Explicit Solution Method for Generalized Predictive Control

Generalized Predictive Control (GPC) represents an advanced control strategy widely applied in industrial process control systems. The explicit solution method serves as one implementation approach for GPC, which obtains the control law by analytically solving the optimization problem, demonstrating higher computational efficiency compared to implicit solution methods.

The core concept of the explicit solution involves transforming the optimization problem into analytical expressions, thereby avoiding the complexity of online iterative solutions. This approach proves particularly suitable for control systems with demanding real-time requirements. During MATLAB implementation, practitioners typically need to construct prediction models, design objective functions, and solve for optimal control sequences through matrix operations using functions like 'mpc' or custom GPC algorithms with explicit matrix inversions.

In simulation processes, the performance of explicit solutions can be influenced by factors such as model accuracy and prediction horizon length. By adjusting these parameters through systematic tuning procedures (e.g., using MATLAB's optimization toolbox), engineers can effectively balance control responsiveness and system robustness while maintaining computational efficiency through precomputed gain matrices.