51,323 research outputs found
Robust fault tolerant control framework using uncertain Takagi-Sugeno fuzzy models
This chapter is concerned with the introduction of a fault tolerant control (FTC) framework using uncertain Takagi-Sugeno
(FS) fuzzy models. Depending on how much information is available about the fault, the framework gives rise to passive FTC,
active FTC without controller reconfiguration and active FTC with controller reconfiguration. The design is performed using
a Linear Matrix Inequality (LMI)-based synthesis that directly takes into account the TS description of the system and its
uncertainties. An example based on a mobile robot is used to show the application of this methodologyPeer ReviewedPreprin
Synthesis for Constrained Nonlinear Systems using Hybridization and Robust Controllers on Simplices
In this paper, we propose an approach to controller synthesis for a class of
constrained nonlinear systems. It is based on the use of a hybridization, that
is a hybrid abstraction of the nonlinear dynamics. This abstraction is defined
on a triangulation of the state-space where on each simplex of the
triangulation, the nonlinear dynamics is conservatively approximated by an
affine system subject to disturbances. Except for the disturbances, this
hybridization can be seen as a piecewise affine hybrid system on simplices for
which appealing control synthesis techniques have been developed in the past
decade. We extend these techniques to handle systems subject to disturbances by
synthesizing and coordinating local robust affine controllers defined on the
simplices of the triangulation. We show that the resulting hybrid controller
can be used to control successfully the original constrained nonlinear system.
Our approach, though conservative, can be fully automated and is
computationally tractable. To show its effectiveness in practical applications,
we apply our method to control a pendulum mounted on a cart
Application of velocity-based gain-scheduling to lateral auto-pilot design for an agile missile
In this paper a modern gain-scheduling methodology is proposed which exploits recently developed velocity-based techniques to resolve many of the deficiencies of classical gain-scheduling approaches (restriction to near equilibrium operation, to slow rate of variation). This is achieved while maintaining continuity with linear methods and providing an open design framework (any linear synthesis approach may be used) which supports divide and conquer design strategies. The application of velocity-based gain-scheduling techniques is demonstrated in application to a demanding, highly nonlinear, missile control design task. Scheduling on instantaneous incidence (a rapidly varying quantity) is well-known to lead to considerable difficulties with classical gain-scheduling methods. It is shown that the methods proposed here can, however, be used to successfully design an effective and robust gain-scheduled controller
Plug-and-play and coordinated control for bus-connected AC islanded microgrids
This paper presents a distributed control architecture for voltage and
frequency stabilization in AC islanded microgrids. In the primary control
layer, each generation unit is equipped with a local controller acting on the
corresponding voltage-source converter. Following the plug-and-play design
approach previously proposed by some of the authors, whenever the
addition/removal of a distributed generation unit is required, feasibility of
the operation is automatically checked by designing local controllers through
convex optimization. The update of the voltage-control layer, when units plug
-in/-out, is therefore automatized and stability of the microgrid is always
preserved. Moreover, local control design is based only on the knowledge of
parameters of power lines and it does not require to store a global microgrid
model. In this work, we focus on bus-connected microgrid topologies and enhance
the primary plug-and-play layer with local virtual impedance loops and
secondary coordinated controllers ensuring bus voltage tracking and reactive
power sharing. In particular, the secondary control architecture is
distributed, hence mirroring the modularity of the primary control layer. We
validate primary and secondary controllers by performing experiments with
balanced, unbalanced and nonlinear loads, on a setup composed of three
bus-connected distributed generation units. Most importantly, the stability of
the microgrid after the addition/removal of distributed generation units is
assessed. Overall, the experimental results show the feasibility of the
proposed modular control design framework, where generation units can be
added/removed on the fly, thus enabling the deployment of virtual power plants
that can be resized over time
Robustness analysis of evolutionary controller tuning using real systems
A genetic algorithm (GA) presents an excellent method for controller parameter tuning. In our work, we evolved the heading as well as the altitude controller for a small lightweight helicopter. We use the real flying robot to evaluate the GA's individuals rather than an artificially consistent simulator. By doing so we avoid the ldquoreality gaprdquo, taking the controller from the simulator to the real world. In this paper we analyze the evolutionary aspects of this technique and discuss the issues that need to be considered for it to perform well and result in robust controllers
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
Evolving robust and specialized car racing skills
Neural network-based controllers are evolved for racing simulated R/C cars around several tracks of varying difficulty. The transferability of driving skills acquired when evolving for a single track is evaluated, and different ways of evolving controllers able to perform well on many different tracks are investigated. It is further shown that such generally proficient controllers can reliably be developed into specialized controllers for individual tracks. Evolution of sensor parameters together with network weights is shown to lead to higher final fitness, but only if turned on after a general controller is developed, otherwise it hinders evolution. It is argued that simulated car racing is a scalable and relevant testbed for evolutionary robotics research, and that the results of this research can be useful for commercial computer games
- …