This paper presents a decentralized design of a distributed state observer for a continuous-time linear time-invariant plant. The plant is monitored by a group of agents and each agent's measurement of the plant's state suffers from lack of detectability with the plant dynamics. Then, the missing information (i.e., the undetectable portion) is compensated by a local communication with the neighboring agents. In particular, when the local observer for each agent is designed, no global information such as the structure of network and the number of agents is needed, and so, the decentralized design is achieved. Therefore, the plug-and-play operation is enabled; that is, during the operation, a new agent can design itself without global information and join the network, and any agent may leave the network without hampering the operation as long as the rest of the agents remain detectable. The main idea is to let each agent recover its own detectable part while the undetectable part is compensated by a projection of the neighbor's estimate into the receiver's undetectable subspace. An adaptive algorithm for individual agent is designed in order to find suitable gains fo

In this paper, we address the problem of constructing a robust stealthy attack that compromises uncertain cyberphysical systems having unstable zeros. We first interpret the (non-robust) conventional zero-dynamics attack based on Byrnes- Isidori normal form, and then present a new robust zero-dynamics attack for uncertain plants. Different from the conventional strategy, our key idea is to isolate the real zero-dynamics from the plant's input-output relation and to replace it with an auxiliary nominal zero-dynamics. As a result, this alternative attack does not require the exact model knowledge anymore. The price to pay for the robustness is to utilize the input and output signals of the system (i.e., disclosure resources). It is shown that a disturbance observer can be employed to realize the new attack philosophy when there is a lack of model knowledge. Simulation results with a hydro-turbine power system are presented to verify the attack performance and robustness.

This paper presents a detection algorithm for sensor attacks and a resilient state estimation scheme for a class of uniformly observable nonlinear systems. An adversary is supposed to corrupt a subset of sensors with the possibly unbounded signals, while the system has sensor redundancy. We design an individual high-gain observer for each measurement output so that only the observable portion of the system state is obtained. Then, a nonlinear error correcting problem is solved by collecting all the information from those partial observers and exploiting redundancy. A computationally efficient, on-line monitoring scheme is presented for attack detection. Based on the attack detection scheme, an algorithm for resilient state estimation is provided. The simulation results demonstrate the effectiveness of the proposed algorithm.

The security of control systems under sensor attacks is investigated. Redundant observability is introduced, explaining existing security notions including the security index, attack detectability, and observability under attacks. Equivalent conditions between redundant observability and existing notions are presented. Based on a bank of partial observers utilizing Kalman decomposition and a decoder exploiting redundancy, an estimator design algorithm is proposed enhancing the resilience of control systems. This scheme substantially improves computational efficiency utilizing far less memory.

In truck platooning, the leading vehicle is driven manually, and the following vehicles run by autonomous driving, with the short inter-vehicle distance between trucks. To successfully perform platooning in various situations, each truck must maintain dynamic stability, and furthermore, the whole system must maintain string stability. Due to the short front-view range, however, the following vehicles’ path planning capabilities become significantly impaired. In addition, in platooning with articulated cargo trucks, the off-tracking phenomenon occurring on a curved road makes it hard for the following vehicle to track the trajectory of the preceding truck. In addition, without knowledge of the global coordinate system, it is difficult to correlate the local coordinate systems that each truck relies on for sensing environment and dynamic signals. In this paper, in order to solve these problems, a path planning algorithm for platooning of articulated cargo trucks has been developed. Using the Kalman filter, V2V (Vehicle-to-Vehicle) communication, and a novel update-and-conversion method, each following vehicle can accurately compute the trajectory of the leading vehicle’s front part for using it as a target path. The path planning algorithm of this paper was validated by simulations on severe driving scenarios and by tests on an actual road. The results demonstrated that the algorithm could provide lateral string stability and robustness for truck platooning.