输入受限的β增量型模型算法控制

β Incremental Model Algorithmic Control with Input Constraints

在β增量型广义预测控制算法和输入受限解决方案的基础上,提出一种输入受限的β增量型模型算法控制算法。该算法通过在指标函数中引入适当加权多项式来解决输入受限,并通过在输入受限的增量型模型算法控制的控制增量前乘以β因子来提高系统稳定性。理论分析和仿真实验都表明,当模型无失配时,该算法可以通过调整β来确保闭环系统的稳定性;在满足一定条件的失配情况下,该算法具有良好的鲁棒稳定性。

In this paper, a new β-IMAC algorithm with input constraints is proposed. It solves the problem of input constraints by selecting the proper weighing polynomials in the cost function and increases the system stability by multiplying β to the control increment of IMAC. Theoretical analysis and simulations show that the algorithm guarantees the closed-loop stability by adjusting β when the model has no mismatch and also possesses good robustness to some types of model mismatches.