diff --git a/main.py b/main.py
new file mode 100644
index 00000000..bea2b185
--- /dev/null
+++ b/main.py
@@ -0,0 +1,13 @@
+from uuv_mission.mission_class import Mission
+
+def main():
+    # Adjust path if your folder name is different
+    mission = Mission.from_csv("data/mission.csv")
+    print(mission)
+    print("\nSample Data:")
+    print("Reference:", mission.reference.head())
+    print("Cave Height:", mission.cave_height.head())
+    print("Cave Depth:", mission.cave_depth.head())
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/notebooks/demo.ipynb b/notebooks/demo.ipynb
index aa0c5cee..b54b716e 100644
--- a/notebooks/demo.ipynb
+++ b/notebooks/demo.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -32,23 +32,379 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "uuv_mission.dynamic\n",
+      "No __file__ attribute\n",
+      "ClosedLoop instance created\n",
+      "Mission length = 100\n",
+      "Calling simulate_with_random_disturbances...\n",
+      "Starting simulation...\n",
+      "Before loop\n",
+      "[t=000] Ref=0.00, Obs=0.00, Error=0.00, Ctrl=0.00, VelY=0.00, PosY=0.00\n",
+      "Before update: pos_y=0.0000, vel_y=0.0000\n",
+      "After update: pos_y=0.0000, vel_y=0.8626, action=0.0000, disturbance=0.8626\n",
+      "[t=001] Ref=2.92, Obs=0.00, Error=2.92, Ctrl=2.34, VelY=0.86, PosY=0.00\n",
+      "Before update: pos_y=0.0000, vel_y=0.8626\n",
+      "After update: pos_y=0.8626, vel_y=2.4453, action=2.3391, disturbance=-0.6702\n",
+      "[t=002] Ref=5.51, Obs=0.86, Error=4.65, Ctrl=1.59, VelY=2.45, PosY=0.86\n",
+      "Before update: pos_y=0.8626, vel_y=2.4453\n",
+      "After update: pos_y=3.3079, vel_y=3.4846, action=1.5869, disturbance=-0.3031\n",
+      "[t=003] Ref=7.49, Obs=3.31, Error=4.18, Ctrl=0.01, VelY=3.48, PosY=3.31\n",
+      "Before update: pos_y=3.3079, vel_y=3.4846\n",
+      "After update: pos_y=6.7925, vel_y=2.6880, action=0.0065, disturbance=-0.4546\n",
+      "[t=004] Ref=8.65, Obs=6.79, Error=1.86, Ctrl=-1.41, VelY=2.69, PosY=6.79\n",
+      "Before update: pos_y=6.7925, vel_y=2.6880\n",
+      "After update: pos_y=9.4806, vel_y=0.7950, action=-1.4120, disturbance=-0.2122\n",
+      "[t=005] Ref=8.94, Obs=9.48, Error=-0.54, Ctrl=-1.56, VelY=0.80, PosY=9.48\n",
+      "Before update: pos_y=9.4806, vel_y=0.7950\n",
+      "After update: pos_y=10.2756, vel_y=-0.9511, action=-1.5581, disturbance=-0.1085\n",
+      "[t=006] Ref=8.40, Obs=10.28, Error=-1.88, Ctrl=-0.83, VelY=-0.95, PosY=10.28\n",
+      "Before update: pos_y=10.2756, vel_y=-0.9511\n",
+      "After update: pos_y=9.3244, vel_y=-1.2734, action=-0.8334, disturbance=0.4160\n",
+      "[t=007] Ref=7.21, Obs=9.32, Error=-2.11, Ctrl=-0.06, VelY=-1.27, PosY=9.32\n",
+      "Before update: pos_y=9.3244, vel_y=-1.2734\n",
+      "After update: pos_y=8.0510, vel_y=-1.2409, action=-0.0586, disturbance=-0.0362\n",
+      "[t=008] Ref=5.64, Obs=8.05, Error=-2.41, Ctrl=-0.16, VelY=-1.24, PosY=8.05\n",
+      "Before update: pos_y=8.0510, vel_y=-1.2409\n",
+      "After update: pos_y=6.8101, vel_y=-1.0388, action=-0.1600, disturbance=0.2380\n",
+      "[t=009] Ref=4.01, Obs=6.81, Error=-2.80, Ctrl=-0.30, VelY=-1.04, PosY=6.81\n",
+      "Before update: pos_y=6.8101, vel_y=-1.0388\n",
+      "After update: pos_y=5.7713, vel_y=-1.2811, action=-0.3036, disturbance=-0.0426\n",
+      "[t=010] Ref=2.61, Obs=5.77, Error=-3.16, Ctrl=-0.35, VelY=-1.28, PosY=5.77\n",
+      "Before update: pos_y=5.7713, vel_y=-1.2811\n",
+      "After update: pos_y=4.4902, vel_y=-1.3280, action=-0.3470, disturbance=0.1719\n",
+      "[t=011] Ref=1.72, Obs=4.49, Error=-2.77, Ctrl=0.17, VelY=-1.33, PosY=4.49\n",
+      "Before update: pos_y=4.4902, vel_y=-1.3280\n",
+      "After update: pos_y=3.1623, vel_y=0.3004, action=0.1685, disturbance=1.3271\n",
+      "[t=012] Ref=1.50, Obs=3.16, Error=-1.66, Ctrl=0.71, VelY=0.30, PosY=3.16\n",
