经济型NMPC的Gauss-Newton类Hessian逼近
A Gauss-Newton-Like Hessian Approximation for Economic NMPC
摘要：
经济模型预测控制（EMPC）由于能够控制受约束的非线性系统，同时显式优化给定的性能指标，近年来受到广泛的关注。大的性能增益已被报道为许多应用和闭环稳定性最近已被调查。然而，计算性能仍然是一个悬而未决的问题，只有很少的贡献提出了实时算法量身定制的EMPC。我们提出了一个新的正定Hessian近似，它不妨碍快速收敛，并适合于被用于实时迭代（RTI）计划的计算成本低廉的算法EMPC的一步。我们提供了两个仿真例子来证明基于RTI的EMPC的有效性依赖于建议的海森近似。
英文摘要：
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.
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