论文标题
经济NMPC的高斯牛顿般的黑森近似
A Gauss-Newton-Like Hessian Approximation for Economic NMPC
论文作者
论文摘要
经济模型预测控制（EMPC）最近由于其控制受约束的非线性系统的能力而明确优化了规定的绩效标准，因此最近变得流行。已经报道了许多应用程序的巨大性能增长，并且最近研究了闭环稳定性。但是，计算性能仍然是一个空旷的问题，只有很少的贡献提出了针对EMPC量身定制的实时算法。我们通过提出一种新的正定黑森近似值来迈向EMPC计算廉价算法的一步，该近似不会阻碍快速收敛，适合在实时迭代（RTI）方案中使用。我们提供了两个仿真示例，以证明依赖于拟议的Hessian近似值的基于RTI的EMPC的有效性。
Economic Model Predictive Control (EMPC) has recently become popular because of its ability to control constrained nonlinear systems while explicitly optimizing a prescribed performance criterion. Large performance gains have been reported for many applications and closed-loop stability has been recently investigated. However, computational performance still remains an open issue and only few contributions have proposed real-time algorithms tailored to EMPC. We perform a step towards computationally cheap algorithms for EMPC by proposing a new positive-definite Hessian approximation which does not hinder fast convergence and is suitable for being used within the real-time iteration (RTI) scheme. We provide two simulation examples to demonstrate the effectiveness of RTI-based EMPC relying on the proposed Hessian approximation.
