontroller Implementation of Fuzzy Guidance System Ahmed ElSayed1, Mohamed Rizk2, Tarek Sobh1 1 Department of Computer Science and Engineering, University of Bridgeport 2Electrical Engineering Department, Alexandria University In the last few years microcontrollers found a big place in industrial applications. It had been used in many applications such as washing machines, textile machines and many other fields. Microchip PIC microcontrollers are one of the most used types of microcontroller. In this paper we consider a  method which can be used to implement a fuzzy control system and the description of this implementation technique. The proposed method will be applied to implement the fuzzy guidance systems discussed in [1],[2] on a microchip PIC16F877A microcontroller system. There are number of methods to implement the fuzzy logic control system. Generally, we can divide them into two major categories as shown in table 1.   Fuzzy Systems Implementation Techniques References The value of each crisp input will be added to the given offset address to get μ of that input. Then, the minimum (denoted by α) among the μ values from the inputs is calculated. α will be used in aggregation process. c) Fuzzy rule base: The fuzzy rule base is stored with the inputs and output membership functions in the knowledge base memory (KBM). Figure (6) shows the memory map of KBM. In this map, the address increases from top to bottom. From left to right, the map items are shown from the biggest group division to the smallest item details hierarchically. The rightmost column shows the memory space needed to store the data at the corresponding rows.  The first byte of the memory stores the number of rules for the output. The following parts are the actual fuzzy rule base definition. They are fuzzy rule table, input membership function table and output membership function table. This memory organization creates flexibility for the user. The number of rules for can be up to 255. The number of memberships for inputs and outputs is not stated in the memory and can be defined freely by user, as the rule table will directly point to the offset address of the membership function.  [1] M. Rizk, A. ElSayed, "AIR-TO-GROUND INTEGRATED FUZZY GUIDANCE SYSTEM", Ubiquitous Computing and Communication Journal (ISSN 1992-8424), UbiCC Journal, Volume 6: Issue 2. [2] M. Rizk, A. ElSayed, "LEARNING AIR-TO-GROUND INTEGRATED FUZZY GUIDANCE SYSTEM USING TABU SEARCH", Ubiquitous Computing and Communication Journal (ISSN 1992-8424), UbiCC Journal, Volume 6: Issue 2. [3]Costa, A., Gloria, A. De & Olivieri, M. (1996). Hardware Design of Asynchronous Fuzzy Controllers. IEEE Transactions on Fuzzy Systems. 4 (3); 328-338.  [4]Tan, Kok Leong (1998). Fuzzy Expert System in Navigation Control” Thesis B. Eng., Universiti Teknologi Malaysia.  Figure 3: Proposed System Architecture  Standard hardware Fuzzy hardware 1) Computer: PC or WS 2) Processors: microprocessor, microcontroller, DSP 3) Look-up table: digital memory devices 4) A/D & D/A converters  1) Fuzzy logic controller: CMOS semi/full custom design 2) Fuzzy processor: microprocessor plus fuzzy add on 3) Fuzzy microcontroller: microcontroller plus FLC 4) Analog fuzzy controller: analog circuit design Proposed System Architecture  Fuzzy Processor (Microcontroller) D/A I2C   Serial  Interface PC Guidance Card Control program Analog input Analog output  Fuzzy toolbox A/D     Fuzzy Rule Base Fuzzy Inference Engine   Fuzzifier  Defuzzifier  Crisp input value Crisp output value Figure 4, Configuration of fuzzy system with fuzzifier and defuzzifier  Implemented Fuzzy Algorithm The fuzzy algorithm used in this paper is modified from conventional fuzzy algorithms to optimize the system performance by reducing computation operations. The following subsections describe each stage of the fuzzy algorithm. a) Fuzzification: The input membership functions are defined in a look-up table based method. The universe of discourse of the input variables is limited to ten bits length (A/D resolution), ranges from 0 to 1023. The resolution of membership grade (denoted by μ) is 8 bit wide, ranges from 0 to 255. Thus, the value of input variables must be normalized to ten bits wide before running fuzzification process. Figure (5) shows an example of one input membership function defined in the look-up table.  