Chapter Three: Sacred Paths among Indigenous Religions
Introduction:
Examples
Sacred in various forms
Myths
the Tribe
Hunting and Gathering, Fishing and Herding
(Pastoralists)
Identity through Sacred Stories: Fit,
excommunication, origins, not worried a
Chapter Two: Beginnings of the
Religious Adventure
The world religions of today, highly developed and
encompassing large numbers, are relatively
modern phenomena, all of them having reached
their classical form within the past three thousand
years. (27)
T
Lab 14 Excel Formulas & Tableau Maps
The Lab14 Data file contains statistics for the NBAs 2001-02 season. Rows 2 through 30 contain the
following information for each NBA team:
Col A contains a code number assigned by the statisticians to each NBA team.
C
Understanding the Religions
of the World
Religions, to the surprise of some, have not faded
from importance in our world today.
Secularization Theory
Atheistic Theory: Tylor or Fraser(supernatural
beings, magic: ritual tries to controls gods; or
prayer: t
Lab 16 - Prediction with Watson Analytics
1. Login to Watson Analytics using the Firefox browser. Firefox seems to work best for all
Watson functions. If at any time you receive a pop-up message about a slow running script,
just press Continue.
watsonanal
Solution to problem 2.1-8(e)
MATLAB program:
%ECE457 Homework #1 MATLAB Problem 2.1-8 (e)
%This program generates and plots the signal y(t)=10*sin(5*t).*cos(10*t)
%and also evaluates its signal power.
clear all;
clc;
%y(t) can be written into the sum of t
1.7
Tuning of PID Controllers
Ziegler Nicholas Rules
Tuning is the process of selecting the controller parameters to meet given performance specifications.
Ziegler and Nicholas suggested rules for tuning PID controllers based on experimental step response
2.5
State Space Representation
State space method is based on the description of system equations in terms of n first-order difference
equations, which may be combined into a first-order vector matrix difference equation. The use of the
vector-matrix nota
2.4
Stability Analysis
2.4.1
Stability in the z-plane
Just as the transient analysis of continuous systems may be undertaken in the s-plane , stability and transient analysis
on discrete systems may be conducted in the z-plane It is possible to map from t
1.4
Derivative Control Action
The control signal u(t)is given by
d
u(t) = kd e(t)
dt
where kd is the derivative gain
when the slope of e(t) is large at the current time , the magnitude of u(t)will increase i.e the derivative
control law provides a large c
Integral Control
For integral control, the control signal u(t) is given by
u(t) = kie(t)dt
(1)
t
0
where ki is the integral gain constant
converting equation 1 to frequency domain
ki
U (s) = E (s)
s
Integral Control of type 0, first order system
Consider
Proportional Control of a type 1, second order system
Consider the system shown below
Figure 5: Proportional Control of type 1, second order system
The open loop transfer function is given by
kp
G(s) =
ks2 + cs
The closed loop transfer function is given b
Pulse Transfer Function
Pulse transfer function relates the Z transform of the output at the sampling instants to that of the sampled input
X0 (z) G(z) = U (z)
The ratio of the output X0 (z) and input U (z) is called the pulse transfer function of the dis
2.2.2
Inverse Z Transform
The inverse Z transform of X (z) yields the corresponding time sequence x(t)
The notation for the inverse Z transform is z1
1. Direct division method
We obtain the inverse Z-transform by expanding X (z) into an infinite power ser
Digital Control
Lecture Notes by A. M. Muhia
1
D
IGITAL
CONTROL
SYSTEMS
2.1
Sampled Data Systems
A sampled data system operates on discrete-time rather than continuous-time signals. A digital computer
is used as the controller in such a system. A D/A conv
2.8
Digital Controller Design
2.8.1
Controllability and Observability
Controllability is concerned with the problem of whether it is possible to steer a system from a given initial
state to an arbitrary state. A system is said to be controllable if it is
PID Controllers
Lecture Notes by A. M. Muhia
1
Process Control
1.1
Introduction
In a control system, the variable to be controlled is called the Process Variable or PV. In industrial process
control, the PV is measured by an instrument in the field and ac
Errata List For
Nonlinear Systems Third Edition
Updated on August 12, 2014
Please e-mail error reports to [email protected]
Preface
1. Page xiv, Line 5: Change books to book
Chapter 1
1. Page 10, Line 3: Change Coulombs plus to Coulomb plus
2. Page 24, Secon
Chapter
14
Forecasting
DISCUSSION QUESTIONS
1. a.
b.
c.
d.
2.
There is no apparent trend in the data. The nave forecast method, exponential
smoothing or the simple moving average would be appropriate for estimating the
average.
The primary external factor
PROBLEMS
1.
Boehring University
a. Value of output:
students
credit-hours $200 tuition $100 state support
75
3
$67,500 class
class
student
credit-hours
Value of input: labor + material + overhead
$25
$6500
75 students $30, 000
student
$38,375 class
Cements.
Preface xiii
PART 1 Creating Value through
Operations Management
We, rams r- a
{if Wg}EE[EII r 1
Scholastic and Harry Potter 1
Operations and Supply Chain Management across
the Organization 2
Historical Evolution of Operations and
Supply Ch
Nonlinear Systems and Control Lecture # 36 Tracking Equilibrium-to-Equilibrium Transition
p. 1/1
= f0 (, ) i = i+1 , 1 i - 1 -1 = L h(x) + Lg L h(x) u
f f fb (,) gb (,)
y = 1
Equilibrium point:
0 = f0 ( ) , 0 = i+1 , 1 i - 1 0 = fb ( ) + gb ( ) , , u y
Nonlinear Systems and Control Lecture # 30 Stabilization Control Lyapunov Functions
p. 1/1
x = f (x) + g(x)u,
f (0) = 0, x Rn , u R
Suppose there is a continuous stabilizing state feedback control u = (x) such that the origin of
x = f (x) + g(x)(x)
is
Nonlinear Systems and Control Lecture # 38 Observers Exact Observers
p. 1/1
Observer with Linear Error Dynamics
Observer Form:
x = Ax + (y, u), y = Cx
where (A, C ) is observable, x Rn , u Rm , y Rp From Lecture # 24: An n-dimensional SO system
x = f (x)
Nonlinear Systems and Control Lecture # 41 Integral Control
p. 1/1
x = f (x, u, w ) y = h(x, w ) ym = hm (x, w ) x Rn state, u Rp control input y Rp controlled output, ym Rm measured output w Rl unknown constant parameters and disturbances
Goal:
y (t) r