论文标题
控制未知的非线性系统具有线性变化的MPC
Control of Unknown Nonlinear Systems with Linear Time-Varying MPC
论文作者
论文摘要
我们提出了未知输入型非线性动力学系统的模型预测控制（MPC）策略。一种非参数方法用于从观察到的数据估算非线性动力学。然后将估计的非线性动力学随着时间的时间而变化的状态空间变化，以构建仿射时间变化（ATV）模型。通过使用采样技术计算估计和线性化过程产生的误差界限。 ATV模型和不确定性集用于设计可靠的模型预测控制（MPC）问题，该问题可确保具有很高概率的未知系统的安全性。一个简单的非线性示例演示了通常使用线性化方法失败的方法的有效性。
We present a Model Predictive Control (MPC) strategy for unknown input-affine nonlinear dynamical systems. A non-parametric method is used to estimate the nonlinear dynamics from observed data. The estimated nonlinear dynamics are then linearized over time varying regions of the state space to construct an Affine Time Varying (ATV) model. Error bounds arising from the estimation and linearization procedure are computed by using sampling techniques. The ATV model and the uncertainty sets are used to design a robust Model Predictive Control (MPC) problem which guarantees safety for the unknown system with high probability. A simple nonlinear example demonstrates the effectiveness of the approach where commonly used linearization methods fail.
