Generalized Predictive Control Algorithm Based on TS Model

This article introduces a Generalized Predictive Control (GPC) algorithm based on the Takagi-Sugeno (TS) model. This algorithm represents an efficient control methodology capable of operating under uncertain environmental conditions. The predictive model employs TS fuzzy identification techniques, which enable system modeling and control without requiring precise mathematical models. In practice, the algorithm has been widely implemented across various domains including mechanical systems, electronics, and telecommunications to enhance control performance and system robustness. From an implementation perspective, the algorithm typically involves constructing fuzzy rules through offline identification or adaptive learning techniques, with the control law derived through optimization procedures like quadratic programming. Key implementation components include fuzzy rule base generation, antecedent parameter tuning using clustering algorithms (e.g., FCM), and consequent parameter estimation via least squares methods. When designing and implementing this algorithm, thorough analysis and evaluation of system characteristics and requirements are essential to ensure algorithmic effectiveness and reliability. Critical considerations include selecting appropriate prediction horizons, control horizons, and weight matrices in the cost function to balance tracking performance and computational complexity.