+      "Before update: pos_y=3.1623, vel_y=0.3004\n",
+      "After update: pos_y=3.4626, vel_y=0.8389, action=0.7084, disturbance=-0.1398\n",
+      "[t=013] Ref=2.01, Obs=3.46, Error=-1.45, Ctrl=0.01, VelY=0.84, PosY=3.46\n",
+      "Before update: pos_y=3.4626, vel_y=0.8389\n",
+      "After update: pos_y=4.3015, vel_y=0.7737, action=0.0088, disturbance=0.0099\n",
+      "[t=014] Ref=3.18, Obs=4.30, Error=-1.13, Ctrl=0.08, VelY=0.77, PosY=4.30\n",
+      "Before update: pos_y=4.3015, vel_y=0.7737\n",
+      "After update: pos_y=5.0753, vel_y=0.3175, action=0.0842, disturbance=-0.4630\n",
+      "[t=015] Ref=4.82, Obs=5.08, Error=-0.26, Ctrl=0.51, VelY=0.32, PosY=5.08\n",
+      "Before update: pos_y=5.0753, vel_y=0.3175\n",
+      "After update: pos_y=5.3927, vel_y=0.1568, action=0.5106, disturbance=-0.6395\n",
+      "[t=016] Ref=6.67, Obs=5.39, Error=1.28, Ctrl=1.09, VelY=0.16, PosY=5.39\n",
+      "Before update: pos_y=5.3927, vel_y=0.1568\n",
+      "After update: pos_y=5.5495, vel_y=1.6227, action=1.0859, disturbance=0.3957\n",
+      "[t=017] Ref=8.44, Obs=5.55, Error=2.89, Ctrl=1.25, VelY=1.62, PosY=5.55\n",
+      "Before update: pos_y=5.5495, vel_y=1.6227\n",
+      "After update: pos_y=7.1722, vel_y=2.5359, action=1.2513, disturbance=-0.1758\n",
+      "[t=018] Ref=9.85, Obs=7.17, Error=2.68, Ctrl=-0.08, VelY=2.54, PosY=7.17\n",
+      "Before update: pos_y=7.1722, vel_y=2.5359\n",
+      "After update: pos_y=9.7081, vel_y=2.3110, action=-0.0764, disturbance=0.1051\n",
+      "[t=019] Ref=10.66, Obs=9.71, Error=0.95, Ctrl=-1.24, VelY=2.31, PosY=9.71\n",
+      "Before update: pos_y=9.7081, vel_y=2.3110\n",
+      "After update: pos_y=12.0191, vel_y=0.3835, action=-1.2389, disturbance=-0.4575\n",
+      "[t=020] Ref=10.75, Obs=12.02, Error=-1.27, Ctrl=-1.70, VelY=0.38, PosY=12.02\n",
+      "Before update: pos_y=12.0191, vel_y=0.3835\n",
+      "After update: pos_y=12.4026, vel_y=-0.4230, action=-1.7040, disturbance=0.9358\n",
+      "[t=021] Ref=10.11, Obs=12.40, Error=-2.29, Ctrl=-0.88, VelY=-0.42, PosY=12.40\n",
+      "Before update: pos_y=12.4026, vel_y=-0.4230\n",
+      "After update: pos_y=11.9796, vel_y=-1.1408, action=-0.8846, disturbance=0.1245\n",
+      "[t=022] Ref=8.85, Obs=11.98, Error=-3.13, Ctrl=-0.83, VelY=-1.14, PosY=11.98\n",
+      "Before update: pos_y=11.9796, vel_y=-1.1408\n",
+      "After update: pos_y=10.8388, vel_y=-1.3088, action=-0.8330, disturbance=0.5509\n",
+      "[t=023] Ref=7.18, Obs=10.84, Error=-3.66, Ctrl=-0.69, VelY=-1.31, PosY=10.84\n",
+      "Before update: pos_y=10.8388, vel_y=-1.3088\n",
+      "After update: pos_y=9.5301, vel_y=-1.3449, action=-0.6853, disturbance=0.5183\n",
+      "[t=024] Ref=5.41, Obs=9.53, Error=-4.12, Ctrl=-0.74, VelY=-1.34, PosY=9.53\n",
+      "Before update: pos_y=9.5301, vel_y=-1.3449\n",
+      "After update: pos_y=8.1852, vel_y=-2.1003, action=-0.7395, disturbance=-0.1505\n",
+      "[t=025] Ref=3.84, Obs=8.19, Error=-4.35, Ctrl=-0.65, VelY=-2.10, PosY=8.19\n",
+      "Before update: pos_y=8.1852, vel_y=-2.1003\n",
+      "After update: pos_y=6.0848, vel_y=-2.4378, action=-0.6507, disturbance=0.1032\n",
+      "[t=026] Ref=2.77, Obs=6.08, Error=-3.32, Ctrl=0.26, VelY=-2.44, PosY=6.08\n",
+      "Before update: pos_y=6.0848, vel_y=-2.4378\n",
+      "After update: pos_y=3.6470, vel_y=-1.1099, action=0.2581, disturbance=0.8260\n",
+      "[t=027] Ref=2.43, Obs=3.65, Error=-1.21, Ctrl=1.10, VelY=-1.11, PosY=3.65\n",
+      "Before update: pos_y=3.6470, vel_y=-1.1099\n",
+      "After update: pos_y=2.5371, vel_y=-0.0852, action=1.0977, disturbance=-0.1840\n",
+      "[t=028] Ref=2.95, Obs=2.54, Error=0.41, Ctrl=0.80, VelY=-0.09, PosY=2.54\n",
+      "Before update: pos_y=2.5371, vel_y=-0.0852\n",
+      "After update: pos_y=2.4519, vel_y=0.9974, action=0.7973, disturbance=0.2768\n",
