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