22,021 research outputs found
Optimal and Robust Design of Integrated Control and Diagnostic Modules
The problem of designing an integrated control and diagnostic module is considered. The four degree of freedom controller is recast into a general framework wherein results from optimal and robust control theory can be easily implemented. For the case of an H2 objective, it is shown that the optimal control-diagnostic module involves constructing an optimal controller, closing the loop with this controller, and then designing an optimal diagnostic module for the closed loop. When uncertain plants are involved, this two-step method does not lead to reasonable diagnostics, and the control and diagnostic modules must be synthesized simultaneously. An example shows how this design can be accomplished with available methods
Modeling and Control of the Automated Radiator Inspection Device
Many of the operations performed at the Kennedy Space Center (KSC) are dangerous and repetitive tasks which make them ideal candidates for robotic applications. For one specific application, KSC is currently in the process of designing and constructing a robot called the Automated Radiator Inspection Device (ARID), to inspect the radiator panels on the orbiter. The following aspects of the ARID project are discussed: modeling of the ARID; design of control algorithms; and nonlinear based simulation of the ARID. Recommendations to assist KSC personnel in the successful completion of the ARID project are given
Mathematical control of complex systems 2013
Mathematical control of complex systems have already become an ideal research area for control engineers, mathematicians, computer scientists, and biologists to understand, manage, analyze, and interpret functional information/dynamical behaviours from real-world complex dynamical systems, such as communication systems, process control, environmental systems, intelligent manufacturing systems, transportation systems, and structural systems. This special issue aims to bring together the latest/innovative knowledge and advances in mathematics for handling complex systems. Topics include, but are not limited to the following: control systems theory (behavioural systems, networked control systems, delay systems, distributed systems, infinite-dimensional systems, and positive systems); networked control (channel capacity constraints, control over communication networks, distributed filtering and control, information theory and control, and sensor networks); and stochastic systems (nonlinear filtering, nonparametric methods, particle filtering, partial identification, stochastic control, stochastic realization, system identification)
Reduced Order Controller Design for Robust Output Regulation
We study robust output regulation for parabolic partial differential
equations and other infinite-dimensional linear systems with analytic
semigroups. As our main results we show that robust output tracking and
disturbance rejection for our class of systems can be achieved using a
finite-dimensional controller and present algorithms for construction of two
different internal model based robust controllers. The controller parameters
are chosen based on a Galerkin approximation of the original PDE system and
employ balanced truncation to reduce the orders of the controllers. In the
second part of the paper we design controllers for robust output tracking and
disturbance rejection for a 1D reaction-diffusion equation with boundary
disturbances, a 2D diffusion-convection equation, and a 1D beam equation with
Kelvin-Voigt damping.Comment: Revised version with minor improvements and corrections. 28 pages, 9
figures. Accepted for publication in the IEEE Transactions on Automatic
Contro
A fractional representation approach to the robust regulation problem for MIMO systems
The aim of this paper is in developing unifying frequency domain theory for
robust regulation of MIMO systems. The main theoretical results achieved are a
new formulation of the internal model principle, solvability conditions for the
robust regulation problem, and a parametrization of all robustly regulating
controllers. The main results are formulated with minimal assumptions and
without using coprime factorizations thus guaranteeing applicability with a
very general class of systems. In addition to theoretical results, the design
of robust controllers is addressed. The results are illustrated by two examples
involving a delay and a heat equation.Comment: 23 pages, 3 figures, submitted to International Journal of Robust and
Nonlinear Contro
PAC: A Novel Self-Adaptive Neuro-Fuzzy Controller for Micro Aerial Vehicles
There exists an increasing demand for a flexible and computationally
efficient controller for micro aerial vehicles (MAVs) due to a high degree of
environmental perturbations. In this work, an evolving neuro-fuzzy controller,
namely Parsimonious Controller (PAC) is proposed. It features fewer network
parameters than conventional approaches due to the absence of rule premise
parameters. PAC is built upon a recently developed evolving neuro-fuzzy system
known as parsimonious learning machine (PALM) and adopts new rule growing and
pruning modules derived from the approximation of bias and variance. These rule
adaptation methods have no reliance on user-defined thresholds, thereby
increasing the PAC's autonomy for real-time deployment. PAC adapts the
consequent parameters with the sliding mode control (SMC) theory in the
single-pass fashion. The boundedness and convergence of the closed-loop control
system's tracking error and the controller's consequent parameters are
confirmed by utilizing the LaSalle-Yoshizawa theorem. Lastly, the controller's
efficacy is evaluated by observing various trajectory tracking performance from
a bio-inspired flapping-wing micro aerial vehicle (BI-FWMAV) and a rotary wing
micro aerial vehicle called hexacopter. Furthermore, it is compared to three
distinctive controllers. Our PAC outperforms the linear PID controller and
feed-forward neural network (FFNN) based nonlinear adaptive controller.
Compared to its predecessor, G-controller, the tracking accuracy is comparable,
but the PAC incurs significantly fewer parameters to attain similar or better
performance than the G-controller.Comment: This paper has been accepted for publication in Information Science
Journal 201
Process operating mode monitoring : switching online the right controller
This paper presents a structure which deals with
process operating mode monitoring and allows the control law reconfiguration
by switching online the right controller. After a short
review of the advances in switching based control systems during
the last decade, we introduce our approach based on the definition
of operating modes of a plant. The control reconfiguration
strategy is achieved by online selection of an adequate controller,
in a case of active accommodation. The main contribution lies
in settling up the design steps of the multicontroller structure
and its accurate integration in the operating mode detection and
accommodation loop. Simulation results show the effectiveness
of the operating mode detection and accommodation (OMDA)
structure for which the design steps propose a method to study the
asymptotic stability, switching performances improvement, and
the tuning of the multimodel based detector
A passivity approach to controller-observer design for robots
Passivity-based control methods for robots, which achieve the control objective by reshaping the robot system's natural energy via state feedback, have, from a practical point of view, some very attractive properties. However, the poor quality of velocity measurements may significantly deteriorate the control performance of these methods. In this paper the authors propose a design strategy that utilizes the passivity concept in order to develop combined controller-observer systems for robot motion control using position measurements only. To this end, first a desired energy function for the closed-loop system is introduced, and next the controller-observer combination is constructed such that the closed-loop system matches this energy function, whereas damping is included in the controller- observer system to assure asymptotic stability of the closed-loop system. A key point in this design strategy is a fine tuning of the controller and observer structure to each other, which provides solutions to the output-feedback robot control problem that are conceptually simple and easily implementable in industrial robot applications. Experimental tests on a two-DOF manipulator system illustrate that the proposed controller-observer systems enable the achievement of higher performance levels compared to the frequently used practice of numerical position differentiation for obtaining a velocity estimat
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