+      "[t=029] Ref=4.31, Obs=2.45, Error=1.86, Ctrl=0.74, VelY=1.00, PosY=2.45\n",
+      "Before update: pos_y=2.4519, vel_y=0.9974\n",
+      "After update: pos_y=3.4493, vel_y=1.8532, action=0.7439, disturbance=0.2116\n",
+      "[t=030] Ref=6.38, Obs=3.45, Error=2.94, Ctrl=0.56, VelY=1.85, PosY=3.45\n",
+      "Before update: pos_y=3.4493, vel_y=1.8532\n",
+      "After update: pos_y=5.3025, vel_y=2.1693, action=0.5583, disturbance=-0.0569\n",
+      "[t=031] Ref=8.93, Obs=5.30, Error=3.63, Ctrl=0.36, VelY=2.17, PosY=5.30\n",
+      "Before update: pos_y=5.3025, vel_y=2.1693\n",
+      "After update: pos_y=7.4718, vel_y=3.2382, action=0.3649, disturbance=0.9209\n",
+      "[t=032] Ref=11.64, Obs=7.47, Error=4.17, Ctrl=0.35, VelY=3.24, PosY=7.47\n",
+      "Before update: pos_y=7.4718, vel_y=3.2382\n",
+      "After update: pos_y=10.7100, vel_y=3.9018, action=0.3472, disturbance=0.6403\n",
+      "[t=033] Ref=14.17, Obs=10.71, Error=3.46, Ctrl=-0.54, VelY=3.90, PosY=10.71\n",
+      "Before update: pos_y=10.7100, vel_y=3.9018\n",
+      "After update: pos_y=14.6119, vel_y=2.7286, action=-0.5414, disturbance=-0.2417\n",
+      "[t=034] Ref=16.20, Obs=14.61, Error=1.59, Ctrl=-1.43, VelY=2.73, PosY=14.61\n",
+      "Before update: pos_y=14.6119, vel_y=2.7286\n",
+      "After update: pos_y=17.3404, vel_y=0.9797, action=-1.4342, disturbance=-0.0418\n",
+      "[t=035] Ref=17.50, Obs=17.34, Error=0.16, Ctrl=-1.15, VelY=0.98, PosY=17.34\n",
+      "Before update: pos_y=17.3404, vel_y=0.9797\n",
+      "After update: pos_y=18.3201, vel_y=-0.0325, action=-1.1470, disturbance=0.2328\n",
+      "[t=036] Ref=17.92, Obs=18.32, Error=-0.40, Ctrl=-0.51, VelY=-0.03, PosY=18.32\n",
+      "Before update: pos_y=18.3201, vel_y=-0.0325\n",
+      "After update: pos_y=18.2876, vel_y=-0.4924, action=-0.5144, disturbance=0.0512\n",
+      "[t=037] Ref=17.47, Obs=18.29, Error=-0.82, Ctrl=-0.45, VelY=-0.49, PosY=18.29\n",
+      "Before update: pos_y=18.2876, vel_y=-0.4924\n",
+      "After update: pos_y=17.7951, vel_y=-1.8503, action=-0.4451, disturbance=-0.9621\n",
+      "[t=038] Ref=16.25, Obs=17.80, Error=-1.55, Ctrl=-0.72, VelY=-1.85, PosY=17.80\n",
+      "Before update: pos_y=17.7951, vel_y=-1.8503\n",
+      "After update: pos_y=15.9448, vel_y=-1.8214, action=-0.7249, disturbance=0.5688\n",
+      "[t=039] Ref=14.49, Obs=15.94, Error=-1.45, Ctrl=-0.14, VelY=-1.82, PosY=15.94\n",
+      "Before update: pos_y=15.9448, vel_y=-1.8214\n",
+      "After update: pos_y=14.1234, vel_y=-1.8105, action=-0.1362, disturbance=-0.0351\n",
+      "[t=040] Ref=12.49, Obs=14.12, Error=-1.63, Ctrl=-0.38, VelY=-1.81, PosY=14.12\n",
+      "Before update: pos_y=14.1234, vel_y=-1.8105\n",
+      "After update: pos_y=12.3129, vel_y=-2.5841, action=-0.3778, disturbance=-0.5769\n",
+      "[t=041] Ref=10.57, Obs=12.31, Error=-1.75, Ctrl=-0.37, VelY=-2.58, PosY=12.31\n",
+      "Before update: pos_y=12.3129, vel_y=-2.5841\n",
+      "After update: pos_y=9.7288, vel_y=-2.6270, action=-0.3669, disturbance=0.0656\n",
+      "[t=042] Ref=9.01, Obs=9.73, Error=-0.72, Ctrl=0.51, VelY=-2.63, PosY=9.73\n",
+      "Before update: pos_y=9.7288, vel_y=-2.6270\n",
+      "After update: pos_y=7.1018, vel_y=-2.1418, action=0.5068, disturbance=-0.2842\n",
+      "[t=043] Ref=8.05, Obs=7.10, Error=0.95, Ctrl=1.05, VelY=-2.14, PosY=7.10\n",
+      "Before update: pos_y=7.1018, vel_y=-2.1418\n",
+      "After update: pos_y=4.9600, vel_y=-0.8231, action=1.0503, disturbance=0.0541\n",
+      "[t=044] Ref=7.79, Obs=4.96, Error=2.83, Ctrl=1.33, VelY=-0.82, PosY=4.96\n",
+      "Before update: pos_y=4.9600, vel_y=-0.8231\n",
+      "After update: pos_y=4.1368, vel_y=0.5801, action=1.3287, disturbance=-0.0078\n",
+      "[t=045] Ref=8.23, Obs=4.14, Error=4.09, Ctrl=0.98, VelY=0.58, PosY=4.14\n",
+      "Before update: pos_y=4.1368, vel_y=0.5801\n",
