Numerical robustness and efficiency of generalised predictive control algorithms with guaranteed stability
Numerical robustness and efficiency of generalised predictive control algorithms with guaranteed stability
复制标题
具有保证稳定性的广义预测控制算法的数值鲁棒性和效率
DOI：
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发表时间：
1994
期刊：
影响因子：
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通讯作者：
B. Kouvaritakis
中科院分区：
文献类型：
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作者：
J. Rossiter;B. Kouvaritakis
最近的三篇出版物提出了对广义预测控制算法的修改，以保证闭环稳定性。其中，前两种采用相同的原理，即约束后退水平预测控制（CRHPC），而第三种采用稳定广义预测控制（SGPC）策略，首先稳定然后控制对象。本文的目的是探讨 CRHPC 和 SGPC 之间的关系。结果表明，从理论上讲，这两种方法是等效的，但也表明 CRHPC 可能会遇到严重的数值不稳定问题。提出了 CRHPC 的两种替代改进实现，但 SGPC 在数值稳定性和计算效率方面具有优势。
Three recent publications proposed modifications to the generalised predictive control algorithm which guarantee closed-loop stability. Of these the first two adopt the same philosophy, namely that of constrained receding horizon predictive control (CRHPC), whereas the third adopts a stable generalised predictive control (SGPC) strategy by first stabilising then controlling the plant. The purpose of the paper is to examine the relationship between CRHPC and SGPC. It is shown that, theoretically, the two approaches are equivalent, but is is also shown that CRHPC could be subject to significant numerical instability problems. Two alternative improved implementations of CRHPC are proposed, but SGPC is shown to have the advantage in terms of numerical stability and computational efficiency.