The main objective of this paper is to show how one can benefit from using Iterative Learning control instead of conventional feedback control. As a main result it is shown that even if the nominal plant satisfies a g...
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The main objective of this paper is to show how one can benefit from using Iterative Learning control instead of conventional feedback control. As a main result it is shown that even if the nominal plant satisfies a given uncertainty condition, there always exists ILC algorithms that can drive the tracking error monotonically to zero. This same result cannot be achieved with conventional feedback control, or by inverting a nominal model of the plant. Hence ILC offers an unique tool to invert dynamical systems with uncertainty.
It is well-known that for linear systems internal asymptotic stability implies external stability in the sense that when the external input is in L/sub p/ then also the state will be in L/sub p/. However, for the cont...
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It is well-known that for linear systems internal asymptotic stability implies external stability in the sense that when the external input is in L/sub p/ then also the state will be in L/sub p/. However, for the control of linear systems with saturation where the controlled system is nonlinear this implication is no longer directly applicable. Several people have studied the effect of external inputs in L/sub p/ either directly or in the context of ISS as introduced by Sontag. In this paper we study the effect of external stochastic disturbances on linear systems with input saturation and we establish that when we can achieve internal global asymptotic stability then we can also achieve a bounded variance for the state.
UTMAC is an object-oriented C++ class library, developed for design, implementation and simulation of multi-agent controllers. Using UTMAC, the control problem under analysis should be decomposed into several partial ...
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UTMAC is an object-oriented C++ class library, developed for design, implementation and simulation of multi-agent controllers. Using UTMAC, the control problem under analysis should be decomposed into several partial sub-problems to be solved by controller-agents, which can be implemented as reusable entities. UTMAC uses a non-centralized simulation scheme in which cash agent simulates itself. In this paper the structure of UTMAC is explored and design of a simple reusable multi-agent controller is provided to illustrate the work and show the convenience of design and implementation.
In an Internet-based control system, particular human operations may violate desired requirements and lead to destructive failure. For such human-in-the-loop systems, this paper extends the remote supervisory scheme b...
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ISBN:
(纸本)4907764227
In an Internet-based control system, particular human operations may violate desired requirements and lead to destructive failure. For such human-in-the-loop systems, this paper extends the remote supervisory scheme by Lee and Hsu (2003) to a modular one so as to reduce the supervisor synthesis complexity. Also, remote human issued commands are guaranteed to meet required specifications. A rapid thermal process in semiconductor manufacturing systems is provided to show the practicability of the proposed approach.
This study is an effort to give a practical solution in the problem of optimizing the structure of the hierarchical mixture of experts model, which is a natural extension of the associative Gaussian mixture of experts...
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This study is an effort to give a practical solution in the problem of optimizing the structure of the hierarchical mixture of experts model, which is a natural extension of the associative Gaussian mixture of experts system. We present two novel methods for optimizing such structures using genetic algorithms. Special concern is taken for reducing the computational time so as to efficiently allow the structure to "grow" while it evolves with the genetic algorithm. The main contribution of the paper lies on the efficient, topologically oriented, representations of such architectures so as to be optimized through involving genetic algorithms.
Three individual experts have been developed based on extended auto associative neural networks (E-AANN), Kohonen self organizing maps (KSOM), and the radial basis function based clustering (RBFC) algorithms. An integ...
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Three individual experts have been developed based on extended auto associative neural networks (E-AANN), Kohonen self organizing maps (KSOM), and the radial basis function based clustering (RBFC) algorithms. An integrated method is proposed later to combine the set of individual experts managed by a gated experts algorithm, which assigns the experts based on their best performance regions. We have used a Matlab Simulink model of a chiller system and applied the individual experts and the integrated method to detect and recover sensor errors. It has been shown that the integrated method gets better performance in diagnostics and prognostics compared with each individual expert.
Recently, a novel optimality based Repetitive control algorithm was proposed in (Hätönen et al. , 2003). According to the convergence analysis carried out in that paper, the algorithm will result in asymptot...
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Recently, a novel optimality based Repetitive control algorithm was proposed in (Hätönen et al. , 2003). According to the convergence analysis carried out in that paper, the algorithm will result in asymptotic convergence for an arbitrary discrete-time LTI plant and a T -periodic reference. However, the performance of the algorithm was tested only using simulation studies. In order to rigorously test how the algorithm performs with real systems, in this paper the algorithm is implemented on a non-minimum phase spring-mass-damper system. The results are very satisfactory, because the algorithm results in near perfect tracking with this rather demanding plant type.
In this paper, a new model inverse optimal iterative learning control algorithm is practically implemented on an industrial gantry robot. The algorithm has only one tuning parameter which can be adjusted to provide a ...
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In this paper, a new model inverse optimal iterative learning control algorithm is practically implemented on an industrial gantry robot. The algorithm has only one tuning parameter which can be adjusted to provide a balance between convergence speed and robustness. Results show that the algorithm is capable of learning the required trajectory in very few iterations. However at this convergence rate the lack of robustness is a major issue. Appropriate use of the tuning parameter is shown to greatly increase the algorithm robustness as demonstrated by tests which successfully reach 600 iterations.
The theory of self-concordant barriers was introduced by Nesterov and Nemirovskii (1994) in the context of interior-point methods for convex optimisation. Their development is general, elegant and enjoys widespread im...
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The theory of self-concordant barriers was introduced by Nesterov and Nemirovskii (1994) in the context of interior-point methods for convex optimisation. Their development is general, elegant and enjoys widespread implementation in state-of-the-art algorithms. In this paper we exploit the theory of self-concordant functions with application to nonlinear MPC. In particular we construct an invariant terminal constraint set via properties of self-concordant functions. We also extend earlier results on recent red barrier function MPC to nonlinear MPC (model predictive control) with state constraints. We show nominal closed-loop stability for a wide class of nonlinear systems under full state feedback.
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