When designing networked controlsystems (NCS), stability guarantees are typically derived based on the maximum possible time between successive transmissions. A bound on this time is called the maximum allowable tran...
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When designing networked controlsystems (NCS), stability guarantees are typically derived based on the maximum possible time between successive transmissions. A bound on this time is called the maximum allowable transmission interval (MATI). In order to minimize network usage, one is often interested in the maximum value of the MATI for which stability can still be guaranteed for the NCS. However, in many practical scenarios, the worst case transmission behavior occurs only seldom, rendering an analysis based solely on the MATI unnecessarily conservative. It was recently shown in the literature that considering information about the average allowable transmission interval in the form of a reverse average dwell time (RADT) can be used to increase the maximum value of the MATI for which stability can be guaranteed. Using the same assumptions, we show that stability guarantees can even be obtained for any arbitrary given value of the MATI as long as the RADT is small enough.
This article is about the effects of computation delay on the stability caused by pulse-width modulation computation cycle and communication interface especially at high voltage systems. A topology for implementing a ...
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We propose a novel approach for analyzing dynamic regret of first-order constrained online convex optimization algorithms for strongly convex and Lipschitz-smooth objectives. Crucially, we provide a general analysis t...
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Quantum computing provides a powerful frame-work for tackling computational problems that are classically intractable. The goal of this paper is to explore the use of quantum computers for solving relevant problems in...
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ISBN:
(数字)9783907144107
ISBN:
(纸本)9798331540920
Quantum computing provides a powerful frame-work for tackling computational problems that are classically intractable. The goal of this paper is to explore the use of quantum computers for solving relevant problems in systems and control theory. In the recent literature, different quantum algorithms have been developed to tackle binary optimization, which plays an important role in various control-theoretic problems. As a prototypical example, we consider the verification of interval matrix properties such as non-singularity and stability on a quantum computer. We present a quantum algorithm solving these problems and we study its performance in simulation. Our results demonstrate that quantum computers provide a promising tool for control whose applicability to further computationally complex problems remains to be explored.
This paper,from the view of a defender,addresses the security problem of cyber-physical systems(CPSs)subject to stealthy false data injection(FDI)attacks that cannot be detected by a residual-based anomaly detector wi...
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This paper,from the view of a defender,addresses the security problem of cyber-physical systems(CPSs)subject to stealthy false data injection(FDI)attacks that cannot be detected by a residual-based anomaly detector without other defensive *** detect such a class of FDI attacks,a stochastic coding scheme,which codes the sensor measurement with a Gaussian stochastic signal at the sensor side,is proposed to assist an anomaly detector to expose the FDI *** order to ensure the system performance in the normal operational context,a decoder is adopted to decode the coded sensor measurement when received at the controller *** this detection scheme,the residual under the attack can be significantly different from that in the normal situation,and thus trigger an *** design condition of the coding signal covariance is derived to meet the constraints of false alarm rate and attack detection *** minimize the trace of the coding signal covariance,the design problem of the coding signal is converted into a constraint non-convex optimization problem,and an estimation-optimization iteration algorithm is presented to obtain a numerical solution of the coding signal covariance.A numerical example is given to verify the effectiveness of the proposed scheme.
This paper presents first results for near optimality in expectation of the closed-loop solutions for stochastic economic MPC. The approach relies on a recently developed turnpike property for stochastic optimal contr...
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Unexpected delays in train operations can cause a cascade of negative consequences in a high-speed railway *** such cases,train timetables need to be ***,timely and efficient train timetable rescheduling is still a ch...
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Unexpected delays in train operations can cause a cascade of negative consequences in a high-speed railway *** such cases,train timetables need to be ***,timely and efficient train timetable rescheduling is still a challenging problem due to its modeling difficulties and low optimization *** paper presents a Transformer-based macroscopic regulation approach which consists of two stages including Transformer-based modeling and policy-based ***,the relationship between various train schedules and operations is described by creating a macroscopic model with the Transformer,providing the better understanding of overall operation in the high-speed railway ***,a policy-based approach is used to solve a continuous decision problem after macro-modeling for fast *** experiments on various delay scenarios are *** results demonstrate the effectiveness of the proposed method in comparison to other popular methods.
We derive direct data-driven dissipativity analysis methods for Linear Parameter-Varying (LPV) systems using a single sequence of input-scheduling-output data. By means of constructing a semi-definite program subject ...
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Designing data-driven controllers in the presence of noise is an important research problem, in particular when guarantees on stability, robustness, and constraint satisfaction are desired. In this paper, we propose a...
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ISBN:
(数字)9798350382655
ISBN:
(纸本)9798350382662
Designing data-driven controllers in the presence of noise is an important research problem, in particular when guarantees on stability, robustness, and constraint satisfaction are desired. In this paper, we propose a data-driven min-max model predictive control (MPC) scheme to design state-feedback controllers from noisy data for unknown linear time-invariant (LTI) system. The considered min-max problem minimizes the worst-case cost over the set of system matrices consistent with the data. We show that the resulting optimization problem can be reformulated as a semidefinite program (SDP). By solving the SDP, we obtain a state-feedback control law that stabilizes the closed-loop system and guarantees input and state constraint satisfaction. A numerical example demonstrates the validity of our theoretical results.
A discrete-time super twisting algorithm is extended such that the windup effect is avoided in the case of limitations in the amplitude of the actuating signal. This is achieved by proposing a so-called incremental ve...
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