多任务控制中抗干扰，通过聚类+鲁棒聚合提升系统稳定性
Adversarially Robust Multitask Adaptive Control
- 按系统相似性聚类，融合识别与抗干扰聚合策略
- 诚实系统越多，平均损失越低，且在恶意系统占比有限时仍有效
- 适合分布式控制、工业自动化等存在异常节点的多系统场景
我们研究对抗鲁棒的多任务自适应线性二次控制：多个系统在模型不确定和对抗性干扰下协同学习控制策略。提出一种聚类式多任务方法，结合聚类、系统辨识与鲁棒聚合，缓解被污染的模型更新。分析表明，聚类精度、簇内异质性及对抗行为共同影响各类线性二次型（LQR）任务下的确定性等价（CE）控制期望损失。建立非渐近界，证明损失随每簇中诚实系统数量增加而反比下降，且在每簇中恶意系统占比受控时该优势依然保持。
原文摘要 · Abstract (English)
We study adversarially robust multitask adaptive linear quadratic control; a setting where multiple systems collaboratively learn control policies under model uncertainty and adversarial corruption. We propose a clustered multitask approach that integrates clustering and system identification with resilient aggregation to mitigate corrupted model updates. Our analysis characterizes how clustering accuracy, intra-cluster heterogeneity, and adversarial behavior affect the expected regret of certainty-equivalent (CE) control across LQR tasks. We establish non-asymptotic bounds demonstrating that the regret decreases inversely with the number of honest systems per cluster and that this reduction is preserved under a bounded fraction of adversarial systems within each cluster.
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