Using unbounded time-varying scaling of the states we design C/sup 1/ feedback laws for power integrator triangular systems which globally asymptotically stabilize (GAS) the origin despite the uncontrollability of the...
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Using unbounded time-varying scaling of the states we design C/sup 1/ feedback laws for power integrator triangular systems which globally asymptotically stabilize (GAS) the origin despite the uncontrollability of the linearization. With bounded scaling the feedback laws achieve global practical stability (GPS). For a trade-off between GAS/GPS of the origin and unboundedness/boundedness of the scaling we construct a dynamic version of these feedback laws.
This paper provides a model predictive approach to control switched reluctance motors (SRM's). A local linear neuro-fuzzy model is used to model SRM. Then a predictive control schema is devised considering an appr...
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In this paper a model reference variable structure controller (VSC) for an active suspension system is designed. A half vehicle model is used in which, the vertical and pitch motions of the mass supported by the suspe...
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In the practical path-following problem formulated in this paper, it is required that the error between the system output and the desired geometric path be less then any prespecified constant. If in a nonlinear MIMO s...
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In the practical path-following problem formulated in this paper, it is required that the error between the system output and the desired geometric path be less then any prespecified constant. If in a nonlinear MIMO system the output derivatives do not enter the zero dynamics, a geometric condition on the path is given under which a solution to this problem exists. The solution is obtained by combining input-to-state stability and switched-system methodology.
We present robust stability results for discrete-time nonlinear systems using certainty equivalence output feedback, particularly those that employ a model predictive control (MPC) formulation to generate the feedback...
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We present robust stability results for discrete-time nonlinear systems using certainty equivalence output feedback, particularly those that employ a model predictive control (MPC) formulation to generate the feedback control law. To this end, we discuss nominal robustness properties of general discrete-time nonlinear systems, including those that use discontinuous control laws in the feedback loop. This is important for systems employing MPC since the method can, and sometimes necessarily does, result in discontinuous control laws. Coupling assumptions of nominal robustness with certain uniform observability or detectability assumptions (for each of which we give an observer), we assert that, in particular, MPC is robustly globally asymptotically stabilizing when used in a certainty equivalence output feedback structure. Finally, we give an example to illuminate our results.
For comments by R.J. Mantz and H. De Battisa see ibid. (vol. 51, p. 736-38, June 2004) . For original paper by A. S. Hodel and C. E. Hall see ibid.(vol. 48, p. 442-51, Apr. 2001).
For comments by R.J. Mantz and H. De Battisa see ibid. (vol. 51, p. 736-38, June 2004) . For original paper by A. S. Hodel and C. E. Hall see ibid.(vol. 48, p. 442-51, Apr. 2001).
Magnetic resonance imaging (MRI) is a widely used approach to obtaining high quality medical images of the brain. Post-processing MRI images with segmentation algorithms enhances the visualization and measurement of s...
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ISBN:
(纸本)188933524X
Magnetic resonance imaging (MRI) is a widely used approach to obtaining high quality medical images of the brain. Post-processing MRI images with segmentation algorithms enhances the visualization and measurement of soft tissues and lesions. Segmented brain images contain information amenable to quantitative analysis (e.g., tissue component percentage in a region of interest (ROI)) and diagnostic interpretation (e.g., total lesion volume). A number of different segmentation algorithms have been developed for this purpose. In this paper, we propose a novel automated segmentation technique, hierarchical structure weighted probabilistic neural network (HSWPNN), based on multi-scale feature extraction, hierarchical labeling structure, and a modified weighted probabilistic neural network (PNN). Compared to other clustering algorithms, our method is relatively robust to noise and accurate. We compare our results to a model of ground truth.
A hierarchical fuzzy expert system is proposed for multispectral Landsat image classification to overcome difficulties with conventional maximum-likelihood classifier (MLC) based on normal distribution and easily inco...
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A hierarchical fuzzy expert system is proposed for multispectral Landsat image classification to overcome difficulties with conventional maximum-likelihood classifier (MLC) based on normal distribution and easily incorporate other collateral data, such as vegetation index, digital elevation model, etc. The hierarchical structure is to reduce fuzzy rules to incorporate as many useful data sources as possible. Adaptive-Neural-Network Based Fuzzy Inference System (ANFIS) is used to build up fuzzy rule based systems to adapt training data. The expert system is tested for the classification on Landsat 7 ETM+ image and results are effective for multispectral image classification.
The purpose of this paper is to design a high performance sliding mode controller through the use of a new switching function. This method uses the idea of boundary layer sliding mode while taking the boundary layer w...
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The purpose of this paper is to design a high performance sliding mode controller through the use of a new switching function. This method uses the idea of boundary layer sliding mode while taking the boundary layer width as a function of the angle between the state trajectory and the sliding surface which we call approach angle. By incorporating the approach angle into the switching function, the overall sliding mode controller guarantees the asymptotical stability of the system while having only a slight amount of chattering. This method overcomes the shortcomings of a pure discontinuous switching such as excessive chattering, while maintaining its benefit that is the asymptotical stability is guaranteed. The proposed method is used to control an inverted pendulum. It is also compared with a pure switching function. Simulation results show that the new sliding mode controller has good control performance with negligible chattering.
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