5. lecture fuzzy systems...• the resulting controller can be the described link between inputs and...
TRANSCRIPT
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120 WS 17/18 Georg Frey
5. Structure of the lecture
1. Introduction Soft Control: Definition and delimitation, basic of 'intelligent'
systems
2. Knowledge representation and knowledge engineering (symbolic AI)
Application: Expert Systems
3. FuzzySystems: dealing with fuzzy knowledge
Application: Fuzzy control
1. Fuzzy-Quantity
2. Fuzzy-Relations, Fuzzy-Inference
3. Fuzzy-System, Fuzzy-Control
4. Connective Systems: Neural Networks
Application: Identification and neural control
5. Genetic algorithms, Simulated annealing, Differential evolution
Application: Optimization
6. Summary & References
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121 WS 17/18 Georg Frey
Contents of the Lecture 5.
1. Fuzzy Systems
1. Fuzzification
2. Defuzzyfying
3. Operation of the overall system
2. Fuzzy Control
1. Rules
2. Control
3. Fuzzy Control
4. Design Process
3. Summary
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122 WS 17/18 Georg Frey
Fuzzy System
• engl.: Fuzzy system
System, that used linguistic rules and with the help of the partial blocks
fuzzification, inference and defuzzyfying, mapped the numeric input variables
to numeric output variables
(VDI/VDE 3550)
Fuzzification Inference Defuzzyfication
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123 WS 17/18 Georg Frey
Fuzzification
• engl.: fuzzification
Conversion of a numeric size in a degree of membership to linguistic
expressions of a linguistic size
(VDI/VDE 3550)
Fuzzification Inference Defuzzyfication
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124 WS 17/18 Georg Frey
Fuzzification
• Transition from a sharp signal value X to a fuzzy signal value X*
• Assignment of the degrees of membership for all linguistic terms of the
corresponding linguistic variable
• For n linguistic terms, there is a n-tuples of degrees of membership
In the fuzzification, a sharp signal is not transferred in a fuzzy-quantity, but in a
vector of sharp degrees of memberships of fuzzy-quantities
1
0
μ
T/°C
50 100 0
very low low very high high medium
T = 58°C T * = (0 0 0.5 0.15 0)
0.5
0.15
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125 WS 17/18 Georg Frey
Example for Fuzzification
• T1 = 28 °C T1*= (0 0,8 0 0 0) The temperature T1 = 28 °C is low
• T2 = 58°C T2*= (0 0 0,5 0,15 0) The temperature T2 = 58 °C is between medium and high, more medium
• T3 = 95°C T3*= (0 0 0 0 1) The temperature T3 = 95 °C is very high
0
μ
T/°C
50 100 0
very low low very high high medium
T2 = 58°C
0.5
0.15
1
T3 = 95°C T1 = 28°C
0.8
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126 WS 17/18 Georg Frey
Defuzzyfication
• Engl.: defuzzyfication
Conversion of a fuzzy-quantity in a numeric output value (e.g. in a control
variable).
(VDI/VDE 3550)
Fuzzification Inference Defuzzyfication
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127 WS 17/18 Georg Frey
Thoughts about Defuzzyfication
• The output fuzzy-quantity represents a activation function
• Question: What exact value best describes the result of the inference?
• Basic Ideas:
Maxima of the function:
Value, that is the maximum in the fuzzy quantity
(Problem: Definition by multiple maxima)
"Middle" of the area
Center or median of the area under the curve
(Problem: complex calculation)
• Methods
Maximum-Defuzzyfication
gravity method
Area median method
• First an example
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128 WS 17/18 Georg Frey
Example: linguistic variables
1
0
μ
T/°C
50 100 0
very low low very high high medium
1
0
μ
W/%
50 100 0
very low low very high high medium
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129 WS 17/18 Georg Frey
Example: rule base and factum
Rule base
• R1: IF T = very low THEN W = very high
• R2: IF T = low THEN W = high
• R3: IF T = medium THEN W = medium
• R4: IF T = high THEN W = low
• R5: IF T = very high THEN W = very low
• Input Variable: T = 15 °C
1
0
μ
T/°C
50 100 0
very low low very high high medium
1
0
μ
W/%
50 100 0
very low low very high high medium
1
0
μ
W/%
50 100 0
very low low very high high medium
0.75
0.25
Fuzzification: T * = (0.75 0.25 0 0 0)
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130 WS 17/18 Georg Frey
Example: Accumulation (MAX)
1
0
μ
W/%
50 100 0
Very Low Low Very High High Medium
1
0
μ
W/%
50 100 0
Very Low Low Very High High Medium
1
0
μ
W/%
50 100 0
Very Low Low Very High High Medium
0.75
0.25
μ
W/%
50 100
Very High High 1
0
0.75
0.25
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131 WS 17/18 Georg Frey
Maximum-Defuzzyfication
• Where is the maximum ?
