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Intelligent vs Classical Control
Bax Smith
EN9940
Today’s Topics
Distinguishing Between Intelligent and Classical Control
Methods of Classical Control Methods of Intelligent Control Applications for Both Types of Control Discussion
Distinguishing b/w Intelligent and Classical Control
Classical Control
The Mathematicians Approach– Rigidly Modeled System
Software does what it is told– Intelligence comes from the Designer
Intelligent Control
The Lazymans Approach– System not Rigidly Modeled
Software does what it wants to– Intelligence comes from the Software
Shifting Intelligence
Software
Designer
Increasing Intelligence
Designer
SoftwareClassical Control
Intelligent Control
Methods for Classical Control
Open-Loop Control System
Closed-Loop Control System
System Modeling
First-Order System:
Second-Order System:
Classical Control Examples
PID Control Optimal Control Discrete-Event Control Hybrid Control
PID Control
Proportional Control– Pure gain adjustment acting on error signal
Integral Control– Adjust accuracy of the system
Derivative Control– Adjust damping of the system
PID Control
dt
tdeKdeKteKtm D
t
Ip
)()()()(
0
sKs
KKsG D
IpC )(
Optimal Control (LQR)
Optimal Control (LQR)
Inverted Pendulum
Inverted Pendulum Model
Methods for Intelligent Control
Intelligent Control Examples
Fuzzy Logic Control Neural Network Control Genetic Programming Control Support Vector Machines Numerical Learning COMDPs - POMDPs
No System Modeling
Software learns system model
Fuzzy Logic Control
Multi-valued Logic– Rather warm/pretty cold vs hot/cold– Fairly dark/very light vs Black/White
Apply a more human-like way of thinking in the programming of computers
Sets
Set A = {set of young people} = [0,20] Is somebody on his 20th birthday young and
right on the next day not young?
Fuzzy Sets
Fuzzy Example – Inverted Pendulum
Fuzzy Rules
If angle is zero and angular velocity is zero then speed shall be zero
If angle is zero and angular velocity is pos. low then speed shall be pos. low
…
Actual Values
Neural Network Control
Mimic Structure and Function of the Human Nervous System
Biological Neurons
Dendrites– Connects neurons– Modify signals
Synapses– Connects Dendrites
Neuron– Emits a pulse if input
exceeds a threshold– Stores info in weight
patterns
Mathematical Representation of a Neuron
Back-Propagation Neural Network
Training a Neural Network
Analogous to teaching a child to read– Present some letters and assign values to them– Don’t learn first time, must repeat training– Knowledge is stored by the connection weights
Minimize the error of the output using LMS algorithm to modify connection weights
Genetic Programming Control
Output of Genetic Programming is another computer program!
Genetic Programming Steps
Generate a random group of functions and terminals (programs)
– Functions: +, -, *, /, etc…– Terminals: velocity, acceleration, etc…
Execute each program assigning fitness values Create a new population via:
– Mutation– Crossover– Most fit
Which ever program works best is the result
Crossover Operation
Mutation Operation
Applications
In general,– Use Classical Control (Intelligent Control can take long to
train) If problem too complex
– Use Intelligent Control
Discussion
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