+      "After update: pos_y=4.7169, vel_y=1.2523, action=0.9839, disturbance=-0.2537\n",
+      "[t=046] Ref=9.24, Obs=4.72, Error=4.53, Ctrl=0.46, VelY=1.25, PosY=4.72\n",
+      "Before update: pos_y=4.7169, vel_y=1.2523\n",
+      "After update: pos_y=5.9692, vel_y=1.1480, action=0.4649, disturbance=-0.4439\n",
+      "[t=047] Ref=10.61, Obs=5.97, Error=4.64, Ctrl=0.32, VelY=1.15, PosY=5.97\n",
+      "Before update: pos_y=5.9692, vel_y=1.1480\n",
+      "After update: pos_y=7.1172, vel_y=1.4143, action=0.3191, disturbance=0.0619\n",
+      "[t=048] Ref=12.04, Obs=7.12, Error=4.92, Ctrl=0.55, VelY=1.41, PosY=7.12\n",
+      "Before update: pos_y=7.1172, vel_y=1.4143\n",
+      "After update: pos_y=8.5315, vel_y=2.9292, action=0.5537, disturbance=1.1026\n",
+      "[t=049] Ref=13.24, Obs=8.53, Error=4.70, Ctrl=0.27, VelY=2.93, PosY=8.53\n",
+      "Before update: pos_y=8.5315, vel_y=2.9292\n",
+      "After update: pos_y=11.4606, vel_y=2.4292, action=0.2685, disturbance=-0.4755\n",
+      "[t=050] Ref=13.94, Obs=11.46, Error=2.48, Ctrl=-1.25, VelY=2.43, PosY=11.46\n",
+      "Before update: pos_y=11.4606, vel_y=2.4292\n",
+      "After update: pos_y=13.8899, vel_y=1.1807, action=-1.2537, disturbance=0.2481\n",
+      "[t=051] Ref=13.97, Obs=13.89, Error=0.08, Ctrl=-1.45, VelY=1.18, PosY=13.89\n",
+      "Before update: pos_y=13.8899, vel_y=1.1807\n",
+      "After update: pos_y=15.0706, vel_y=-0.7278, action=-1.4546, disturbance=-0.3359\n",
+      "[t=052] Ref=13.26, Obs=15.07, Error=-1.81, Ctrl=-1.17, VelY=-0.73, PosY=15.07\n",
+      "Before update: pos_y=15.0706, vel_y=-0.7278\n",
+      "After update: pos_y=14.3428, vel_y=-1.5034, action=-1.1710, disturbance=0.3226\n",
+      "[t=053] Ref=11.85, Obs=14.34, Error=-2.49, Ctrl=-0.33, VelY=-1.50, PosY=14.34\n",
+      "Before update: pos_y=14.3428, vel_y=-1.5034\n",
+      "After update: pos_y=12.8394, vel_y=-1.8356, action=-0.3315, disturbance=-0.1510\n",
+      "[t=054] Ref=9.91, Obs=12.84, Error=-2.93, Ctrl=-0.22, VelY=-1.84, PosY=12.84\n",
+      "Before update: pos_y=12.8394, vel_y=-1.8356\n",
+      "After update: pos_y=11.0038, vel_y=-3.0891, action=-0.2171, disturbance=-1.2199\n",
+      "[t=055] Ref=7.71, Obs=11.00, Error=-3.30, Ctrl=-0.25, VelY=-3.09, PosY=11.00\n",
+      "Before update: pos_y=11.0038, vel_y=-3.0891\n",
+      "After update: pos_y=7.9147, vel_y=-3.0109, action=-0.2463, disturbance=0.0157\n",
+      "[t=056] Ref=5.55, Obs=7.91, Error=-2.37, Ctrl=0.71, VelY=-3.01, PosY=7.91\n",
+      "Before update: pos_y=7.9147, vel_y=-3.0109\n",
+      "After update: pos_y=4.9039, vel_y=-2.1523, action=0.7064, disturbance=-0.1489\n",
+      "[t=057] Ref=3.74, Obs=4.90, Error=-1.16, Ctrl=0.93, VelY=-2.15, PosY=4.90\n",
+      "Before update: pos_y=4.9039, vel_y=-2.1523\n",
+      "After update: pos_y=2.7515, vel_y=-1.6099, action=0.9287, disturbance=-0.6015\n",
+      "[t=058] Ref=2.56, Obs=2.75, Error=-0.19, Ctrl=0.78, VelY=-1.61, PosY=2.75\n",
+      "Before update: pos_y=2.7515, vel_y=-1.6099\n",
+      "After update: pos_y=1.1416, vel_y=-1.2727, action=0.7796, disturbance=-0.6033\n",
+      "[t=059] Ref=2.19, Obs=1.14, Error=1.05, Ctrl=1.04, VelY=-1.27, PosY=1.14\n",
+      "Before update: pos_y=1.1416, vel_y=-1.2727\n",
+      "After update: pos_y=-0.1310, vel_y=0.6670, action=1.0350, disturbance=0.7774\n",
+      "[t=060] Ref=2.68, Obs=-0.13, Error=2.81, Ctrl=1.54, VelY=0.67, PosY=-0.13\n",
+      "Before update: pos_y=-0.1310, vel_y=0.6670\n",
+      "After update: pos_y=0.5360, vel_y=2.1850, action=1.5417, disturbance=0.0431\n",
+      "[t=061] Ref=3.97, Obs=0.54, Error=3.44, Ctrl=0.77, VelY=2.19, PosY=0.54\n",
+      "Before update: pos_y=0.5360, vel_y=2.1850\n",
+      "After update: pos_y=2.7210, vel_y=2.7685, action=0.7741, disturbance=0.0278\n",
