Distributed Predictive Control Algorithm

In this paper, the author presents a self-developed distributed predictive control algorithm that effectively controls dynamic matrices of fundamental step response models. The algorithm implementation utilizes dynamic matrix control (DMC) methodology, where the control law is computed through quadratic programming optimization with embedded step response coefficients. By employing this algorithm, more efficient and precise control can be achieved, thereby enhancing system stability and reliability. The implementation features distributed optimization architecture where multiple subsystems coordinate through limited information exchange, reducing computational burden while maintaining global performance. Furthermore, the author elaborates on the algorithm's implementation mechanism and advantages, providing detailed mathematical derivations including cost function formulation, constraint handling, and convergence analysis. Experimental results validate the algorithm's effectiveness and feasibility, demonstrating improved response characteristics and robustness compared to centralized approaches. In conclusion, this algorithm provides substantial support and guidance for the development and application of distributed control systems, warranting further research and broader implementation in industrial automation scenarios.