945 research outputs found
Performance-based control system design automation via evolutionary computing
This paper develops an evolutionary algorithm (EA) based methodology for computer-aided control system design (CACSD)
automation in both the time and frequency domains under performance satisfactions. The approach is automated by efficient
evolution from plant step response data, bypassing the system identification or linearization stage as required by conventional
designs. Intelligently guided by the evolutionary optimization, control engineers are able to obtain a near-optimal ‘‘off-thecomputer’’
controller by feeding the developed CACSD system with plant I/O data and customer specifications without the need of
a differentiable performance index. A speedup of near-linear pipelineability is also observed for the EA parallelism implemented on
a network of transputers of Parsytec SuperCluster. Validation results against linear and nonlinear physical plants are convincing,
with good closed-loop performance and robustness in the presence of practical constraints and perturbations
System Level Synthesis
This article surveys the System Level Synthesis framework, which presents a
novel perspective on constrained robust and optimal controller synthesis for
linear systems. We show how SLS shifts the controller synthesis task from the
design of a controller to the design of the entire closed loop system, and
highlight the benefits of this approach in terms of scalability and
transparency. We emphasize two particular applications of SLS, namely
large-scale distributed optimal control and robust control. In the case of
distributed control, we show how SLS allows for localized controllers to be
computed, extending robust and optimal control methods to large-scale systems
under practical and realistic assumptions. In the case of robust control, we
show how SLS allows for novel design methodologies that, for the first time,
quantify the degradation in performance of a robust controller due to model
uncertainty -- such transparency is key in allowing robust control methods to
interact, in a principled way, with modern techniques from machine learning and
statistical inference. Throughout, we emphasize practical and efficient
computational solutions, and demonstrate our methods on easy to understand case
studies.Comment: To appear in Annual Reviews in Contro
The design of nonovershooting and nonundershooting multivariable state feedback tracking controllers
We consider the use of linear multivariable feedback control to achieve a nonovershooting and nonundershooting step response. Recently, Schmid and Ntogramatzidis (2010) [13] introduced a linear state feedback controller design method to avoid overshoot. In this paper, we describe conditions under which the design method may be modified to avoid undershoot. The method is applicable to square and nonsquare systems, minimum and nonminimum phase systems, and also strictly proper and bi-proper systems
Nonovershooting and nonundershooting exact output regulation
We consider the classic problem of exact output regulation for a linear time invariant plant. Under the assumption that either a state feedback or measurement feedback output regulator exists, we give design methods to obtain a regulator that avoids overshoot and undershoot in the transient response
Robust nonlinear control of vectored thrust aircraft
An interdisciplinary program in robust control for nonlinear systems with applications to a variety of engineering problems is outlined. Major emphasis will be placed on flight control, with both experimental and analytical studies. This program builds on recent new results in control theory for stability, stabilization, robust stability, robust performance, synthesis, and model reduction in a unified framework using Linear Fractional Transformations (LFT's), Linear Matrix Inequalities (LMI's), and the structured singular value micron. Most of these new advances have been accomplished by the Caltech controls group independently or in collaboration with researchers in other institutions. These recent results offer a new and remarkably unified framework for all aspects of robust control, but what is particularly important for this program is that they also have important implications for system identification and control of nonlinear systems. This combines well with Caltech's expertise in nonlinear control theory, both in geometric methods and methods for systems with constraints and saturations
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
- …