+      "[t=062] Ref=5.88, Obs=2.72, Error=3.16, Ctrl=0.15, VelY=2.77, PosY=2.72\n",
+      "Before update: pos_y=2.7210, vel_y=2.7685\n",
+      "After update: pos_y=5.4895, vel_y=2.9580, action=0.1491, disturbance=0.3173\n",
+      "[t=063] Ref=8.11, Obs=5.49, Error=2.62, Ctrl=-0.00, VelY=2.96, PosY=5.49\n",
+      "Before update: pos_y=5.4895, vel_y=2.9580\n",
+      "After update: pos_y=8.4474, vel_y=1.4540, action=-0.0038, disturbance=-1.2045\n",
+      "[t=064] Ref=10.35, Obs=8.45, Error=1.90, Ctrl=-0.13, VelY=1.45, PosY=8.45\n",
+      "Before update: pos_y=8.4474, vel_y=1.4540\n",
+      "After update: pos_y=9.9014, vel_y=1.2891, action=-0.1302, disturbance=0.1108\n",
+      "[t=065] Ref=12.25, Obs=9.90, Error=2.35, Ctrl=0.81, VelY=1.29, PosY=9.90\n",
+      "Before update: pos_y=9.9014, vel_y=1.2891\n",
+      "After update: pos_y=11.1905, vel_y=1.1454, action=0.8077, disturbance=-0.8224\n",
+      "[t=066] Ref=13.53, Obs=11.19, Error=2.34, Ctrl=0.51, VelY=1.15, PosY=11.19\n",
+      "Before update: pos_y=11.1905, vel_y=1.1454\n",
+      "After update: pos_y=12.3360, vel_y=2.1895, action=0.5141, disturbance=0.6445\n",
+      "[t=067] Ref=14.00, Obs=12.34, Error=1.67, Ctrl=0.03, VelY=2.19, PosY=12.34\n",
+      "Before update: pos_y=12.3360, vel_y=2.1895\n",
+      "After update: pos_y=14.5255, vel_y=1.7364, action=0.0262, disturbance=-0.2604\n",
+      "[t=068] Ref=13.59, Obs=14.53, Error=-0.94, Ctrl=-1.52, VelY=1.74, PosY=14.53\n",
+      "Before update: pos_y=14.5255, vel_y=1.7364\n",
+      "After update: pos_y=16.2619, vel_y=-0.5752, action=-1.5180, disturbance=-0.6199\n",
+      "[t=069] Ref=12.34, Obs=16.26, Error=-3.92, Ctrl=-1.97, VelY=-0.58, PosY=16.26\n",
+      "Before update: pos_y=16.2619, vel_y=-0.5752\n",
+      "After update: pos_y=15.6867, vel_y=-2.0479, action=-1.9689, disturbance=0.4387\n",
+      "[t=070] Ref=10.44, Obs=15.69, Error=-5.25, Ctrl=-0.87, VelY=-2.05, PosY=15.69\n",
+      "Before update: pos_y=15.6867, vel_y=-2.0479\n",
+      "After update: pos_y=13.6389, vel_y=-2.7112, action=-0.8720, disturbance=0.0039\n",
+      "[t=071] Ref=8.15, Obs=13.64, Error=-5.49, Ctrl=-0.18, VelY=-2.71, PosY=13.64\n",
+      "Before update: pos_y=13.6389, vel_y=-2.7112\n",
+      "After update: pos_y=10.9277, vel_y=-2.7386, action=-0.1779, disturbance=-0.1207\n",
+      "[t=072] Ref=5.78, Obs=10.93, Error=-5.15, Ctrl=0.17, VelY=-2.74, PosY=10.93\n",
+      "Before update: pos_y=10.9277, vel_y=-2.7386\n",
+      "After update: pos_y=8.1891, vel_y=-2.9069, action=0.1688, disturbance=-0.6109\n",
+      "[t=073] Ref=3.64, Obs=8.19, Error=-4.55, Ctrl=0.29, VelY=-2.91, PosY=8.19\n",
+      "Before update: pos_y=8.1891, vel_y=-2.9069\n",
+      "After update: pos_y=5.2822, vel_y=-3.5973, action=0.2933, disturbance=-1.2744\n",
+      "[t=074] Ref=2.01, Obs=5.28, Error=-3.27, Ctrl=0.77, VelY=-3.60, PosY=5.28\n",
+      "Before update: pos_y=5.2822, vel_y=-3.5973\n",
+      "After update: pos_y=1.6849, vel_y=-1.9987, action=0.7663, disturbance=0.4726\n",
+      "[t=075] Ref=1.04, Obs=1.68, Error=-0.64, Ctrl=1.85, VelY=-2.00, PosY=1.68\n",
+      "Before update: pos_y=1.6849, vel_y=-1.9987\n",
+      "After update: pos_y=-0.3139, vel_y=0.3910, action=1.8545, disturbance=0.3354\n",
+      "[t=076] Ref=0.81, Obs=-0.31, Error=1.12, Ctrl=1.28, VelY=0.39, PosY=-0.31\n",
+      "Before update: pos_y=-0.3139, vel_y=0.3910\n",
+      "After update: pos_y=0.0772, vel_y=0.9774, action=1.2782, disturbance=-0.6527\n",
+      "[t=077] Ref=1.24, Obs=0.08, Error=1.16, Ctrl=0.01, VelY=0.98, PosY=0.08\n",
+      "Before update: pos_y=0.0772, vel_y=0.9774\n",
+      "After update: pos_y=1.0546, vel_y=0.6791, action=0.0082, disturbance=-0.2088\n",
+      "[t=078] Ref=2.16, Obs=1.05, Error=1.10, Ctrl=-0.05, VelY=0.68, PosY=1.05\n",
+      "Before update: pos_y=1.0546, vel_y=0.6791\n",
+      "After update: pos_y=1.7337, vel_y=0.5199, action=-0.0461, disturbance=-0.0453\n",
