1,334 research outputs found
Sparse Packetized Predictive Control for Networked Control over Erasure Channels
We study feedback control over erasure channels with packet-dropouts. To
achieve robustness with respect to packet-dropouts, the controller transmits
data packets containing plant input predictions, which minimize a finite
horizon cost function. To reduce the data size of packets, we propose to adopt
sparsity-promoting optimizations, namely, ell-1-ell-2 and ell-2-constrained
ell-0 optimizations, for which efficient algorithms exist. We derive sufficient
conditions on design parameters, which guarantee (practical) stability of the
resulting feedback control systems when the number of consecutive
packet-dropouts is bounded.Comment: IEEE Transactions on Automatic Control, Volume 59 (2014), Issue 7
(July) (to appear
Robust H∞ control of networked control systems with access constraints and packet dropouts
We consider a class of networked control systems (NCSs) where the plant has time-varying norm-bounded parameter uncertainties, the network only provides a limited number of simultaneous accesses for the sensors and actuators, and the packet dropouts occur randomly in the network. For this class of NCSs with uncertainties and access constraints as well as packet dropouts, we derive sufficient conditions in the form of linear matrix inequalities that guarantee robust stochastic stabilisation and synthesis of H∞ controller. An example is provided to illustrate our proposed method
A data-based hybrid driven control for networked-based remote control applications
This paper develops a data-based hybrid driven control (DHDC) approach for a class of networked nonlinear systems compromising delays, packet dropouts and disturbances. First, the delays and/or packet dropouts are detected and updated online using a network problem detector. Second, a single-variable first-order proportional-integral (PI) -based adaptive grey model is designed to predict in a near future the network problems. Third, a hybrid driven scheme integrated a small adaptive buffer is used to allow the system to operate without any interrupt due to the large delays or packet dropouts. Forth, a prediction-based model-free adaptive controller is developed to compensate for the network problems. Effectiveness of the proposed approach is demonstrated through a case study
Packetized Predictive Control for Rate-Limited Networks via Sparse Representation
We study a networked control architecture for linear time-invariant plants in
which an unreliable data-rate limited network is placed between the controller
and the plant input. The distinguishing aspect of the situation at hand is that
an unreliable data-rate limited network is placed between controller and the
plant input. To achieve robustness with respect to dropouts, the controller
transmits data packets containing plant input predictions, which minimize a
finite horizon cost function. In our formulation, we design sparse packets for
rate-limited networks, by adopting an an ell-0 optimization, which can be
effectively solved by an orthogonal matching pursuit method. Our formulation
ensures asymptotic stability of the control loop in the presence of bounded
packet dropouts. Simulation results indicate that the proposed controller
provides sparse control packets, thereby giving bit-rate reductions for the
case of memoryless scalar coding schemes when compared to the use of, more
common, quadratic cost functions, as in linear quadratic (LQ) control.Comment: 9 pages, 7 figures. arXiv admin note: text overlap with
arXiv:1307.824
A novel robust predictive control system over imperfect networks
This paper aims to study on feedback control for a networked system with both uncertain delays, packet dropouts and disturbances. Here, a so-called robust predictive control (RPC) approach is designed as follows: 1- delays and packet dropouts are accurately detected online by a network problem detector (NPD); 2- a so-called PI-based neural network grey model (PINNGM) is developed in a general form for a capable of forecasting accurately in advance the network problems and the effects of disturbances on the system performance; 3- using the PINNGM outputs, a small adaptive buffer (SAB) is optimally generated on the remote side to deal with the large delays and/or packet dropouts and, therefore, simplify the control design; 4- based on the PINNGM and SAB, an adaptive sampling-based integral state feedback controller (ASISFC) is simply constructed to compensate the small delays and disturbances. Thus, the steady-state control performance is achieved with fast response, high adaptability and robustness. Case studies are finally provided to evaluate the effectiveness of the proposed approach
Sparsely-Packetized Predictive Control by Orthogonal Matching Pursuit
We study packetized predictive control, known to be robust against packet
dropouts in networked systems. To obtain sparse packets for rate-limited
networks, we design control packets via an L0 optimization, which can be
effectively solved by orthogonal matching pursuit. Our formulation ensures
asymptotic stability of the control loop in the presence of bounded packet
dropouts.Comment: 3-page extended abstract for MTNS 2012 with 3 figure
Fuzzy-model-based robust fault detection with stochastic mixed time-delays and successive packet dropouts
This is the Post-Print version of the Article. The official published version can be accessed from the link below - Copyright @ 2012 IEEEThis paper is concerned with the network-based robust fault detection problem for a class of uncertain discrete-time Takagi–Sugeno fuzzy systems with stochastic mixed time delays and successive packet dropouts. The mixed time delays comprise both the multiple discrete time delays and the infinite distributed delays. A sequence of stochastic variables is introduced to govern the random occurrences of the discrete time delays, distributed time delays, and successive packet dropouts, where all the stochastic variables are mutually independent but obey the Bernoulli distribution. The main purpose of this paper is to design a fuzzy fault detection filter such that the overall fault detection dynamics is exponentially stable in the mean square and, at the same time, the error between the residual signal and the fault signal is made as small as possible. Sufficient conditions are first established via intensive stochastic analysis for the existence of the desired fuzzy fault detection filters, and then, the corresponding solvability conditions for the desired filter gains are established. In addition, the optimal performance index for the addressed robust fuzzy fault detection problem is obtained by solving an auxiliary convex optimization problem. An illustrative example is provided to show the usefulness and effectiveness of the proposed design method.This work was supported in part by the National Natural Science Foundation of China under Grant 61028008, 60825303, 61004067,
National 973 Project under Grant 2009CB320600, the Key Laboratory of Integrated Automation for the Process Industry (Northeastern University), Ministry of Education, the Engineering and Physical
Sciences Research Council (EPSRC) of the U.K. under Grant GR/S27658/01, the Royal Society of the U.K., the University of Hong
Kong under Grant HKU/CRCG/200907176129 and the Alexander von Humboldt Foundation of Germany
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