1,216 research outputs found
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
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
Compressive Sampling for Remote Control Systems
In remote control, efficient compression or representation of control signals
is essential to send them through rate-limited channels. For this purpose, we
propose an approach of sparse control signal representation using the
compressive sampling technique. The problem of obtaining sparse representation
is formulated by cardinality-constrained L2 optimization of the control
performance, which is reducible to L1-L2 optimization. The low rate random
sampling employed in the proposed method based on the compressive sampling, in
addition to the fact that the L1-L2 optimization can be effectively solved by a
fast iteration method, enables us to generate the sparse control signal with
reduced computational complexity, which is preferable in remote control systems
where computation delays seriously degrade the performance. We give a
theoretical result for control performance analysis based on the notion of
restricted isometry property (RIP). An example is shown to illustrate the
effectiveness of the proposed approach via numerical experiments
Data based identification and prediction of nonlinear and complex dynamical systems
We thank Dr. R. Yang (formerly at ASU), Dr. R.-Q. Su (formerly at ASU), and Mr. Zhesi Shen for their contributions to a number of original papers on which this Review is partly based. This work was supported by ARO under Grant No. W911NF-14-1-0504. W.-X. Wang was also supported by NSFC under Grants No. 61573064 and No. 61074116, as well as by the Fundamental Research Funds for the Central Universities, Beijing Nova Programme.Peer reviewedPostprin
Adaptive Sampling with Mobile Sensor Networks
Mobile sensor networks have unique advantages compared with wireless sensor networks. The mobility enables mobile sensors to flexibly reconfigure themselves to meet sensing requirements. In this dissertation, an adaptive sampling method for mobile sensor networks is presented. Based on the consideration of sensing resource constraints, computing abilities, and onboard energy limitations, the adaptive sampling method follows a down sampling scheme, which could reduce the total number of measurements, and lower sampling cost. Compressive sensing is a recently developed down sampling method, using a small number of randomly distributed measurements for signal reconstruction. However, original signals cannot be reconstructed using condensed measurements, as addressed by Shannon Sampling Theory. Measurements have to be processed under a sparse domain, and convex optimization methods should be applied to reconstruct original signals. Restricted isometry property would guarantee signals can be recovered with little information loss. While compressive sensing could effectively lower sampling cost, signal reconstruction is still a great research challenge. Compressive sensing always collects random measurements, whose information amount cannot be determined in prior. If each measurement is optimized as the most informative measurement, the reconstruction performance can perform much better.
Based on the above consideration, this dissertation is focusing on an adaptive sampling approach, which could find the most informative measurements in unknown environments and reconstruct original signals. With mobile sensors, measurements are collect sequentially, giving the chance to uniquely optimize each of them. When mobile sensors are about to collect a new measurement from the surrounding environments, existing information is shared among networked sensors so that each sensor would have a global view of the entire environment. Shared information is analyzed under Haar Wavelet domain, under which most nature signals appear sparse, to infer a model of the environments. The most informative measurements can be determined by optimizing model parameters. As a result, all the measurements collected by the mobile sensor network are the most informative measurements given existing information, and a perfect reconstruction would be expected.
To present the adaptive sampling method, a series of research issues will be addressed, including measurement evaluation and collection, mobile network establishment, data fusion, sensor motion, signal reconstruction, etc. Two dimensional scalar field will be reconstructed using the method proposed. Both single mobile sensors and mobile sensor networks will be deployed in the environment, and reconstruction performance of both will be compared.In addition, a particular mobile sensor, a quadrotor UAV is developed, so that the adaptive sampling method can be used in three dimensional scenarios
SimpleTrack:Adaptive Trajectory Compression with Deterministic Projection Matrix for Mobile Sensor Networks
Some mobile sensor network applications require the sensor nodes to transfer
their trajectories to a data sink. This paper proposes an adaptive trajectory
(lossy) compression algorithm based on compressive sensing. The algorithm has
two innovative elements. First, we propose a method to compute a deterministic
projection matrix from a learnt dictionary. Second, we propose a method for the
mobile nodes to adaptively predict the number of projections needed based on
the speed of the mobile nodes. Extensive evaluation of the proposed algorithm
using 6 datasets shows that our proposed algorithm can achieve sub-metre
accuracy. In addition, our method of computing projection matrices outperforms
two existing methods. Finally, comparison of our algorithm against a
state-of-the-art trajectory compression algorithm show that our algorithm can
reduce the error by 10-60 cm for the same compression ratio
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