Anfis controller for double inverted pendulum

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Anfis controller for double inverted pendulum

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ANFIS Controller for Double Inverted Pendulum

The control of a double inverted pendulum is a classic challenge in control theory due to its inherently unstable and nonlinear dynamics. Traditional control methods often struggle to maintain stability under varying conditions, making adaptive and intelligent control techniques like ANFIS (Adaptive Neuro-Fuzzy Inference System) a promising solution.

Understanding ANFIS ANFIS combines the interpretability of fuzzy logic with the learning capability of neural networks. It adjusts its membership functions and rule base during training, optimizing control performance. For a double inverted pendulum, ANFIS helps balance the system by continuously adapting to disturbances and parameter variations.

Challenges in Double Inverted Pendulum Control Unlike a single pendulum, the double inverted pendulum has an additional link, increasing complexity. The controller must account for coupled dynamics, nonlinearities, and external disturbances. ANFIS excels here by learning from input-output data, refining its rules, and improving robustness over time.

Design Considerations by Mohammad Vahedian In Mohammad Vahedian’s work, the ANFIS controller likely follows these steps: Data Collection: Simulate or experimentally record pendulum states (angles, velocities) and corresponding control inputs. Training: Use input-output pairs to train ANFIS, optimizing fuzzy rules and membership functions. Real-Time Adaptation: Deploy the trained ANFIS to adjust control actions dynamically, ensuring stability even with unmodeled disturbances.

Advantages of ANFIS Adaptability: Self-tuning capability handles system variations. Precision: Combines fuzzy logic’s approximate reasoning with neural networks’ accuracy. Robustness: Effective under noise and parameter uncertainties.

Conclusion ANFIS offers a powerful approach to stabilizing double inverted pendulums, blending learning and control. Mohammad Vahedian’s design highlights its potential in complex, nonlinear systems, paving the way for applications in robotics and automation where precise, adaptive control is critical.