Predictive Control Based on T-S Model with MATLAB Implementation

This implementation demonstrates predictive control based on the T-S (Takagi-Sugeno) model, a widely adopted control methodology that utilizes fuzzy logic-based T-S models to forecast future system behavior for enhanced control performance. The T-S model framework employs a combination of local linear models weighted by fuzzy membership functions, making it suitable for various complex systems while maintaining robust performance characteristics. The MATLAB file provides a complete implementation of T-S model predictive control, featuring key algorithmic components including: - Fuzzy rule base construction using Takagi-Sugeno inference mechanism - Local linear model identification and parameter estimation - Predictive horizon optimization with constraints handling - Control law computation through weighted linear model aggregation The code includes comprehensive model documentation that explains the mathematical formulation, covering aspects such as antecedent membership function design, consequent linear model development, and prediction optimization procedures. This allows users to thoroughly understand the implementation approach and adapt the control strategy to their specific applications. By utilizing this implementation, users can effectively comprehend and apply T-S model predictive control in practical scenarios, achieving improved control performance through accurate system behavior prediction and optimized control actions. The modular code structure facilitates easy integration with existing control systems and provides clear pathways for customization and extension.