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In academic PMSM motors are quite oblivion,
DELIVERS.

PID
let's discuss P
P reduces the rise time,
P needs error.
Hence it cannot cancel out the error.
After a time interval, steady state error is most common.
In MANY Control system , of B.Tech and B.E.

The next topic in focus shall be PID Control

In this post, let's talk about the last topic of , the topic name is .
As a programming topic for and courses, this topic is important, as many depends a lot on the .
means exchange or flip of genes in an individual offspring in order to maintain population .
Thus, speaking diversity is the reason the population keeps on generating new and thus genetic algorithm keeps running.
When diversity is completely finished, it means no further offspring production and hence no growth in population.
// here comes the pseudo code.
POPULATION {1,2,3,0,1,3,1}
// main program
int main() {
// individual 1
int ind1 = POPULATION[0];
// convert to binary
int bin;
bin = dec2bin(ind1);
// mutation
int ind_mut1;
ind_mut1 = bin;
int temp;
temp = bin[0];
int temp1;
temp1 = bin[end];
ind_mut1[0] = temp1;
ind_mut1[end]= temp;
// mutation completed
return 0;
}
This pseudo code is just to make an understanding how a mutation is done, as this process is a repeated in loop, it shall be optimized to provide the least possible usage of memory.
Thanks.

In this post of , the topic is related to offspring and mutation.
Generally speaking offspring comes right after ,
In step , the algorithm generally performs a interchange of between the parents.
Hence how it is done, let's have a closer look on it by a pseudo code.
static int genetic_offspring(){

// parent 1
int parent[10] = {1,0,1,0,1,0,1,1,1,1};
// parent2
int parent2[10] ={1,1,1,0,0,0,1,1,0,1};
// mutation.
// perform interchange of position of parent1 and parent2 at 3rd,4rth, 7th places.
INTERCHANGE1 3
INTERCHANGE2 5
INTERCHANGE3 7
// define a temporary variable
int temp;
// use a loop to interchange the position
int ARRAY[3] = {INTERCHANGE1, INTERCHANGE2, INTERCHANGE3};
int i,index;
for(i =0; i

In genetic algorithm,
The next step is to do Crossover of the two best fit individuals obtained after selection process, selection technique used or explained in last post was related to .
Here crossover can be obtained through different methods which are already well established, like masking, random position change and so on.
Today we see the pseudo code for masking which just makes the life of a engineer far easy in terms of understanding.

Let's choose a mask:
MASK 0x010010010;
// this is a eight bit mask and at bit position 6 and bit position 3, 1 1 is present.
let's define the Crossover function

int crossover() {
int Individual1 = 255;
int Individual2 = 127;

// since these two are just numerics but if they are converted to bits they are all 1.
// let's apply mask.
Individual_mask1 = Individual1 & MASK;
Individual_mask2 = Individual2 & MASK;

return 0;
}
// Hence obtained the crossover using a simple technique.
Hope this helps for

In genetic algorithm
After fitness score, next step is to do selection.
Let's consider Roulette wheel selection process.

In Roulette wheel, the circle area is divided in parts.
So let's consider a linear array instead of a circle to relate it with a circle.
Population is as follows
char Popn[11];
Popn[11] = { A, B, C, D, E, F, G, H, I, J};
Now the fitness score of the Popn is a numeric value:
int Fitscore[10] = {1, 1, 1, 2, 0, 0, 0, 3, 0, 1, 1};
here let's see the fitness sum
int fitsum = 10; // summed up the fitscore manually
next lets' review the area of roulette wheel in a linear array
char RW_Array[11] ={'A', 'B' , 'C', 'D','D', 'H','H','H','I','J'};
We see that 'H' has the maximum area and 'E','F','G' does not have any area as there fitness score is 0.
So if we play the wheel then lets see how and what Population is getting selected from the Roulette wheel array.
To do this generate a random number between index 0 to 9 for two trials to fetch two parents.
int i, r, Parent[2];
TRIAL 2;
for (i = 0; i < TRIAL; i++) {
r = random();
Parent[i] = r;
}
the two random numbers in parent will be index for Population Array.
// fetch the two parent
char Parent1, Parent2;
int index1, index2;
index1 = Parent[0];
index2 = Parent[1];
Parent1 = Popn[index1];
Parent2 = Popn[index2];
Hence the Roulette wheel has provided the two fit parents for reproduction stage.
Hope this post helps!!!