1,689 research outputs found
Optimal Control for Aperiodic Dual-Rate Systems With Time-Varying Delays
[EN] In this work, we consider a dual-rate scenario with slow input and fast output. Our objective is the maximization of the decay rate of the system through the suitable choice of the n-input signals between two measures (periodic sampling) and their times of application. The optimization algorithm is extended for time-varying delays in order to make possible its implementation in networked control systems. We provide experimental results in an air levitation system to verify the validity of the algorithm in a real plant.This work was supported in part by the Spanish Ministry of Economy and Competitiveness (MINECO) under the Projects DPI2012-31303 and DPI2014-55932-C2-2-R.Aranda-Escolástico, E.; Salt Llobregat, JJ.; Guinaldo, M.; Chacon, J.; Dormido, S. (2018). Optimal Control for Aperiodic Dual-Rate Systems With Time-Varying Delays. Sensors. 18(5):1-19. https://doi.org/10.3390/s18051491S119185Mansano, R., Godoy, E., & Porto, A. (2014). The Benefits of Soft Sensor and Multi-Rate Control for the Implementation of Wireless Networked Control Systems. Sensors, 14(12), 24441-24461. doi:10.3390/s141224441Shao, Q. M., & Cinar, A. (2015). System identification and distributed control for multi-rate sampled systems. Journal of Process Control, 34, 1-12. doi:10.1016/j.jprocont.2015.06.010Albertos, P., & Salt, J. (2011). Non-uniform sampled-data control of MIMO systems. Annual Reviews in Control, 35(1), 65-76. doi:10.1016/j.arcontrol.2011.03.004Cuenca, A., & Salt, J. (2012). RST controller design for a non-uniform multi-rate control system. Journal of Process Control, 22(10), 1865-1877. doi:10.1016/j.jprocont.2012.09.010Cuenca, Á., Ojha, U., Salt, J., & Chow, M.-Y. (2015). A non-uniform multi-rate control strategy for a Markov chain-driven Networked Control System. Information Sciences, 321, 31-47. doi:10.1016/j.ins.2015.05.035Kalman, R. E., & Bertram, J. E. (1959). 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Mathematical Problems in Engineering, 2013, 1-8. doi:10.1155/2013/865493Salt, J., & Tomizuka, M. (2014). Hard disk drive control by model based dual-rate controller. Computation saving by interlacing. Mechatronics, 24(6), 691-700. doi:10.1016/j.mechatronics.2013.12.003Salt, J., Casanova, V., Cuenca, A., & Pizá, R. (2013). Multirate control with incomplete information over Profibus-DP network. International Journal of Systems Science, 45(7), 1589-1605. doi:10.1080/00207721.2013.844286Liu, F., Gao, H., Qiu, J., Yin, S., Fan, J., & Chai, T. (2014). Networked Multirate Output Feedback Control for Setpoints Compensation and Its Application to Rougher Flotation Process. IEEE Transactions on Industrial Electronics, 61(1), 460-468. doi:10.1109/tie.2013.2240640Khargonekar, P. P., & Sivashankar, N. (1991). 2 optimal control for sampled-data systems. Systems & Control Letters, 17(6), 425-436. doi:10.1016/0167-6911(91)90082-pTornero, J., Albertos, P., & Salt, J. (2001). Periodic Optimal Control of Multirate Sampled Data Systems. IFAC Proceedings Volumes, 34(12), 195-200. doi:10.1016/s1474-6670(17)34084-3Kim, C. H., Park, H. J., Lee, J., Lee, H. W., & Lee, K. D. (2015). Multi-rate optimal controller design for electromagnetic suspension systems via linear matrix inequality optimization. Journal of Applied Physics, 117(17), 17B506. doi:10.1063/1.4906588LEE, J. H., GELORMINO, M. S., & MORARIH, M. (1992). Model predictive control of multi-rate sampled-data systems: a state-space approach. International Journal of Control, 55(1), 153-191. doi:10.1080/00207179208934231Mizumoto, I., Ikejiri, M., & Takagi, T. (2015). Stable Adaptive Predictive Control System Design via Adaptive Output Predictor for Multi-rate Sampled Systems∗∗This work was partially supported by KAKENHI, the Grant-in-Aid for Scientific Research (C) 25420444, from the Japan Society for the Promotion of Science (JSPS). IFAC-PapersOnLine, 48(8), 1039-1044. doi:10.1016/j.ifacol.2015.09.105Carpiuc, S., & Lazar, C. (2016). Real-Time Multi-Rate Predictive Cascade Speed Control of Synchronous Machines in Automotive Electrical Traction Drives. IEEE Transactions on Industrial Electronics, 1-1. doi:10.1109/tie.2016.2561881Roshany-Yamchi, S., Cychowski, M., Negenborn, R. R., De Schutter, B., Delaney, K., & Connell, J. (2013). Kalman Filter-Based Distributed Predictive Control of Large-Scale Multi-Rate Systems: Application to Power Networks. IEEE Transactions on Control Systems Technology, 21(1), 27-39. doi:10.1109/tcst.2011.2172444Donkers, M. C. F., Tabuada, P., & Heemels, W. P. M. H. (2012). Minimum attention control for linear systems. Discrete Event Dynamic Systems, 24(2), 199-218. doi:10.1007/s10626-012-0155-xQuevedo, D. E., Ma, W.