Yongsoon Eun

Robust Path Diversity for Network Quality of Service in Cyber-Physical Systems

The reliability of control in cyber-physical systems (CPSs) heavily depends on the network-induced delay. The problem of obtaining a maximum allowable delay bound has been widely studied in the networked control systems (NCS) area. Once the delay bound is derived, the remaining question is how to make a network satisfy the bound. In this paper, we propose a robust path selection algorithm, which exploits multipath diversity for providing robust network performance against intrinsic randomness in delay. Our path selection algorithm gives the required paths for any given robustness level parameterized by the reliability violation probability. Based on extensive experimental results with our testbed, we empirically show that the proposed scheme can provide the required network quality of service (QoS) for system robustness.

Secure and robust state estimation under sensor attacks, measurement noises, and process disturbances: Observer-based combinatorial approach

This paper presents a secure and robust state estimation scheme for continuous-time linear dynamical systems. The method is secure in that it correctly estimates the states under sensor attacks by exploiting sensing redundancy, and it is robust in that it guarantees a bounded estimation error despite measurement noises and process disturbances. In this method, an individual Luenberger observer (of possibly smaller size) is designed from each sensor. Then, the state estimates from each of the observers are combined through a scheme motivated by error correction techniques, which results in estimation resiliency against sensor attacks under a mild condition on the system observability. Moreover, in the state estimates combining stage, our method reduces the search space of a minimization problem to a finite set, which substantially reduces the required computational effort.

Robustness of multivariable discrete-time variable structure control

A robust stability criterion for multivariable discrete-time variable structure control is proposed. When a variable structure controller is designed and implemented as a sampled-data system, achieving robustness to uncertainties can become difficult. To ameliorate the situation, an uncertainty estimator, also formulated within the variable structure framework, can be used as an embedded discrete-time variable structure controller. This approach requires a bounded changing rate of the uncertainties to ensure robust stability. However, when uncertainties vary as a function of state variables, which occurs with parametric uncertainties, it is not practical to assume such a bound. In this paper, uncertainties are assumed to consist of external disturbances and parametric uncertainties. An uncertainty compensator is used to deal with the former, and a robust stability criterion is developed using the small gain theorem for the latter.

Tracking of random references: random sensitivity function and tracking quality indicators

In this note, the problem of random reference tracking by linear feedback systems is addressed. The approach is based on the so-called random sensitivity function, which plays the same role as the usual sensitivity function in step reference tracking. Using the random sensitivity function, quality indicators for random reference tracking are introduced, and their utilization for analysis and design of servomechanisms is illustrated.

An LQG approach to systems with saturating actuators and anti-windup implementation

This paper extends the LQG design methodology to systems with saturating actuators and shows that resulting linear controllers do not require an anti-windup implementation.

Resilient State Estimation for Control Systems Using Multiple Observers and Median Operation

This paper addresses the problem of state estimation for linear dynamic systems that is resilient against malicious attacks on sensors. By “resiliency” we mean the capability of correctly estimating the state despite external attacks. We propose a state estimation with a bank of observers combined through median operations and show that the proposed method is resilient in the sense that estimated states asymptotically converge to the true state despite attacks on sensors. In addition, the effect of sensor noise and process disturbance is also considered. For bounded sensor noise and process disturbance, the proposed method eliminates the effect of attack and achieves state estimation error within a bound proportional to those of sensor noise and disturbance. While existing methods are computationally heavy because online solution of nonconvex optimization is needed, the proposed approach is computationally efficient by using median operation in the place of the optimization. It should be pointed out that the proposed method requires the system states being observable with every sensor, which is not a necessary condition for the existing methods. From resilient system design point of view, however, this fact may not be critical because sensors can be chosen for resiliency in the design stage. The gained computational efficiency helps real-time implementation in practice.

Impact of sensor measurement error on sensor positioning in water quality monitoring networks

This paper studies the impact of sensor measurement error on designing a water quality monitoring network for a river system, and shows that robust sensor locations can be obtained when an optimization algorithm is combined with a statistical process control (SPC) method. Specifically, we develop a possible probabilistic model of sensor measurement error and the measurement error model is embedded into a simulation model of a river system. An optimization algorithm is used to find the optimal sensor locations that minimize the expected time until a spill detection in the presence of a constraint on the probability of detecting a spill. The experimental results show that the optimal sensor locations are highly sensitive to the variability of measurement error and false alarm rates are often unacceptably high. An SPC method is useful in finding thresholds that guarantee a false alarm rate no more than a pre-specified target level, and an optimization algorithm combined with the thresholds finds a robust sensor network.

Global Chartwise Feedback Linearization of the Quadcopter With a Thrust Positivity Preserving Dynamic Extension

We propose a new dynamic extension of the thrust variable in the quadcopter dynamics that preserves the positive sign of the thrust. This extension not only eliminates the positive sign constraint on the thrust variable, but also leads to global chartwise feedback linearization of the quadcopter dynamics. For the latter, an atlas is first constructed on the entire state space of the quadcopter and then the dynamically extended quadcopter system is transformed to a 14-dimensional linear controllable system on each chart in the atlas. Based on the chartwise dynamic feedback linearization, a global tracking strategy is proposed for the quadcopter and its excellent performance is demonstrated with a simulation.

When adversary encounters uncertain cyber-physical systems: Robust zero-dynamics attack with disclosure resources

In this paper we address the problem of designing a robust stealthy attack for adversaries to compromise an uncertain cyber-physical system without being detected. We first re-interpret the zero-dynamics attack based on the normal form representation. Then, a new alternative zero dynamics attack is presented for uncertain systems. This alternative employs a disturbance observer and does not require exact system knowledge in order to remain stealthy. The proposed robust zero-dynamics attack needs a nominal model of the system and, in addition, utilizes the input and output signals of the system. The proposed attack illustrates how the adversary is able to use disclosure resources instead of exact model knowledge. A simulation result with a hydro-turbine power system is presented to verify the attack performance.

Noise Induced Loss of Tracking in Systems With Saturating Actuators and Antiwindup

This technical note is devoted to a recently discovered phenomenon that takes place in feedback systems with saturating actuators, proportional-integral (PI) control, and antiwindup. Namely, in such systems, measurement noise induces steady-state error in step tracking, which is incompatible with the standard error coefficients. We quantify this phenomenon using stochastic averaging theory and show that the noise induced loss of tracking occurs only if antiwindup is present. An indicator that predicts this phenomenon is derived, and a rule-of-thumb, based on this indicator, is formulated. An illustration using a digital printing device is provided. [DOI: 10.1115/1.4027163]