8,095 research outputs found
Multirate sampled-data yaw-damper and modal suppression system design
A multirate control law synthesized algorithm based on an infinite-time quadratic cost function, was developed along with a method for analyzing the robustness of multirate systems. A generalized multirate sampled-data control law structure (GMCLS) was introduced. A new infinite-time-based parameter optimization multirate sampled-data control law synthesis method and solution algorithm were developed. A singular-value-based method for determining gain and phase margins for multirate systems was also developed. The finite-time-based parameter optimization multirate sampled-data control law synthesis algorithm originally intended to be applied to the aircraft problem was instead demonstrated by application to a simpler problem involving the control of the tip position of a two-link robot arm. The GMCLS, the infinite-time-based parameter optimization multirate control law synthesis method and solution algorithm, and the singular-value based method for determining gain and phase margins were all demonstrated by application to the aircraft control problem originally proposed for this project
A Periodic Systems Toolbox for MATLAB
The recently developed Periodic Systems Toolbox for MATLAB is described. The basic approach to develop this toolbox was to exploit the powerful object manipulation features of MATLAB via flexible andfunctionally rich high level m-functions, while simultaneously enforcing highly efficient and numerically sound computations via the mex-function technology of MATLAB to solve critical numerical problems.The m-functions based user interfaces ensure user-friendliness in operating with the functions of this toolbox via an object oriented approach to handle periodic system descriptions. The mex-functions are based on Fortran implementations of recently developed structure exploiting and structure preserving numerical algorithms for periodic systems which completely avoid forming of lifted representations
Stabilization of systems with asynchronous sensors and controllers
We study the stabilization of networked control systems with asynchronous
sensors and controllers. Offsets between the sensor and controller clocks are
unknown and modeled as parametric uncertainty. First we consider multi-input
linear systems and provide a sufficient condition for the existence of linear
time-invariant controllers that are capable of stabilizing the closed-loop
system for every clock offset in a given range of admissible values. For
first-order systems, we next obtain the maximum length of the offset range for
which the system can be stabilized by a single controller. Finally, this bound
is compared with the offset bounds that would be allowed if we restricted our
attention to static output feedback controllers.Comment: 32 pages, 6 figures. This paper was partially presented at the 2015
American Control Conference, July 1-3, 2015, the US
3 sampled-data control of nonlinear systems
This chapter provides some of the main ideas resulting from recent developments in sampled-data control of nonlinear systems. We have tried to bring the basic parts of the new developments within the comfortable grasp of graduate students. Instead of presenting the more general results that are available in the literature, we opted to present their less general versions that are easier to understand and whose proofs are easier to follow. We note that some of the proofs we present have not appeared in the literature in this simplified form. Hence, we believe that this chapter will serve as an important reference for students and researchers that are willing to learn about this area of research
Stochastic Stability of Event-triggered Anytime Control
We investigate control of a non-linear process when communication and
processing capabilities are limited. The sensor communicates with a controller
node through an erasure channel which introduces i.i.d. packet dropouts.
Processor availability for control is random and, at times, insufficient to
calculate plant inputs. To make efficient use of communication and processing
resources, the sensor only transmits when the plant state lies outside a
bounded target set. Control calculations are triggered by the received data. If
a plant state measurement is successfully received and while the processor is
available for control, the algorithm recursively calculates a sequence of
tentative plant inputs, which are stored in a buffer for potential future use.
This safeguards for time-steps when the processor is unavailable for control.
We derive sufficient conditions on system parameters for stochastic stability
of the closed loop and illustrate performance gains through numerical studies.Comment: IEEE Transactions on Automatic Control, under revie
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