No. of rules Rules Input membership functions Output membership functions Rule no.1 Rule no.2 Rule no.k Offset add. of MFi for input1  Offset add. of MFi for input2  Offset add. of MFi for output  Input 1 Input 2 MF1 MF2 MFx MF1 MF2 MF3 MFy Space (byte) 1 2 2 2 1024 1024 1024 60 60 60 60 0 d) Defuzzifier: After applying the rules, the output of the rules is aggregated using the max operation to get the aggregated membership function. The last operation in controller is the defuzzification process. The defuzzification method which used in this controller is the centroid method.  There are two integration processes that must be computed to can apply the centroid method. This integration processes can be simplified using the numerical method by taking a sample every degree in the output variable delta δ (fin deflection angle). 0 0 255 μ Input 1023 It takes 1024 bytes to record one membership function in knowledge base memory (KBM). Although the value of membership μ changes quite discretely, it is still acceptable, as fuzzy values do not require being too precise. b) Fuzzy Inference Engine: A simple fuzzy inference technique is used. To record rules in KBM, for each rule, the membership of each input is stated and the corresponding required output membership is also stated. For example, say there is a system of 2 inputs and 1 outputs, one of the rules are as below:       If In1 is NM and In2 is PS, then Out1 is PM. In the knowledge base, it is recorded as follow:    [In1NM] [In2PS] [Out1PM] [In1NM] = 2-byte offset address of where the NM membership of input 1. [In2PS] = 2-byte offset address of where the PS membership of input 2. [Out1PM] = 2-byte offset address of where the PM membership of output 1. The offset addresses are used to point to membership functions instead of using indexed number. Thus, there is no further calculation needed to locate the address where the memberships are recorded. This method reduces computation time in fuzzy inference process. Rules for different outputs are defined separately  Rules Table In 1 In 2 out Rule 1 Rule 2 Rule 3 Rule 4 MFx MFy MFz Input1 Membership function x Input2 Membership function y Output Membership function z Crisp input1 Crisp input2 I2C Data Memory min min μ1 μ2 α β max Agg. output array Centroid Defuzzifier Crisp output Crisp output =j ×agg[ j]j=160åagg[ j]j=160åFigure 7, Graphical illustration showing how the fuzzy algorithm works. The  Results The program which downloads the data and makes the serial connection is also developed using JAVA programming language     The fuzzy knowledge base, or known as fuzzy inference system, is developed and tested with both Matlab's Fuzzy logic toolbox and JAVA programming language. The knowledge base stored on the I2C, which used in this implementation, is computed with JAVA and stored on the I2C via the serial connection between the PC and the guidance card. Figure 3 shows block diagram of the proposed system with its PC interface to configure the serial memory I2C. Figure 4 shows the structure of fuzzy system with fuzzification and defuzzification which we used in our implementation. Integrated Fuzzy Guidance System Conclusion Xe -Ze λ The proportional navigation method is a guidance method where the controller will get the LOS angle from missile to target (λ) and the rate of change of this angle (  ) as two inputs, and will produce the elevator angle delta δ (fin deflection angle ) as an output, Figure 1. PB NS Z NB PS -300 300 δ The output of  the controller μδ NM PM λ VS S MS M MB B λ, The first input to the controller   VS S MS M MB B , The second input to the controller   μ μλ Figure 2, The membership function  of the Integrated proportional navigation fuzzy guidance system  illustrated in [1][2]. Figure 1, The proportional Navigation Guidance System Figure 5, Discretization of the Membership function to store it in as Lookup Table  l    l  VS S MS M MB B VS NM PB PB PB PB PB S NS NP NM PM NP NP MS Z NS Z PS NP NP M PS PS PS NM NP NP MB NM PB NB NP Z NM B NS NS Z PM NP NP llldlFigure 6, The memory structure for knowledge base memory  KBM Table 2, The Fuzzy Guidance Rules The fuzzy guidance card has been tested to be fully functional, including interfacing with computer, microcontroller calculations, A/D converter and D/A converter. The fuzzy guidance card implements the fuzzy algorithm efficiently. The proposed controllers in [1][2] could be implemented using a common hardware device, which reduces the cost of the controllers and increase its efficiency.  Table 1, Major implementation methodologies of fuzzy controller 

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