Chapter 8: Real-World Applications

Drone Altitude Control

class DroneAltitudePID:
    def __init__(self):
        self.pid = PID(Kp=2.0, Ki=0.5, Kd=1.5, dt=0.01)
        
    def compute_thrust(self, target_altitude, current_altitude):
        base_thrust = 9.81  # Hover thrust
        correction = self.pid.compute(target_altitude, current_altitude)
        return base_thrust + correction

Robot Arm Control

Joint-space PID with gravity compensation.

Autonomous Vehicle Lane Keeping

MPC for path following with steering constraints.

# Simplified vehicle model
# x = [y, psi, v]  (lateral pos, heading, velocity)
# u = [delta]      (steering angle)

A = np.array([[0, v, 0],
              [0, 0, v/L],
              [0, 0, 0]])
B = np.array([[0], [v/L], [0]])

Comparison: PID vs MPC

Aspect	PID	MPC
Complexity	Low	High
Constraints	Difficult	Native
Computation	Fast	Slower
MIMO systems	Limited	Natural
Optimality	No	Yes

Congratulations! You've completed the Control Algorithms course! 🎯🚀