534 research outputs found
Backstepping PDE Design: A Convex Optimization Approach
Abstract\u2014Backstepping design for boundary linear PDE is
formulated as a convex optimization problem. Some classes of
parabolic PDEs and a first-order hyperbolic PDE are studied,
with particular attention to non-strict feedback structures. Based
on the compactness of the Volterra and Fredholm-type operators
involved, their Kernels are approximated via polynomial
functions. The resulting Kernel-PDEs are optimized using Sumof-
Squares (SOS) decomposition and solved via semidefinite
programming, with sufficient precision to guarantee the stability
of the system in the L2-norm. This formulation allows optimizing
extra degrees of freedom where the Kernel-PDEs are included
as constraints. Uniqueness and invertibility of the Fredholm-type
transformation are proved for polynomial Kernels in the space
of continuous functions. The effectiveness and limitations of the
approach proposed are illustrated by numerical solutions of some
Kernel-PDEs
An algebraic approach to modeling distributed multiphysics problems: The case of a DRI reactor
© 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.This paper deals with the problem of modelling a chemical reactor for the Direct Reduction of Iron ore (DRI). Such a process is being increasingly promoted as a more viable alternative to the classic Blast Furnace for the production of iron from raw minerals. Due to the inherent complexity of the process and the reactor itself, its effective monitoring and control requires advanced mathematical models containing distributed-parameter components. While classical approaches such as Finite Element or Finite Differences are still reasonable options, for accuracy and computational efficiency reasons, an algebraic approach is proposed. A full multi-physical, albeit one-dimensional model is addressed and its accuracy is analysed
Plug-and-Play Fault Detection and control-reconfiguration for a class of nonlinear large-scale constrained systems
This paper deals with a novel Plug-and-Play (PnP) architecture for the control and monitoring of Large-Scale Systems (LSSs). The proposed approach integrates a distributed Model Predictive Control (MPC) strategy with a distributed Fault Detection (FD) architecture and methodology in a PnP framework. The basic concept is to use the FD scheme as an autonomous decision support system: once a fault is detected, the faulty subsystem can be unplugged to avoid the propagation of the fault in the interconnected LSS. Analogously, once the issue has been solved, the disconnected subsystem can be re-plugged-in. PnP design of local controllers and detectors allow these operations to be performed safely, i.e. without spoiling stability and constraint satisfaction for the whole LSS. The PnP distributed MPC is derived for a class of nonlinear LSSs and an integrated PnP distributed FD architecture is proposed. Simulation results in two paradigmatic examples show the effectiveness and the potential of the general methodology
A distributed networked approach for fault detection of large-scale systems
Networked systems present some key new challenges in the development of fault diagnosis architectures. This paper proposes a novel distributed networked fault detection methodology for large-scale interconnected systems. The proposed formulation incorporates a synchronization methodology with a filtering approach in order to reduce the effect of measurement noise and time delays on the fault detection performance. The proposed approach allows the monitoring of multi-rate systems, where asynchronous and delayed measurements are available. This is achieved through the development of a virtual sensor scheme with a model-based re-synchronization algorithm and a delay compensation strategy for distributed fault diagnostic units. The monitoring architecture exploits an adaptive approximator with learning capabilities for handling uncertainties in the interconnection dynamics. A consensus-based estimator with timevarying weights is introduced, for improving fault detectability in the case of variables shared among more than one subsystem. Furthermore, time-varying threshold functions are designed to prevent false-positive alarms. Analytical fault detectability sufficient conditions are derived and extensive simulation results are presented to illustrate the effectiveness of the distributed fault detection technique
Networked Predictive Control of Uncertain Constrained Nonlinear Systems: Recursive Feasibility and Input-to-State Stability Analysis
Abstract-In this paper, the robust state feedback stabilization of uncertain discrete-time constrained nonlinear systems in which the loop is closed through a packet-based communication network is addressed. In order to cope with model uncertainty, timevarying transmission delays, and packet dropouts (typically affecting the performances of networked control systems), a robust control scheme combining model predictive control with a network delay compensation strategy is proposed in the context of non-acknowledged UDP-like networks. The contribution of the paper is twofold. First, the issue of guaranteeing the recursive feasibility of the optimization problem associated to the receding horizon control law has been addressed, such that the invariance of the feasible region under the networked closed-loop dynamics can be guaranteed. Secondly, by exploiting a novel characterization of regional Input-to-State Stability in terms of time-varying Lyapunov functions, the networked closed-loop system has been proven to be Input-to-State Stable with respect to bounded perturbations
