Biomedical Engineering Reference
In-Depth Information
Fig. 2
PID control
Motor Specifications and Parameter
2hp, 230 v, 8.5 A, 1500 rpm;
R
a
= 2.45 ohm,
J = 0.022 kg-m
2
/rad,
L
a
= 0.035 H,
K
b
= 1.0 v/(rad/sec),
B = 0.5 * 10
-3
N-m/(rad/sec).
ð
H
ð
s
ÞÞ=ð
v
ð
s
ÞÞ ¼
1
:
0
=ð
0
:
00077s
^
20
:
0539s
^
1
þ
1
:
441
Þ:
ð
8
Þ
Tuning of PID Controller Using Ziegler-Nichols Approach
The block diagram shown in Fig.
2
illustrates a closed-loop system with a PID
controller in the direct path, which is the usual connection. The system's output
should follow as closely as possible the reference signal (set point) [
4
,
5
].
The tuning of a PID controller consists of selecting gains K
p
, K
i
, and K
d
so that
performance specifications are satisfied (Table
1
). By employing Ziegler-Nic-
hols's method for PID tuning [
6
,
7
] those gains are obtained through experiments
with the process under control. Controller tuning involves the selection of the best
values of K
p
, K
i
, and K
d
. PID gain values after simulation is given in Table
2
,
(Fig.
3
).
Tuning of PID Controller Using Genetic Algorithm Approach
Genetic algorithm (GA) is a heuristic mimicking the natural evolution process and
is routinely used to generate useful solutions to optimization problems. In this
paper, the genetic algorithm is used to derive the PID controller parameters. In GA
[
6
,
7
], a population of strings called chromosomes, encodes the possible solutions
of an optimization problem and evolves for a better solution by process of
reproduction (Fig.
4
).
The process of evolution starts from a population of randomly generated
individuals. Optimization is achieved in generations where in each generation, the
fitness function evaluates each individual in the population and multiple individ-
uals are selected stochastically based on their fitness. These selected individuals
are modified to form a new population. The algorithm terminates when either a
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