AI 中文总结
本文针对带标称植物模型和神经网络残差模型的NNCSs，提出基于区间的方法构造控制仿射包含函数，通过有限表征迭代构造MCIS的内外近似，经数值例子验证了方法的有效性。
AI 中文摘要
本文研究具有标称植物模型和神经网络残差模型的神经网络控制系统（NNCSs）的最大受控不变集（MCIS）的计算。开发了一种基于区间的方法，用于为NNCS动力学构造控制仿射包含函数，并利用输入空间中诱导的超平面排列，这将受控不变性的验证从无限控制搜索简化为有限组代表性评估。基于该有限表征，验证引导算法迭代构造MCIS的内外近似，这些近似天然可并行化。数值例子证明了所提方法的有效性。
英文摘要
This article studies the computation of the maximal controlled invariant set (MCIS) for neural network control systems (NNCSs) with a nominal plant model and a neural network residual model. An interval-based method is developed to construct a control-affine inclusion function for the NNCS dynamics and to exploit the induced hyperplane arrangement in the input space, which reduces the verification of controlled invariance from an infinite control search to a finite set of representative evaluations. Based on this finite characterization, verification-guided algorithms iteratively construct inner and outer approximations of the the MCIS, which are naturally parallelizable. Numerical examples demonstrate the effectiveness of the proposed approach.
Commentsaccepted to IEEE Conference on Decision and Control, Honolulu, HI, USA, 2026