A distributed methodology for approximate uniform global minimum sharing
The paper deals with the distributed minimum sharing problem: a set of decision-makers compute the minimum of some local quantities of interest in a distributed and decentralized way by exchanging information through a communication network. We propose an adjustable approximate solution which enjoys several properties of crucial importance in applications. In particular, the proposed solution has good decentralization properties and it is scalable in that the number of local variables does not grow with the size or topology of the communication network. Moreover, a global and uniform (both in the initial time and in the initial conditions) asymptotic stability result is provided towards a steady state which can be made arbitrarily close to the sought minimum. Exact asymptotic convergence can be recovered at the price of losing uniformity with respect to the initial time
Distributed fault diagnosis for process and sensor faults in a class of interconnected input-output nonlinear discrete-time systems
This paper presents a distributed fault diagnosis scheme able to deal with process and sensor faults in an integrated way for a
class of interconnected input–output nonlinear uncertain discrete-time systems. A robust distributed fault detection scheme
is designed, where each interconnected subsystem is monitored by its respective fault detection agent, and according to the
decisions of these agents, further information regarding the type of the fault can be deduced. As it is shown, a process fault
occurring in one subsystem can only be detected by its corresponding detection agent whereas a sensor fault in a subsystem
can be detected by either its corresponding detection agent or the detection agent of another subsystem that is affected by the
subsystem where the sensor fault occurred. This discriminating factor is exploited for the derivation of a high-level isolation
scheme.Moreover, process and sensor fault detectability conditions characterising quantitatively the class of detectable faults
are derived. Finally, a simulation example is used to illustrate the effectiveness of the proposed distributed fault detection
scheme
Distributed cyber-attack isolation for large-scale interconnected systems
This work addresses the problem of cyber-attack isolation within a distributed diagnosis architecture for large-scale interconnected systems. Considering a distributed control architecture, malicious agents are capable of compromising the data exchanged between distributed controllers. Building on a distributed detection strategy existent in literature, in this paper we propose a distributed isolation algorithm to identify the attacked communication link. After presenting the isolation algorithm, we give a necessary and a sufficient condition for isolation to occur, relating to the structure of the physical interconnection matrices. We demonstrate the effectiveness of the proposed technique through numerical simulations
Stealthy MTD against unsupervised learning-based blind FDI Attacks in power systems
This paper examines how moving target defenses (MTD) implemented in power systems can be countered by unsupervised learning-based false data injection (FDI) attack and how MTD can be combined with physical watermarking to enhance the system resilience. A novel intelligent attack, which incorporates dimensionality reduction and density-based spatial clustering, is developed and shown to be effective in maintaining stealth in the presence of traditional MTD strategies. In resisting this new type of attack, a novel implementation of MTD combining with physical watermarking is proposed by adding Gaussian watermark into physical plant parameters to drive detection of traditional and intelligent FDI attacks, while remaining hidden to the attackers and limiting the impact on system operation and stability
An Adaptive Approach to Sensor Bias Fault Diagnosis and Accommodation for a Class of Input-Output Nonlinear Systems
This paper presents an adaptive sensor fault
diagnosis and accommodation scheme for multiple sensor bias
faults for a class of input-output nonlinear systems subject to
modeling uncertainty and measurement noise. The proposed
scheme consists of a nonlinear estimation model that includes
an adaptive component which is initiated upon the detection
of a fault, in order to approximate the magnitude of the
bias faults. A detectability condition characterizing the class of
detectable sensor bias faults is derived and the robustness and
stability properties of the adaptive scheme are presented. The
estimation of the magnitude of the sensor bias faults allows the
identification of the faulty sensors and it is also used for fault
accommodation purposes. The effectiveness of the proposed
scheme is demonstrated through a simulation example
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