R Notebook

library(tidyverse)

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.5
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ ggplot2   3.5.2     ✔ tibble    3.3.0
## ✔ lubridate 1.9.4     ✔ tidyr     1.3.1
## ✔ purrr     1.0.4     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(ggplot2)

target <- 100
heading <- 0
last_heading <- 0
error <- target - heading
speed <- 0
accuracy <- 0.1
kp <- 1
results <- data.frame(iteration = numeric(0), error = numeric(0), speed = numeric(0))

iteration <- 1
while (abs(error) > accuracy) {
  heading <- speed * 0.2
  heading <- heading + last_heading
  error <- target - heading
  speed <- kp * error
  last_heading <- heading
  results <- rbind(results, data.frame(iteration = iteration, error = error, speed = speed))
  iteration <- iteration + 1
}

# Plot the results
ggplot(results, aes(x = error, y = iteration)) +
  geom_line(color = "blue") +
  geom_point(color = "red") +


# Save results to CSV
write.csv(results, "pid_results.csv", row.names = FALSE)

This shows how decreases when iterations decrease

ggplot(results, aes(x = speed, y = iteration)) +
  geom_line(color = "blue") +
  geom_point(color = "red") +


# Save results to CSV
write.csv(results, "pid_results.csv", row.names = FALSE)

Notice how the speed and iteration graph are the same as the error and iteration graph

target <- 100
heading <- 0
last_heading <- 0
error <- target - heading
speed <- 0
accuracy <- 0.1
kp <- 8
results <- data.frame(iteration = numeric(0), error = numeric(0), speed = numeric(0), heading = numeric(0))

iteration <- 1
while (abs(error) > accuracy) {
  heading <- speed * 0.2
  heading <- heading + last_heading
  error <- target - heading
  speed <- kp * error
  last_heading <- heading
  results <- rbind(results, data.frame(iteration = iteration, error = error, speed = speed, heading = heading))
  iteration <- iteration + 1
}

# Plot the results
ggplot(results, aes(x = error, y = iteration)) +
  geom_line(color = "blue") +
  geom_point(color = "red") 

  # Save results to CSV
  write.csv(results, "pid_results.csv", row.names = FALSE)

You can see how changing the Kp can completely changes the graph. As you can see it oscillates between 0 in order to accurately get their quickly.

ggplot(results, aes(x = speed, y = iteration)) +
  geom_line(color = "blue") +
  geom_point(color = "red") +
  geom_vline(xintercept = 0, color = "red")

# Save results to CSV
write.csv(results, "pid_results.csv", row.names = FALSE)

The speed graph also oscillates with the error making a similar graph.

ggplot(results, aes(x = iteration, y = heading)) +
  geom_line(color = "blue") +
  geom_point(color = "red") +
  geom_hline(yintercept = 100, color = "red") 

# Save results to CSV
write.csv(results, "pid_results.csv", row.names = FALSE)

The red line represents the target. This graph shows how it over shoots its target then undershoots it allowing it to slowly get their

target <- 100
heading <- 0
last_heading <- 0
error <- target - heading
speed <- 0
accuracy <- 0.1
kp <- 9.7
results <- data.frame(iteration = numeric(0), error = numeric(0), speed = numeric(0), heading = numeric(0))

iteration <- 1
while (abs(error) > accuracy) {
  heading <- speed * 0.2
  heading <- heading + last_heading
  error <- target - heading
  speed <- kp * error
  last_heading <- heading
  results <- rbind(results, data.frame(iteration = iteration, error = error, speed = speed, heading = heading))
  iteration <- iteration + 1
}

# Plot the results
ggplot(results, aes(x = error, y = iteration)) +
  geom_line(color = "blue") +
  geom_point(color = "red") +
  geom_vline(xintercept = 0, color = "red")

target <- 100
heading <- 0
last_heading <- 0
error <- target - heading
speed <- 0
accuracy <- 0.1
kp <- 1
results <- data.frame(iteration = numeric(0), error = numeric(0), speed = numeric(0))

iteration <- 1
while (abs(error) > accuracy) {
  heading <- speed * 0.2
  heading <- heading + last_heading
  error <- target - heading
  speed <- kp * error
  last_heading <- heading
  results <- rbind(results, data.frame(iteration = iteration, error = error, speed = speed))
  iteration <- iteration + 1
}

# Plot the results
ggplot(results, aes(x = error, y = iteration)) +
  geom_polygon(fill = "yellow")+
  geom_line(color = "blue") +
  geom_point(color = "red") 

# Save results to CSV
write.csv(results, "pid_results.csv", row.names = FALSE)

YELLOW PINEAPPLE SLICE :)

ggplot(results, aes(x = -error, y = -iteration)) +
  geom_polygon(fill = "yellow")+
  geom_line(color = "blue") +
  geom_point(color = "red") 

# Save results to CSV
write.csv(results, "pid_results.csv", row.names = FALSE)

):ECILS ELPPAENIP WOLLEY