# Intelligent vs Classical Control by os8lO1GE

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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

Classical Control
Software                             Designer

Increasing Intelligence

Designer                             Software
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

t
de(t )
m(t )  K p e(t )  K I  e( )d  K D
0
dt

KI
GC ( s )  K p      KDs
s
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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