论文标题
非线性系统的解释性和自适应MPC
An interpretative and adaptive MPC for nonlinear systems
论文作者
论文摘要
非线性系统的模型预测控制（MPC）在模型准确性与实时计算负担之间的权衡受到了权衡。一种广泛使用的近似方法是使用EKF方法的连续线性化MPC（SL-MPC），其中EKF算法是处理未衡量的干扰和无法获得的完整状态信息。受此启发，本文提出了一种解释性和自适应MPC（IA-MPC）方法。在我们的IA-MPC方法中，首先通过在初始点执行基于第一原则的模型的线性化来获得线性状态空间模型，然后将此线性状态空间模型转换为等效的ARX模型。然后由EKF算法在线更新该解释性ARX模型，该算法被修改为无矩阵内运算符的脱钩。基于ARX的MPC问题通过我们以前的无构造，无基质和无库CDAL-ARX算法来解决。这种简单的无库C代码实现将大大减少在嵌入式平台上部署非线性MPC的困难。在四个典型的非线性基准示例中，使用EKF和SL-MPC对非线性MPC进行了IA-MPC方法的性能，以表明我们的IA-MPC方法的有效性。
Model predictive control (MPC) for nonlinear systems suffers a trade-off between the model accuracy and real-time computational burden. One widely used approximation method is the successive linearization MPC (SL-MPC) with EKF method, in which the EKF algorithm is to handle unmeasured disturbances and unavailable full states information. Inspired by this, an interpretative and adaptive MPC (IA-MPC) method, is presented in this paper. In our IA-MPC method, a linear state-space model is firstly obtained by performing the linearization of a first-principle-based model at the initial point, and then this linear state-space model is transformed into an equivalent ARX model. This interpretative ARX model is then updated online by the EKF algorithm, which is modified as a decoupled one without matrix-inverse operator. The corresponding ARX-based MPC problem are solved by our previous construction-free, matrix-free and library-free CDAL-ARX algorithm. This simple library-free C-code implementation would significantly reduce the difficulty in deploying nonlinear MPC on embedded platforms. The performance of the IA-MPC method is tested against the nonlinear MPC with EKF and SL-MPC with EKF method in four typical nonlinear benchmark examples, which show the effectiveness of our IA-MPC method.
