论文标题
通过利用饱和度来加速非线性模型预测控制
Accelerated Nonlinear Model Predictive Control by Exploiting Saturation
论文作者
论文摘要
我们提出了一种加速非线性模型预测控制的方法。如果当前的最佳输入信号饱和，则通常在后续时间步骤中的最佳信号通常是。每当第一个和随后的一些输入信号饱和时，我们建议使用开环的最佳输入信号。当遇到非饱和信号或达到地平线的末端时，我们仅解决下一个最佳控制问题。通过这种方式，我们可以节省大量的NLP，以解决，而另一方面则保持较小的性能损失。此外，当涉及到系统以其参考安全控制系统时，NMPC会在及时重新激活。
We present an approach for accelerating nonlinear model predictive control. If the current optimal input signal is saturated, also the optimal signals in subsequent time steps often are. We propose to use the open-loop optimal input signals whenever the first and some subsequent input signals are saturated. We only solve the next optimal control problem, when a non-saturated signal is encountered, or the end of the horizon is reached. In this way, we can save a significant number of NLPs to be solved while on the other hand keep the performance loss small. Furthermore, the NMPC is reactivated in time when it comes to controlling the system safely to its reference.