Mean-of-Maxima (mean value of the
Maxima)
Smallest-of-Maxima (first Maximum)
Largest of maxima (last peak)
μ
W/%
50 100
Very High High 1
0
0.75
0.25
μ
W/%
50 100
Very High High 1
0
0.75
0.25
μ
W/%
50 100
Very High High 1
0
0.75
0.25
μ
W/%
50 100
Very High High 1
0
0.75
0.25
MOM: YD = 93.75 SOM: YD = 87.5 LOM: YD = 100
Evaluation
Simple Calculation
Only rules with a maximum degree of fulfillment go to the result (usually one)
The degree of fulfillment of the rule is not taken into account (for MOM and
triangular-structured ZGF, others partially).
Range boundaries are not always possible (depends on ZGF)
Discontinuous output values
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132 WS 17/18 Georg Frey
Gravity method
• General
= Center of
gravity (COG)
μ
W/%
50 100
Very High High 1
0
0.75
0.25
Evaluation
All the rules are taken into account
Continuous output values
Levels of fulfillment are taken into account
Complex calculation
Range boundaries are not possible ( Advanced gravity method)
dyy
dyyy
yD
COG: YD =
• Simplified
or for Singletons
= Center of singletons
(COS), centroide
μ
W/%
50 100
Very High High 1
0
0.75
0.25
n
i
i
n
i
ii
D
y
yy
y
1
1
COS: YD = 85
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133 WS 17/18 Georg Frey
Area median method
• = Center of
area (COA)
μ
W/%
50 100
Very High High 1
0
0.75
0.25
Evaluation (almost like in gravity method)
All the rules are taken into account
Continuous output values
Levels of fulfillment are taken into account
Complex calculation (more complex than in gravity method)
Range boundaries are not possible
For singletons in output Fuzzy-Quantities unsuitable
D
D
y
y
D dyydyymity
COA: YD =
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134 WS 17/18 Georg Frey
Operation of a Fuzzy-System
1. Fuzzification Determination of the degrees of membership of the sharp input variables to the Input-Fuzzy-Quantities
2. Aggregation (premise analysis) Determination of the levels of fulfillment of the single rule premises (Determination of active rules)
3. Activation Determination of the single Output-Fuzzy-Quantities (for each rule)
4. Accumulation Overlap of the single Output-Fuzzy-Quantities to an overall Output-Fuzzy-Quantity (function of attractiveness)
5. Defuzzyfication Determination of the sharp output values from the function of attractiveness
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135 WS 17/18 Georg Frey
Application: Fuzzy control
• Basics
Properties of a scheme
Properties of a control
Comparison of control (close loop and open loop)
• Fuzzy control
Application of a Fuzzy-System to control
• Design Methodology
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136 WS 17/18 Georg Frey
Block diagram of a control
Process variable
route Actuators
Sensors
Control element
Control output
reference variable w
-
Feedback variable
Comparing
element
Algorithm
Disturbances (incl. EMC, environment, ... )
Disturbances (incl. EMC, environment, ... )
Control
Characteristics
• Sphere of influence, where variables continuously retroact to themself
• Continuous values
• Standardized task: disturbance correction, tuning the reference variable
Example: Balancing of an inverted pendulum
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137 WS 17/18 Georg Frey
Control
Block Diagram
Output variables Control Part
Control Signals Input Variables
route
Actuator feedback
Actuators
Sensors Feedback variables
Disturbances (incl. EMF, environment, ...)
