Ex8..9

Ex8_tank_linear.py

@@ -0,0 +1,71 @@
+#!/usr/bin/env python
+
+import numpy as np
+from WheeledRobot import TankRobotEnv
+
+# TankRobot kinematics
+b = 1
+r = 0.1
+
+# Wheel equations
+J = np.array([
+    [+1/r, 0, -b/(2*r)],
+    [-1/r, 0, -b/(2*r)]
+])
+F = np.linalg.pinv(J)
+
+def controller_tank_linear(t, X_I, dX_I, target_position):
+    """Enhanced linear control with polar coordinates and dynamic target"""
+    
+    ...
+
+    # Calculate control input
+    U = J @ dX_R_des
+    return U
+
+def run_simulation():
+    """Run simulation using Gymnasium environment with enhanced linear control to dynamic target"""
+    
+    # Initialize environment with fixed target for reproducible results
+    # You can set random_target=True for random target generation
+    env = TankRobotEnv(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))
+    
+    print("Starting Tank Robot Enhanced Linear Control Simulation")
+    print("Controller: Enhanced linear control with polar coordinates to dynamic target")
+    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 enhanced linear controller with dynamic target
+        U = controller_tank_linear(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()

Ex9_tank_l1.py

@@ -0,0 +1,76 @@
+#!/usr/bin/env python
+
+import numpy as np
+from WheeledRobot import TankRobotEnv
+
+# TankRobot kinematics
+b = 1
+r = 0.1
+
+# Wheel equations
+J = np.array([
+    [+1/r, 0, -b/(2*r)],
+    [-1/r, 0, -b/(2*r)]
+])
+F = np.linalg.pinv(J)
+
+def controller_tank_l1(t, X_I, dX_I, target_position):
+    """L1 adaptive control with path planning and dynamic target"""
+
+    # Controller parameters
+    dx_R_max = 3
+    L0 = 4
+    L1 = 4
+
+    ...
+
+    # Calculate control input
+    U = J @ dX_R_des
+    return U
+
+def run_simulation():
+    """Run simulation using Gymnasium environment with L1 adaptive control to dynamic target"""
+    
+    # Initialize environment with fixed target for reproducible results
+    # You can set random_target=True for random target generation
+    env = TankRobotEnv(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))
+    
+    print("Starting Tank Robot L1 Adaptive Control Simulation")
+    print("Controller: L1 adaptive control with path planning to dynamic target")
+    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 L1 adaptive controller with dynamic target
+        U = controller_tank_l1(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()