Modeling and Simulink of Rotary Inverted Pendulum Using LQR and Fuzzy Logic
Implementation Plan:
Step 1: Initially, we constructed the Rotary Inverted Pendulum (RIP) Simulink model and LQR controller which derived its state equations.
Step 2: Then, we simulate the model and collect angular position and velocity data of the pendulum.
Step 3: Next, we derive the state-space equations of the system and linearize them around the upright equilibrium point.
Step 4: Next, we stabilize the pendulum motions using the LQR controller data based on collected data .
Step 5: Next, we implement Fuzzy Logic with defined input membership functions (pendulum angle, angular velocity, error) and output membership functions (control voltage/torque) to handle nonlinear behaviors.
Step 6: Finally, we plot performance for the following metrics:
6.1 : Time Vs Arm Angle (rad)
6.2 : Time Vs Pendulum Angle (rad)
6.3 : Time Vs Voltage
6.4 : Time Vs Stabilization Error(%)
Software Requirements:
1. Development Tool: Matlab-R2023a/Simulink or above
2. Operating System: Windows 10 (64-bit) or above
Note
1) If the plan does not meet your requirements, provide detailed steps, parameters, models, or expected results in advance. Once implemented, changes won’t be possible without prior input; otherwise, we’ll proceed as per our implementation plan.
2) If the plan satisfies your requirement, Please confirm with us.
3) Project based on Simulation only, not a real time project.
4) Please understand that any modifications made to the confirmed implementation plan will not be made after the project
7.1: Number of Vehicles vs. End-to-End Latency (ms)
7.2: Number of Vehicles vs. Transaction Confirmation Latency (ms)
7.3: Number of Vehicles vs. Edge Processing Latency (ms)
7.4: Number of Vehicles vs. Transaction Throughput (Mbps)
7.5: Number of Vehicles vs. Edge data processing rate (%)
7.6: Number of Vehicles vs. Network Scalability (%)
7.7: Number of Vehicles vs. Data Storage Scalability (%)
7.8: Number of Vehicles vs. Communication Overhead
7.9: Number of Vehicles vs. Computational Cost
7.10: Number of Vehicles vs. Security Overhead
7.11: Number of Vehicles vs. Attack detection rate (%)
7.12: Number of Vehicles vs. False Positive rate (%)
7.13: Number of Vehicles vs. Network Uptime(ms)
7.14:Number of Vehicles vs. Packet Loss Rate (%)
7.15: Number of Vehicles vs. Energy Consumption (J)
7.16: Number of Vehicles vs. Transaction Operational Cost
7.17: Number of Transactions vs. Energy Consumption (J)
7.18: Number of Epochs vs. Accuracy (%)
7.19: Number of Epochs vs. Precision (%)
7.20: Number of Epochs vs. Recall (%)
7.21: Number of Epochs vs. F1 Score (%)
7.22: Number of Epochs vs. AUC Curve