Disturbances (incl. EMF, environment, ...)
Algorithms
Characteristics
• Variables in the loop do NOTcontinously retroact themselves
• Binary values
• No standardized task
Example: Positioning of an inverted pendulum
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138 WS 17/18 Georg Frey
„Always restart, "not standardized bar: usually extensive
Rules can be applied“
„Always same“, standardized: „Controlled variable adjust the reference input“
Specification
Always several loops/mehrschleifig, i.e. several hundred sensors and actuators Complexity
>95% of control loops are one-loop/einschleifig (1 Sensor, 1 Actuator)
Number of signals
Variables in loop effects other variables
Variables in loop retroact themselves
Feedback variables
discrete continous Variables
Boolean Algebra, Automata, Petri Nets
Differential equations
Mathematics
Amplifier loop
Disturbances
Feedback system
No amplifier loop Amplification loop is defined Stability problem
only known in advance and trackable disturbances can be corrected
unknown disturbances can be corrected
Asynchronous binary feedback variables( Events)
Permanently synchronised closed loop
Control Automation
Comparison of Automation and Control
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139 WS 17/18 Georg Frey
Fuzzy-Control
• Fuzzy controller (fuzzy controller) can be used for regulatory as well as for control tasks. Often combinations of the two are found.
• The resulting controller can be the described link between inputs and outputs
Characterstics curve
In general not-linear
Application of a fuzzy system for the control and automation
(Control)
Fuzzy controllers are not novel controller types. They belong to the class of nonlinear curves or
Characterstics diagram controller.
However, there are new design methods and the interpretation of results.
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140 WS 17/18 Georg Frey
m
1
negative-up positive-up
positive-down negative-down
middle-up
180 120 90 0 -90 -120 -180
0
negative- up
middle-
up
negative-
up
positive-
down
positive-
up
-30 30
Fuzzy Control in the example of inverted pendulum
Regel 1:
IF Pendulum angle
positiv-down AND
Angular acceleration negative
AND Wagon position
middle
THEN acceleration should be
negative
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142 WS 17/18 Georg Frey
Static characteristics of fuzzy controllers
Control base:
R1: IF e = NG THEN u = NG
R2: IF e = NU THEN u = NU
R3: IF e = PG DANN u = PG
• Examples with mixed Degree of overlap Input fuzzy quantities
• Max-Min-Inference
• COS-Defuzzification
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143 WS 17/18 Georg Frey
Control and Variables characteristics
Control variale y Variable u
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145 WS 17/18 Georg Frey
Design parameters of a fuzzy controller
Fuzzification Inference Defuzzification
Control base
y
ZGF ZGF Input variables
Output variables
x
Problem oriented design parameters
Method oriented design parameters
Defuzzification methods
Inference- methods
(see 4. VL)
•Premise evaluation: Operators for AND and OR
(t-Norm und s-Norm) • Activation:
Operator for the closing of the Premise
Conclusion (t-Norm) • Accumulation:
Operator for the summary of
single control output (s-Norm)
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146 WS 17/18 Georg Frey
Design process of a fuzzy controller
Design process
1. Defining the parameters method
2. Defining the parameters problem
1. Define the linguistic variables and the number of terms
2. Defining the membership functions
3. Defining the rules (expertise)
3. Simulation using a model (if possible)
4. Implementation
Depending on the result of 3 (or 4): Optimization through interventions in 2 (or 1)
• Note: Even method parameters usually have not much influence on the behaviour
• method parameters will be partially used by the design tool set
Design process = method for determining the method and parameters of the problem
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147 WS 17/18 Georg Frey
Dynamic fuzzy controller
• Fuzzy controllers are initially static
• Dynamic behaviour can only be produced by external components are
Post-processing of output variables(integration)
pre-processing of input variables (Derivation)
• Example: Fuzzy-PID-Controller
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148 WS 17/18 Georg Frey
Summary and learning for 5th Lecture
To know the concept of fuzzy system
Fuzzification
Apply and describe the methods of De-fuzzification
Functionality of Fuzzy sytems
Concept of fuzzy controller with respect to with control and regulation
Design process of fuzzy controller