27,066 research outputs found
A review of convex approaches for control, observation and safety of linear parameter varying and Takagi-Sugeno systems
This paper provides a review about the concept of convex systems based on Takagi-Sugeno, linear parameter varying (LPV) and quasi-LPV modeling. These paradigms are capable of hiding the nonlinearities by means of an equivalent description which uses a set of linear models interpolated by appropriately defined weighing functions. Convex systems have become very popular since they allow applying extended linear techniques based on linear matrix inequalities (LMIs) to complex nonlinear systems. This survey aims at providing the reader with a significant overview of the existing LMI-based techniques for convex systems in the fields of control, observation and safety. Firstly, a detailed review of stability, feedback, tracking and model predictive control (MPC) convex controllers is considered. Secondly, the problem of state estimation is addressed through the design of proportional, proportional-integral, unknown input and descriptor observers. Finally, safety of convex systems is discussed by describing popular techniques for fault diagnosis and fault tolerant control (FTC).Peer ReviewedPostprint (published version
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
Bulk Scheduling with the DIANA Scheduler
Results from the research and development of a Data Intensive and Network
Aware (DIANA) scheduling engine, to be used primarily for data intensive
sciences such as physics analysis, are described. In Grid analyses, tasks can
involve thousands of computing, data handling, and network resources. The
central problem in the scheduling of these resources is the coordinated
management of computation and data at multiple locations and not just data
replication or movement. However, this can prove to be a rather costly
operation and efficient sing can be a challenge if compute and data resources
are mapped without considering network costs. We have implemented an adaptive
algorithm within the so-called DIANA Scheduler which takes into account data
location and size, network performance and computation capability in order to
enable efficient global scheduling. DIANA is a performance-aware and
economy-guided Meta Scheduler. It iteratively allocates each job to the site
that is most likely to produce the best performance as well as optimizing the
global queue for any remaining jobs. Therefore it is equally suitable whether a
single job is being submitted or bulk scheduling is being performed. Results
indicate that considerable performance improvements can be gained by adopting
the DIANA scheduling approach.Comment: 12 pages, 11 figures. To be published in the IEEE Transactions in
Nuclear Science, IEEE Press. 200
Multiple Loop Self-Triggered Model Predictive Control for Network Scheduling and Control
We present an algorithm for controlling and scheduling multiple linear
time-invariant processes on a shared bandwidth limited communication network
using adaptive sampling intervals. The controller is centralized and computes
at every sampling instant not only the new control command for a process, but
also decides the time interval to wait until taking the next sample. The
approach relies on model predictive control ideas, where the cost function
penalizes the state and control effort as well as the time interval until the
next sample is taken. The latter is introduced in order to generate an adaptive
sampling scheme for the overall system such that the sampling time increases as
the norm of the system state goes to zero. The paper presents a method for
synthesizing such a predictive controller and gives explicit sufficient
conditions for when it is stabilizing. Further explicit conditions are given
which guarantee conflict free transmissions on the network. It is shown that
the optimization problem may be solved off-line and that the controller can be
implemented as a lookup table of state feedback gains. Simulation studies which
compare the proposed algorithm to periodic sampling illustrate potential
performance gains.Comment: Accepted for publication in IEEE Transactions on Control Systems
Technolog
A Practical Cooperative Multicell MIMO-OFDMA Network Based on Rank Coordination
An important challenge of wireless networks is to boost the cell edge
performance and enable multi-stream transmissions to cell edge users.
Interference mitigation techniques relying on multiple antennas and
coordination among cells are nowadays heavily studied in the literature.
Typical strategies in OFDMA networks include coordinated scheduling,
beamforming and power control. In this paper, we propose a novel and practical
type of coordination for OFDMA downlink networks relying on multiple antennas
at the transmitter and the receiver. The transmission ranks, i.e.\ the number
of transmitted streams, and the user scheduling in all cells are jointly
optimized in order to maximize a network utility function accounting for
fairness among users. A distributed coordinated scheduler motivated by an
interference pricing mechanism and relying on a master-slave architecture is
introduced. The proposed scheme is operated based on the user report of a
recommended rank for the interfering cells accounting for the receiver
interference suppression capability. It incurs a very low feedback and backhaul
overhead and enables efficient link adaptation. It is moreover robust to
channel measurement errors and applicable to both open-loop and closed-loop
MIMO operations. A 20% cell edge performance gain over uncoordinated LTE-A
system is shown through system level simulations.Comment: IEEE Transactions or Wireless Communications, Accepted for
Publicatio
Smart Microgrids: Overview and Outlook
The idea of changing our energy system from a hierarchical design into a set
of nearly independent microgrids becomes feasible with the availability of
small renewable energy generators. The smart microgrid concept comes with
several challenges in research and engineering targeting load balancing,
pricing, consumer integration and home automation. In this paper we first
provide an overview on these challenges and present approaches that target the
problems identified. While there exist promising algorithms for the particular
field, we see a missing integration which specifically targets smart
microgrids. Therefore, we propose an architecture that integrates the presented
approaches and defines interfaces between the identified components such as
generators, storage, smart and \dq{dumb} devices.Comment: presented at the GI Informatik 2012, Braunschweig Germany, Smart Grid
Worksho
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