+      "[t=079] Ref=3.30, Obs=1.73, Error=1.57, Ctrl=0.40, VelY=0.52, PosY=1.73\n",
+      "Before update: pos_y=1.7337, vel_y=0.5199\n",
+      "After update: pos_y=2.2536, vel_y=0.3937, action=0.3951, disturbance=-0.4693\n",
+      "[t=080] Ref=4.38, Obs=2.25, Error=2.13, Ctrl=0.52, VelY=0.39, PosY=2.25\n",
+      "Before update: pos_y=2.2536, vel_y=0.3937\n",
+      "After update: pos_y=2.6473, vel_y=0.6132, action=0.5239, disturbance=-0.2651\n",
+      "[t=081] Ref=5.11, Obs=2.65, Error=2.47, Ctrl=0.41, VelY=0.61, PosY=2.65\n",
+      "Before update: pos_y=2.6473, vel_y=0.6132\n",
+      "After update: pos_y=3.2605, vel_y=0.6779, action=0.4142, disturbance=-0.2882\n",
+      "[t=082] Ref=5.26, Obs=3.26, Error=2.00, Ctrl=-0.16, VelY=0.68, PosY=3.26\n",
+      "Before update: pos_y=3.2605, vel_y=0.6779\n",
+      "After update: pos_y=3.9383, vel_y=2.0197, action=-0.1584, disturbance=1.5680\n",
+      "[t=083] Ref=4.70, Obs=3.94, Error=0.76, Ctrl=-0.76, VelY=2.02, PosY=3.94\n",
+      "Before update: pos_y=3.9383, vel_y=2.0197\n",
+      "After update: pos_y=5.9580, vel_y=1.8665, action=-0.7611, disturbance=0.8099\n",
+      "[t=084] Ref=3.42, Obs=5.96, Error=-2.54, Ctrl=-2.46, VelY=1.87, PosY=5.96\n",
+      "Before update: pos_y=5.9580, vel_y=1.8665\n",
+      "After update: pos_y=7.8246, vel_y=-0.1722, action=-2.4590, disturbance=0.6069\n",
+      "[t=085] Ref=1.52, Obs=7.82, Error=-6.30, Ctrl=-3.04, VelY=-0.17, PosY=7.82\n",
+      "Before update: pos_y=7.8246, vel_y=-0.1722\n",
+      "After update: pos_y=7.6524, vel_y=-3.7931, action=-3.0420, disturbance=-0.5961\n",
+      "[t=086] Ref=-0.77, Obs=7.65, Error=-8.42, Ctrl=-2.04, VelY=-3.79, PosY=7.65\n",
+      "Before update: pos_y=7.6524, vel_y=-3.7931\n",
+      "After update: pos_y=3.8593, vel_y=-6.2141, action=-2.0413, disturbance=-0.7590\n",
+      "[t=087] Ref=-3.16, Obs=3.86, Error=-7.02, Ctrl=0.50, VelY=-6.21, PosY=3.86\n",
+      "Before update: pos_y=3.8593, vel_y=-6.2141\n",
+      "After update: pos_y=-2.3548, vel_y=-4.6202, action=0.4975, disturbance=0.4750\n",
+      "[t=088] Ref=-5.34, Obs=-2.35, Error=-2.99, Ctrl=2.54, VelY=-4.62, PosY=-2.35\n",
+      "Before update: pos_y=-2.3548, vel_y=-4.6202\n",
+      "After update: pos_y=-6.9750, vel_y=-1.7413, action=2.5356, disturbance=-0.1188\n",
+      "[t=089] Ref=-7.01, Obs=-6.98, Error=-0.03, Ctrl=1.81, VelY=-1.74, PosY=-6.98\n",
+      "Before update: pos_y=-6.9750, vel_y=-1.7413\n",
+      "After update: pos_y=-8.7164, vel_y=-0.2025, action=1.8130, disturbance=-0.4483\n",
+      "[t=090] Ref=-7.93, Obs=-8.72, Error=0.79, Ctrl=0.25, VelY=-0.20, PosY=-8.72\n",
+      "Before update: pos_y=-8.7164, vel_y=-0.2025\n",
+      "After update: pos_y=-8.9189, vel_y=-0.1778, action=0.2514, disturbance=-0.2469\n",
+      "[t=091] Ref=-7.99, Obs=-8.92, Error=0.93, Ctrl=-0.23, VelY=-0.18, PosY=-8.92\n",
+      "Before update: pos_y=-8.9189, vel_y=-0.1778\n",
+      "After update: pos_y=-9.0967, vel_y=-1.0911, action=-0.2288, disturbance=-0.7023\n",
+      "[t=092] Ref=-7.18, Obs=-9.10, Error=1.92, Ctrl=0.47, VelY=-1.09, PosY=-9.10\n",
+      "Before update: pos_y=-9.0967, vel_y=-1.0911\n",
+      "After update: pos_y=-10.1878, vel_y=-0.4078, action=0.4709, disturbance=0.1033\n",
+      "[t=093] Ref=-5.63, Obs=-10.19, Error=4.56, Ctrl=1.88, VelY=-0.41, PosY=-10.19\n",
+      "Before update: pos_y=-10.1878, vel_y=-0.4078\n",
+      "After update: pos_y=-10.5956, vel_y=2.1677, action=1.8801, disturbance=0.6546\n",
+      "[t=094] Ref=-3.58, Obs=-10.60, Error=7.02, Ctrl=1.96, VelY=2.17, PosY=-10.60\n",
+      "Before update: pos_y=-10.5956, vel_y=2.1677\n",
+      "After update: pos_y=-8.4279, vel_y=3.3869, action=1.9574, disturbance=-0.5214\n",
+      "[t=095] Ref=-1.34, Obs=-8.43, Error=7.09, Ctrl=0.31, VelY=3.39, PosY=-8.43\n",
