Neural networks and genetic algorithms have been in the past successfully applied, separately, to controller tuning problems. In this paper we purpose to combine its joint use, by exploiting the nonlinear mapping capa...
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Neural networks and genetic algorithms have been in the past successfully applied, separately, to controller tuning problems. In this paper we purpose to combine its joint use, by exploiting the nonlinear mapping capabilities of neural networks to model objective functions, and to use them to supply their values to a genetic algorithm which performs on-line minimization. Simulation results show that this is a valid approach, offering desired properties for on-line use such as a dramatic reduction in computation time and avoiding the need of perturbing the closed-loop operation
Builds on the previous work by the authors (1998) by defining local versions of cross-positivity for vector fields, especially as related to shifted cones. The paper also furthers the development of a stability theory...
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Builds on the previous work by the authors (1998) by defining local versions of cross-positivity for vector fields, especially as related to shifted cones. The paper also furthers the development of a stability theory by introducing preliminary concepts of directed stability. The motivation is to apply this theory to analyzing the control of certain directionally constrained models, such as those found in materials processing.
A MATLAB-based rapid controller prototyping and development system for on-line tuning is presented. This is capable of automatically generating executable code for a digital signal processor from a Simulink specificat...
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A MATLAB-based rapid controller prototyping and development system for on-line tuning is presented. This is capable of automatically generating executable code for a digital signal processor from a Simulink specification of a controller and also has a real-time parameter adjustment and data logging facility. An optimisation problem can therefore be formulated in MATLAB with the controller parameters as decision variables and direct measures of the controller's performance as optimisation objectives. A multiobjective genetic algorithm is used as an optimisation engine to perform on-line tuning for the controller of an active magnetic bearing system. The optimisation objective function is based on on-line H ∞ and H 2 , measures of controller performance.
In computer vision, texture plays an important role. In this work we propose five human perceptual texture features heuristically extracted. Since a modeling can not be obtained from these features, we use a discrimin...
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In this paper we consider the problem of stabilizing the seeker scan loop mounted in a missile head. The system consist of a spin-stabilizing gyro-optics assembly and its driving signal processor. The model contains t...
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The underactuated free floating robot in space is nonlinear systems where velocity and acceleration constraints are both nonintegrable, therefore it is a second-order nonholonomic system. Some of the existing non-holo...
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The underactuated free floating robot in space is nonlinear systems where velocity and acceleration constraints are both nonintegrable, therefore it is a second-order nonholonomic system. Some of the existing non-holonomic control methods will not be directly applicable to such systems as it is extremely difficult, if not impossible, to find the control Lie brackets. In this paper, by investigating the system dynamics in dept., we propose a simple velocity-based method to control the unactuated joints and a multi-step composite strategy to implement orientation tracking tasks. The proposed algorithm is of significance in controlling of space robots when some joints fail to function, or they are intentionally set to be passive for energy efficiency and safety purposes.
Proposes a simple and effective method for building a fuzzy model from data. A three-layered RBF network is introduced to implement the fuzzy model. Differing from existing clustering-based methods, in this approach t...
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A fast and computationally efficient fuzzy clustering approach is presented. In this approach, fuzzy clustering is implemented in two hierarchical phases: subclusters generation by a self-organising network and fuzzy ...
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We give an algorithm for the single-stage maximization criterion to attain multichannel blind deconvolution. This criterion determines the coefficients of equalizers for all channels simultaneously. However, the origi...
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We give an algorithm for the single-stage maximization criterion to attain multichannel blind deconvolution. This criterion determines the coefficients of equalizers for all channels simultaneously. However, the original maximization criterion has many constrains so that it is difficult to directly implement it as a numerical algorithm. By exploiting pre-whitening and lattice representations of paraunitary systems, we can reduce the original maximization problem into a simple constraint-free one and then present a stochastic gradient algorithm.
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