On Data-driven Attack-resilient Gaussian Process Regression for Dynamic Systems
On Data-driven Attack-resilient Gaussian Process Regression for Dynamic Systems
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DOI：
10.23919/acc45564.2020.9147328
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发表时间：
2020-07
期刊：
影响因子：
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通讯作者：
Hunmin Kim;Pinyao Guo;Minghui Zhu;Peng Liu
中科院分区：
文献类型：
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作者：
Hunmin Kim;Pinyao Guo;Minghui Zhu;Peng Liu
研究了部分未知非线性动态系统在传感器攻击和执行器攻击下的抗攻击高斯过程回归问题。该问题被表述为部分未知系统的状态、攻击向量和系统函数的联合估计。我们提出了一种新的学习算法，将我们最近开发的未知输入和状态估计技术的高斯过程回归算法。算法的稳定性进行了形式化研究。我们还表明，如果估计误差为非零的状态估计的数量是由一个常数有界的系统函数逼近的平均情况下的学习误差是递减的。我们证明了所提出的算法的性能，通过数值模拟的IEEE 68节点测试系统。
paper studies attack-resilient Gaussian process regression of partially unknown nonlinear dynamic systems subject to sensor attacks and actuator attacks. The problem is formulated as the joint estimation of states, attack vectors, and system functions of partially unknown systems. We propose a new learning algorithm by incorporating our recently developed unknown input and state estimation technique into the Gaussian process regression algorithm. Stability of the proposed algorithm is formally studied. We also show that average case learning errors of system function approximation are diminishing if the number of state estimates whose estimation errors are non-zero is bounded by a constant. We demonstrate the performance of the proposed algorithm by numerical simulations on the IEEE 68-bus test system.