+      "Before update: pos_y=-8.4279, vel_y=3.3869\n",
+      "After update: pos_y=-5.0410, vel_y=3.0263, action=0.3118, disturbance=-0.3338\n",
+      "[t=096] Ref=0.75, Obs=-5.04, Error=5.80, Ctrl=-0.64, VelY=3.03, PosY=-5.04\n",
+      "Before update: pos_y=-5.0410, vel_y=3.0263\n",
+      "After update: pos_y=-2.0147, vel_y=2.2333, action=-0.6377, disturbance=0.1473\n",
+      "[t=097] Ref=2.38, Obs=-2.01, Error=4.39, Ctrl=-0.67, VelY=2.23, PosY=-2.01\n",
+      "Before update: pos_y=-2.0147, vel_y=2.2333\n",
+      "After update: pos_y=0.2186, vel_y=1.0019, action=-0.6699, disturbance=-0.3381\n",
+      "[t=098] Ref=3.30, Obs=0.22, Error=3.08, Ctrl=-0.59, VelY=1.00, PosY=0.22\n",
+      "Before update: pos_y=0.2186, vel_y=1.0019\n",
+      "After update: pos_y=1.2205, vel_y=0.9882, action=-0.5851, disturbance=0.6716\n",
+      "[t=099] Ref=3.36, Obs=1.22, Error=2.14, Ctrl=-0.28, VelY=0.99, PosY=1.22\n",
+      "Before update: pos_y=1.2205, vel_y=0.9882\n",
+      "After update: pos_y=2.2087, vel_y=1.1775, action=-0.2821, disturbance=0.5703\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "hello\n"
+     ]
+    }
+   ],
    "source": [
+    "import numpy as np\n",
+    "import sys\n",
+    "import os\n",
+    "\n",
+    "# Add the parent directory (the project root) to sys.path\n",
+    "project_root = os.path.abspath(os.path.join(os.getcwd(), '..'))\n",
+    "if project_root not in sys.path:\n",
+    "    sys.path.append(project_root)\n",
+    "\n",
     "# Import relevant modules\n",
+    "from uuv_mission.dynamic import Submarine\n",
+    "from uuv_mission.dynamic import ClosedLoop\n",
+    "from uuv_mission.mission_class import Mission\n",
+    "from uuv_mission.control import PIDController\n",
+    "\n",
+    "print(ClosedLoop.__module__)\n",
+    "print(ClosedLoop.__file__ if hasattr(ClosedLoop, '__file__') else \"No __file__ attribute\")\n",
     "\n",
     "sub = Submarine()\n",
     "# Instantiate your controller (depending on your implementation)\n",
+    "controller = PIDController(KP=0.03, KI=0.02, KD=0.75, integral_limit=100)\n",
+    "\n",
     "closed_loop = ClosedLoop(sub, controller)\n",
-    "mission = Mission.from_csv(\"path/to/file\") # You must implement this method in the Mission class\n",
+    "mission = Mission.from_csv(\"../data/mission.csv\") # You must implement this method in the Mission class\n",
     "\n",
     "trajectory = closed_loop.simulate_with_random_disturbances(mission)\n",
     "trajectory.plot_completed_mission(mission)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "first-venv",
+   "display_name": "base",
    "language": "python",
    "name": "python3"
   },
@@ -62,7 +418,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.12"
+   "version": "3.12.7"
   }
  },
  "nbformat": 4,
diff --git a/uuv_mission/control.py b/uuv_mission/control.py
new file mode 100644
index 00000000..4980b681
--- /dev/null
+++ b/uuv_mission/control.py
@@ -0,0 +1,38 @@
+class PIDController:
+    # Constructor to initialize PID controller
+    def __init__(self, KP, KI, KD, integral_limit):
+        self.KP = KP
+        self.KD = KD
+        self.KI = KI
+        self.prev_error = 0.0
+        self.integral = 0.0
+        self.integral_limit = integral_limit
+
+    def reset(self):
+        # Reset integral and previous error for multiple test runs
+        self.prev_error = 0.0
+        self.integral = 0.0
+
+    def compute_control(self, reference, measurement):
+
+        error = reference - measurement
+        self.integral += error
+
+        # Apply anti-windup by clamping the integral within specified limits
+        if self.integral_limit is not None:
+            self.integral = max(min(self.integral, self.integral_limit), -self.integral_limit)
+
+        derivative = error - self.prev_error
+
+        # Calculate the control signal using PID formula.
