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Integration of model predictive control and backstepping approach and its stability analysis

机译:模型预测控制与反推方法的集成及其稳定性分析

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Backstepping controller (BS) and model predictive controller (MPC) have been widely used for many applications by virtue of their own merits. BS works even with non-minimum phase and finite-time escape and MPC can handle state and input constraints explicitly. Nevertheless, BS requires repeated differentiations of the virtual control, whereas high computational loads of MPC are obstacles to practical implementation. This study proposes a control strategy that combines BS and MPC for nonlinear systems in strict-feedback form. It is proven that the controller renders the closed-loop system asymptotically stable. The proposed MPC-BS requires less computational load than that of MPC, since it only optimizes the virtual input of the first step and computes the input by backstepping approach. The explosion of terms caused by the consecutive differentiation in BS approach is also addressed.
机译:Backstepping控制器(BS)和模型预测控制器(MPC)由于其自身的优点而被广泛用于许多应用中。 BS甚至可以在非最小相位和有限时间转义的情况下工作,MPC可以显式处理状态和输入约束。但是,BS需要对虚拟控件进行反复区分,而MPC的高计算量却是实际实施的障碍。这项研究提出了一种将BS和MPC相结合的控制策略,用于严格反馈形式的非线性系统。证明了控制器使闭环系统渐近稳定。所提出的MPC-BS比MPC所需的计算负载少,因为它仅优化第一步的虚拟输入并通过反步方法计算输入。还解决了由BS方法中的连续差异引起的术语爆炸。

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