-J., & Gupta, V. (2015). Anytime Control Using Input Sequences With Markovian Processor Availability. IEEE Transactions on Automatic Control, 60(2), 515-521. doi:10.1109/tac.2014.2335311Aranda Escolastico, E., Guinaldo, M., Cuenca, A., Salt, J., & Dormido, S. (2017). Anytime Optimal Control Strategy for Multi-Rate Systems. IEEE Access, 5, 2790-2797. doi:10.1109/access.2017.2671906Guinaldo, M., Sánchez, J., & Dormido, S. (2017). Control en red basado en eventos: de lo centralizado a lo distribuido. Revista Iberoamericana de Automática e Informática Industrial RIAI, 14(1), 16-30. doi:10.1016/j.riai.2016.09.007Van Loan, C. (1977). The Sensitivity of the Matrix Exponential. SIAM Journal on Numerical Analysis, 14(6), 971-981. doi:10.1137/0714065Hazan, E. (2016). Introduction to Online Convex Optimization. Foundations and Trends® in Optimization, 2(3-4), 157-325. doi:10.1561/2400000013Sala, A., Cuenca, Á., & Salt, J. (2009). A retunable PID multi-rate controller for a networked control system. Information Sciences, 179(14), 2390-2402. doi:10.1016/j.ins.2009.02.017Chacon, J., Saenz, J., Torre, L., Diaz, J., & Esquembre, F. (2017). Design of a Low-Cost Air Levitation System for Teaching Control Engineering. Sensors, 17(10), 2321. doi:10.3390/s1710232
The Statistical Performance of Collaborative Inference
The statistical analysis of massive and complex data sets will require the
development of algorithms that depend on distributed computing and
collaborative inference. Inspired by this, we propose a collaborative framework
that aims to estimate the unknown mean of a random variable . In
the model we present, a certain number of calculation units, distributed across
a communication network represented by a graph, participate in the estimation
of by sequentially receiving independent data from while
exchanging messages via a stochastic matrix defined over the graph. We give
precise conditions on the matrix under which the statistical precision of
the individual units is comparable to that of a (gold standard) virtual
centralized estimate, even though each unit does not have access to all of the
data. We show in particular the fundamental role played by both the non-trivial
eigenvalues of and the Ramanujan class of expander graphs, which provide
remarkable performance for moderate algorithmic cost
On the convergence rate of distributed gradient methods for finite-sum optimization under communication delays
Motivated by applications in machine learning and statistics, we study
distributed optimization problems over a network of processors, where the goal
is to optimize a global objective composed of a sum of local functions. In
these problems, due to the large scale of the data sets, the data and
computation must be distributed over processors resulting in the need for
distributed algorithms. In this paper, we consider a popular distributed
gradient-based consensus algorithm, which only requires local computation and
communication. An important problem in this area is to analyze the convergence
rate of such algorithms in the presence of communication delays that are
inevitable in distributed systems. We prove the convergence of the
gradient-based consensus algorithm in the presence of uniform, but possibly
arbitrarily large, communication delays between the processors. Moreover, we
obtain an upper bound on the rate of convergence of the algorithm as a function
of the network size, topology, and the inter-processor communication delays
Analysis of Multiple Flows using Different High Speed TCP protocols on a General Network
We develop analytical tools for performance analysis of multiple TCP flows
(which could be using TCP CUBIC, TCP Compound, TCP New Reno) passing through a
multi-hop network. We first compute average window size for a single TCP
connection (using CUBIC or Compound TCP) under random losses. We then consider
two techniques to compute steady state throughput for different TCP flows in a
multi-hop network. In the first technique, we approximate the queues as M/G/1
queues. In the second technique, we use an optimization program whose solution
approximates the steady state throughput of the different flows. Our results
match well with ns2 simulations.Comment: Submitted to Performance Evaluatio
