diff --git a/Ex5_omni_bangbang.py b/Ex5_omni_bangbang.py
index fdf0f77..60a586a 100755
--- a/Ex5_omni_bangbang.py
+++ b/Ex5_omni_bangbang.py
@@ -19,7 +19,6 @@ F = np.linalg.inv(J)
 
 def controller_omni_bangbang(t, X_I, dX_I, target_position):
     """Bang-bang controller with dynamic target position"""
-
     ...
     
     # coordinate transform
diff --git a/Ex6_omni_pid.py b/Ex6_omni_pid.py
index f576065..3162d79 100755
--- a/Ex6_omni_pid.py
+++ b/Ex6_omni_pid.py
@@ -19,7 +19,6 @@ F = np.linalg.inv(J)
 
 def controller_omni_pid(t, X_I, dX_I, target_position):
     """PID Controller with dynamic target position"""
-
     ...
 
     # Coordinate transform
diff --git a/Ex7_omni_pid_pom.py b/Ex7_omni_pid_pom.py
new file mode 100755
index 0000000..5c8f18e
--- /dev/null
+++ b/Ex7_omni_pid_pom.py
@@ -0,0 +1,82 @@
+#!/usr/bin/env python
+
+import numpy as np
+from WheeledRobot import OmniRobotEnv
+
+# OmniRobot kinematics
+a1 = 0.0;         b1 = 0.0; l1 = 0.5
+a2 = (2/3)*np.pi; b2 = 0.0; l2 = 0.5
+a3 = (4/3)*np.pi; b3 = 0.0; l3 = 0.5
+r = 0.1
+
+# Kinematics matrix
+J = np.array([
+    [1/r*np.sin(a1+b1), -1/r*np.cos(a1+b1), -l1/r*np.cos(b1)],
+    [1/r*np.sin(a2+b2), -1/r*np.cos(a2+b2), -l2/r*np.cos(b2)],
+    [1/r*np.sin(a3+b3), -1/r*np.cos(a3+b3), -l3/r*np.cos(b3)]
+])
+F = np.linalg.inv(J)
+
+def controller_omni_pid_pom(t, X_I, dX_I, target_position):
+    """PID Controller with Point of Motion control and dynamic target position"""
+    
+    ...
+
+    # Coordinate transform
+    R_RI = np.array([[ np.cos(X_I[2,0]), np.sin(X_I[2,0]), 0.0],
+                     [-np.sin(X_I[2,0]), np.cos(X_I[2,0]), 0.0],
+                     [             0.0,               0.0, 1.0]])
+    dX_R_des = R_RI @ dX_I_des
+    U = J @ dX_R_des
+    return U
+
+def run_simulation():
+    """Run simulation using Gymnasium environment with PID POM control to dynamic target"""
+    global int_err
+    
+    # Initialize environment with fixed target for reproducible results
+    # You can set random_target=True for random target generation
+    env = OmniRobotEnv(render_mode="human", random_target=False)
+    observation, _ = env.reset()
+    
+    # Expand render bounds to show target position (default target is at [10,5])
+    env.set_render_bounds((-2, 12), (-2, 8))
+    
+    # Initialize integral error
+    int_err = np.zeros((3, 1))
+    
+    print("Starting Omnidirectional Robot PID Point of Motion Control Simulation")
+    print("Controller: PID with Point of Motion control to dynamic target position")
+    print(f"Target position: [{observation[7]:.2f}, {observation[8]:.2f}, {observation[9]:.2f}]")
+    
+    for step in range(1000):
+        # Extract controller inputs from observation
+        # New observation format: [x, y, theta, dx, dy, dtheta, time, target_x, target_y, target_theta]
+        time = observation[6]                    # Current time
+        X_I = observation[:3].reshape(-1, 1)     # State [x, y, theta]
+        dX_I = observation[3:6].reshape(-1, 1)   # Derivatives [dx, dy, dtheta]
+        target_position = observation[7:10]      # Target [target_x, target_y, target_theta]
+        
+        # Call PID POM controller with dynamic target
+        U = controller_omni_pid_pom(time, X_I, dX_I, target_position)
+        
+        # Step environment
+        observation, reward, terminated, truncated, _ = env.step(U.flatten())
+        
+        # Render the environment
+        env.render()
+        
+        # Check if target reached
+        if terminated:
+            print(f"Target reached at step {step}! Reward: {reward:.2f}")
+            break
+        elif truncated:
+            print(f"Maximum steps reached at step {step}")
+            break
+    
+    input("Press Enter to close the simulation window...")
+    env.close()
+    print("Simulation completed")
+
+if __name__ == "__main__":
+    run_simulation()