An output probabilistic constrained control algorithm based on adaptive dynamic matrix control

摘要

In the process industry, the existence of uncertainty will have a bad effect on the control effect of the system. Robustness is an important index to evaluate the performance of the control system. The state or output may be expressed as probability form is more close to real industrial when the state or output is influenced by uncertainty. Therefore, the method of doing with deterministic constraints is no longer applicable. In this paper, an output probabilistic constrained control algorithm based on adaptive dynamic matrix control (DMC) is proposed for systems affected by disturbances. The output probabilistic constraints are turned into deterministic ones, which are equivalent to the standard quadratic programming (QP) problem. The adaptive DMC and penalty function method are used to solve the deterministic situations. Finally, the study on gauge control system (AGC) of hot strip mill process (HSMP) is performed to demonstrate the effectiveness and correctness of the algorithm.