代做ENG5009 Advanced Control 5代做留学生Matlab程序

ENG5009 Advanced Control 5

Laboratory Document

Overview

This lab exercise introduces the Fuzzy Logic Toolbox within MATLAB, introduces the robot model and simulation to be used in the assignment and allows you to create and test a simple Neural Network.

You will be required to work your way through the examples provided and keep track of your progress using the answer grid provided on Moodle. This answer grid is to be included as an appendix in the final assignment report and is worth 20% of the assignment mark.

Laboratory 1

Introduction

The main purpose of this first lab is to provide you with an introduction to the Fuzzy Logic Toolbox within MATLAB. This is achieved through:

•    The MATLAB Fuzzy Logic Toolbox Tutorial,

•    Implementing the Fuzzy Logic system you developed into the command line,

•    Create a second Fuzzy Logic system.

Following this process will give you the necessary skills to create a Fuzzy Logic system.

1. Introduction to Fuzzy Logic Toolbox

The first stage in this tutorial is to learn more about the fuzzy logic toolbox in MATLAB.

The easiest way of doing this is to follow the Mathworks Tutorial, which can be found here:

https://uk.mathworks.com/help/fuzzy/building-systems-with-fuzzy-logic-toolbox-software.html

This tutorial introduces you to the basics of using the toolbox. The skills you learn developing this system will support you with the main assignment – if you choose to use a Fuzzy logic controller.

Using the standard settings, provide the tipping level for the following input values in Table 1.

Table 1: Service and Food Values

Service	Food
5	5
7	8
10	2

Note these values in the Laboratory answer sheet.

Change the defuzzificiation method to the Mean of Maximum and using the values in Table 1 note the values in the Laboratory answer sheet. To change the defuzzification method return to the front page of the Fuzzy Logic toolbox and change via the drop down menu on the right hand side of the screen.

Change the defuzzificiation method to the Bisector and using the values in Table 1 note the values in the Laboratory answer sheet.

2. Integrating the Fuzzy system into the command line

To use the Fuzzy system developed you need to be able to call it from within a MATLAB script.  Ensure you have reset the tipper FIS back to Centroid defuzzification before saving your values.

To use tipper in the command line, do this:

tipper = readfis('tipper'); % create a variable name and assign your fis file to it

You then evaluate the inputs using the following:

tip = evalfis(tipper, [5 5]); % @outputs =

evalfis(@nameofFIS,@inputs)

Further information on the integration of the FIS into the command line, or script file, can be found here:https://uk.mathworks.com/help/fuzzy/evalfis.html. Note the tipping value within the

Laboratory Answer Sheet for the inputs in Table 2, using the command line method.

Table 2: Service and Food Values

Service	Food
6	3
2	2
10	7

3. Further Fuzzy Logic Example

Create a fuzzy logic system for an AC system based on the following parameters.

• Input range: 5 to 32

• Output range: -20 to 20

• Defuzzification method: Centroid

The input fuzzy sets are defined in Table 3 and the output fuzzy sets are defined in Table 4 and the rules are supplied in Table 5.

Table 3: Input Fuzzy Sets

Fuzzy Set	Type	Parameters
Too Cold	Trapezoid	[4 5 12 14]
Cold	Trapezoid	[12 14 16 18]
Comfortable	Trapezoid	[16 18 22 24]
Warm	Trapezoid	[22 24 26 28]
Hot	Trapezoid	[26 28 32 35]

Table 4: Output Fuzzy Sets

Fuzzy Set	Type	Parameters
High power cool	Trapezoid	[-22 -20 -12 -8]
Low power cool	Trapezoid	[-12 -8 -6 -2]
off	Trapezoid	[-6 -2 2 6]
Low power warm	Trapezoid	[2 6 8 12]
High power warm	Trapezoid	[8 12 20 22]

Table 5: Rules

If	Then
Too Cold	High power warm
Cold	Low power warm
Comfortable	off
Warm	Low power cool
Hot	High power cool

Using the system you have developed, note the values in the Laboratory Answer Sheet based on the inputs in Table 6.

Table 6: Inputs for the AC system

Input Temperature, °C

9

13.1

26.5

20

This completes Laboratory 1.

Laboratory 2

For the assignment you will be using a model of a four wheeled robot. This model will be available from the ENG5009 Moodle page under the Lab and Assignment section.

This laboratory provides a demonstration of how to use the robot model and simulation that is to be used and how to integrate a controller within it.

The model is called:  DynamicalModel.m and is fully described in

ENG5009_ModelDescription_2024.

In addition, there is a simple simulation file, RunModel.m, that can be used to run the model.

Further files that maybe of interest are:

drawrobot.m: this file draws a representation of the robot on to a given figure.

WallGeneration.m: supports the development of obstacles.

Sensor.m: A function that provides a forward-looking sensor that returns the nearest objects on the right and left handside of the robot.

1. Basic Operation

The first stage is to learn how to use the model and the simulation file.

Apply the voltages in Table 7 to voltage_left and voltage_right.

Table 7: Values to be applied to left and right Motors

Voltage Left, V	Voltage Right, V	Expected Action
6	6	Forward motion
-6	6	Rotation Left
6	-6	Rotation Right

For each of these insert the output plot figure 1 into the Laboratory Answer Sheet.

2. Basic Manoeuvre

You are required to move the robot:

•    a short way forward,

•    turn left,

•    a short way forward,

•    turn right manoeuvre,

•    a short way forward.

The output should be like the path shown in Figure 1 below. When completed insert output Figure 2 into the Laboratory Answer Sheet.

Figure 1: Path of robot

To achieve this, you need to create a simple time-based controller that alters the voltage_left and voltage_right values as and when required. For example, you could use several if statements to alter the variables at relevant time steps. For example, the code below will alter the voltage_left and voltage_right after 1s.

if (time(timeStep) >= 1)

voltage_left = -6;

voltage_right = 6;

end

3.    Running the Model with a Fuzzy Controller

Develop a fuzzy controller that allows the robot to avoid obstacles. This should be based on the example developed during Lectures 5 and 6 of the course. You should use the Matlab Fuzzy Logic Toolbox to do this.

For this you will be required to add in obstacle detection. To do this add the following line:

[sensorOutput] = Sensor(robot_x, robot_y, headingAngle, obstacleMatrix)

You will also be required to draw walls on the plot. To do this, set drawWalls to 1.

Insert figure 2 into the Laboratory Answer Sheet.

4.    Running the Model with a Neural Controller

In lectures 7 and 8 we discussed avery simple Neural network controller that should be able to drive the robot forward while avoiding obstacles.

The first stage is to create a neural network controller based on Figure 2 below and using the values

calculated in the lecture for each of the weights. Insert the MATLAB output Figure 2 into the Laboratory Answer Sheet. Comment on the behaviour of the system.

Given the output provided, make a change to the system to improve the output. Detail the change you made in the Laboratory Answer Sheet and provide the MATLAB output Figure 2.

Figure 2: Simple Neural Network

To create the Neural network it is advised to use code similar to that below:

sumN1 = (weight(1)*sensorOutput(1) + weight(3)*sensorOutput(2));

sumN2 = (weight(2)*sensorOutput(1) + weight(4)*sensorOutput(2));

if (sumN1 > weight(5))

voltage_left = 3;

else

voltage_left = 0;

end

if (sumN2 > weight(6))

voltage_right = 3;

else

voltage_right = 0;

end