Feedback Control Goes Wireless: Guaranteed Stability over Low-power Multi-hop Networks
Closing feedback loops fast and over long distances is key to emerging
applications; for example, robot motion control and swarm coordination require
update intervals of tens of milliseconds. Low-power wireless technology is
preferred for its low cost, small form factor, and flexibility, especially if
the devices support multi-hop communication. So far, however, feedback control
over wireless multi-hop networks has only been shown for update intervals on
the order of seconds. This paper presents a wireless embedded system that tames
imperfections impairing control performance (e.g., jitter and message loss),
and a control design that exploits the essential properties of this system to
provably guarantee closed-loop stability for physical processes with linear
time-invariant dynamics. Using experiments on a cyber-physical testbed with 20
wireless nodes and multiple cart-pole systems, we are the first to demonstrate
and evaluate feedback control and coordination over wireless multi-hop networks
for update intervals of 20 to 50 milliseconds.Comment: Accepted final version to appear in: 10th ACM/IEEE International
Conference on Cyber-Physical Systems (with CPS-IoT Week 2019) (ICCPS '19),
April 16--18, 2019, Montreal, QC, Canad
The Palomar Testbed Interferometer
The Palomar Testbed Interferometer (PTI) is a long-baseline infrared
interferometer located at Palomar Observatory, California. It was built as a
testbed for interferometric techniques applicable to the Keck Interferometer.
First fringes were obtained in July 1995. PTI implements a dual-star
architecture, tracking two stars simultaneously for phase referencing and
narrow-angle astrometry. The three fixed 40-cm apertures can be combined
pair-wise to provide baselines to 110 m. The interferometer actively tracks the
white-light fringe using an array detector at 2.2 um and active delay lines
with a range of +/- 38 m. Laser metrology of the delay lines allows for servo
control, and laser metrology of the complete optical path enables narrow-angle
astrometric measurements. The instrument is highly automated, using a
multiprocessing computer system for instrument control and sequencing.Comment: ApJ in Press (Jan 99) Fig 1 available from
http://huey.jpl.nasa.gov/~bode/ptiPicture.html, revised duging copy edi
Flexible Scheduling Methods and Tools for Real-Time Control Systems
This thesis deals with flexibility in the design of real-time control systems. By dynamic resource scheduling it is possible to achieve on-line adaptability and increased control performance under resource constraints. The approach requires simulation tools for control and real-time systems co-design. One approach to achieve flexibility in the run-time scheduling of control tasks is feedback scheduling, where resources are scheduled dynamically based on measurements of actual timing variations and control performance. An overview of feedback scheduling techniques for control systems is presented.A flexible strategy for implementation of model predictive control (MPC) is described. In MPC, the control signal in each sample is obtained by the solution of a constrained quadratic optimization problem. A termination criterion is derived that, unlike traditional MPC, takes the effects of computational delay into account in the optimization. A scheduling scheme is also described, where the MPC cost functions being minimized are used as dynamic task priorities for a set of MPC tasks. The MATLAB/Simulink-based simulator TrueTime is presented. TrueTime is a co-design tool that facilitates simulation of distributed real-time control systems, where the execution of controller tasks in a real-time kernel is simulated in parallel with network transmissions and the continuous-time plant dynamics. Using TrueTime it is possible to study the effects of CPU and network scheduling on control performance and to experiment with flexible scheduling techniques and compensation schemes. A general overview of the simulator is given and the event-based kernel implementation is described.TrueTime is used in two simulation case studies. The first emulates TCP on top of standard Ethernet to simulate networked control of a robot system. The second case study uses TrueTime to simulate a web server application. A feedback scheduling strategy for QoS control in the web server is described
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