+        control = self.KP * error + self.KI * self.integral + self.KD * derivative
+        
+        # Store error for next derivative calculation
+        self.prev_error = error
+
+        # Return computed control signal
+        return control
+
+    def __call__(self, reference, measurement):
+        # Make the object callable: allows direct use as controller(reference, measurement). This is utilised when completing the ClosedLoop class.
+        return self.compute_control(reference, measurement)
\ No newline at end of file
diff --git a/uuv_mission/dynamic.py b/uuv_mission/dynamic.py
index c7c7ad53..8b3fde99 100644
--- a/uuv_mission/dynamic.py
+++ b/uuv_mission/dynamic.py
@@ -1,7 +1,8 @@
 from __future__ import annotations
 from dataclasses import dataclass
+import sys
 import numpy as np
-import matplotlib.pyplot as plt
+import matplotlib.pyplot as plt 
 from .terrain import generate_reference_and_limits
 
 class Submarine:
@@ -21,6 +22,7 @@ def __init__(self):
 
     def transition(self, action: float, disturbance: float):
         self.pos_x += self.vel_x * self.dt
+
         self.pos_y += self.vel_y * self.dt
 
         force_y = -self.drag * self.vel_y + self.actuator_gain * (action + disturbance)
@@ -81,11 +83,15 @@ def from_csv(cls, file_name: str):
 
 class ClosedLoop:
     def __init__(self, plant: Submarine, controller):
+        sys.stdout.flush()
         self.plant = plant
         self.controller = controller
 
     def simulate(self,  mission: Mission, disturbances: np.ndarray) -> Trajectory:
 
+        import sys
+        sys.stdout.flush()
+
         T = len(mission.reference)
         if len(disturbances) < T:
             raise ValueError("Disturbances must be at least as long as mission duration")
@@ -93,15 +99,30 @@ def simulate(self,  mission: Mission, disturbances: np.ndarray) -> Trajectory:
         positions = np.zeros((T, 2))
         actions = np.zeros(T)
         self.plant.reset_state()
+        
+        # Reset controller
+        self.controller.reset()
+        sys.stdout.flush()
 
         for t in range(T):
             positions[t] = self.plant.get_position()
+            # Get current observation
             observation_t = self.plant.get_depth()
-            # Call your controller here
-            self.plant.transition(actions[t], disturbances[t])
+            reference_t = mission.reference[t]
+
+            # Compute control action
+            action_t = self.controller(reference_t, observation_t)
+
+            # Store and apply action
+            actions[t] = action_t
+            self.plant.transition(action_t, disturbances[t])
+
 
         return Trajectory(positions)
         
+        
+
     def simulate_with_random_disturbances(self, mission: Mission, variance: float = 0.5) -> Trajectory:
         disturbances = np.random.normal(0, variance, len(mission.reference))
         return self.simulate(mission, disturbances)
+   
diff --git a/uuv_mission/mission_class.py b/uuv_mission/mission_class.py
new file mode 100644
index 00000000..d05b22ad
--- /dev/null
+++ b/uuv_mission/mission_class.py
@@ -0,0 +1,28 @@
+import pandas as pd
+
+class Mission:
+    # Constructor to initialize a Mission object
+    def __init__(self, reference, cave_height, cave_depth):
+        self.reference = reference
+        self.cave_height = cave_height
+        self.cave_depth = cave_depth
+
+    @classmethod
+    def from_csv(cls, filepath):
+        # Class method to create a Mission object from a CSV file.
+        df = pd.read_csv(filepath)
+        required_cols = {"reference", "cave_height", "cave_depth"}
+        
+        # Error check theres the right number of columns
+        if not required_cols.issubset(df.columns):
+            raise ValueError(f"CSV must contain columns: {required_cols}")
+        # Create a Mission instance using the columns from the DataFrame
+        return cls(
+            reference=df["reference"],
+            cave_height=df["cave_height"],
+            cave_depth=df["cave_depth"]
+        )
+
+    def __repr__(self):
+        # String representation showing how many entries are in the reference for troubleshooting
+        return f"<Mission with {len(self.reference)} entries>"
\ No newline at end of file