1,804 research outputs found
Optimal Attack against Cyber-Physical Control Systems with Reactive Attack Mitigation
This paper studies the performance and resilience of a cyber-physical control
system (CPCS) with attack detection and reactive attack mitigation. It
addresses the problem of deriving an optimal sequence of false data injection
attacks that maximizes the state estimation error of the system. The results
provide basic understanding about the limit of the attack impact. The design of
the optimal attack is based on a Markov decision process (MDP) formulation,
which is solved efficiently using the value iteration method. Using the
proposed framework, we quantify the effect of false positives and
mis-detections on the system performance, which can help the joint design of
the attack detection and mitigation. To demonstrate the use of the proposed
framework in a real-world CPCS, we consider the voltage control system of power
grids, and run extensive simulations using PowerWorld, a high-fidelity power
system simulator, to validate our analysis. The results show that by carefully
designing the attack sequence using our proposed approach, the attacker can
cause a large deviation of the bus voltages from the desired setpoint. Further,
the results verify the optimality of the derived attack sequence and show that,
to cause maximum impact, the attacker must carefully craft his attack to strike
a balance between the attack magnitude and stealthiness, due to the
simultaneous presence of attack detection and mitigation
Parameterized MDPs and Reinforcement Learning Problems -- A Maximum Entropy Principle Based Framework
We present a framework to address a class of sequential decision making
problems. Our framework features learning the optimal control policy with
robustness to noisy data, determining the unknown state and action parameters,
and performing sensitivity analysis with respect to problem parameters. We
consider two broad categories of sequential decision making problems modelled
as infinite horizon Markov Decision Processes (MDPs) with (and without) an
absorbing state. The central idea underlying our framework is to quantify
exploration in terms of the Shannon Entropy of the trajectories under the MDP
and determine the stochastic policy that maximizes it while guaranteeing a low
value of the expected cost along a trajectory. This resulting policy enhances
the quality of exploration early on in the learning process, and consequently
allows faster convergence rates and robust solutions even in the presence of
noisy data as demonstrated in our comparisons to popular algorithms such as
Q-learning, Double Q-learning and entropy regularized Soft Q-learning. The
framework extends to the class of parameterized MDP and RL problems, where
states and actions are parameter dependent, and the objective is to determine
the optimal parameters along with the corresponding optimal policy. Here, the
associated cost function can possibly be non-convex with multiple poor local
minima. Simulation results applied to a 5G small cell network problem
demonstrate successful determination of communication routes and the small cell
locations. We also obtain sensitivity measures to problem parameters and
robustness to noisy environment data.Comment: 17 pages, 7 figure
A detectability criterion and data assimilation for non-linear differential equations
In this paper we propose a new sequential data assimilation method for
non-linear ordinary differential equations with compact state space. The method
is designed so that the Lyapunov exponents of the corresponding estimation
error dynamics are negative, i.e. the estimation error decays exponentially
fast. The latter is shown to be the case for generic regular flow maps if and
only if the observation matrix H satisfies detectability conditions: the rank
of H must be at least as great as the number of nonnegative Lyapunov exponents
of the underlying attractor. Numerical experiments illustrate the exponential
convergence of the method and the sharpness of the theory for the case of
Lorenz96 and Burgers equations with incomplete and noisy observations
LQR based improved discrete PID controller design via optimum selection of weighting matrices using fractional order integral performance index
This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.The continuous and discrete time Linear Quadratic Regulator (LQR) theory has been used in this paper for the design of optimal analog and discrete PID controllers respectively. The PID controller gains are formulated as the optimal state-feedback gains, corresponding to the standard quadratic cost function involving the state variables and the controller effort. A real coded Genetic Algorithm (GA) has been used next to optimally find out the weighting matrices, associated with the respective optimal state-feedback regulator design while minimizing another time domain integral performance index, comprising of a weighted sum of Integral of Time multiplied Squared Error (ITSE) and the controller effort. The proposed methodology is extended for a new kind of fractional order (FO) integral performance indices. The impact of fractional order (as any arbitrary real order) cost function on the LQR tuned PID control loops is highlighted in the present work, along with the achievable cost of control. Guidelines for the choice of integral order of the performance index are given depending on the characteristics of the process, to be controlled.This work has been supported by the Dept. of Science & Technology (DST), Govt. of India under PURSE programme
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