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This paper introduces methods of deriving and computing maximal robust positively invariant sets for linear discrete time systems with additive model uncertainty. Two types of uncertainty are considered: state dependent uncertainty, which can handle multiplicative parametric model uncertainty as well as linearisation errors for nonlinear systems, and scaled sets of uncertainty. We provide a framework...
In this paper we present a method to segment RGBD image of a scene into coherent and meaningful parts using both the appearance features and depth information. The segmentation method is totally based on graph cuts theory which uses our proposed unsupervised Conditional Random Field (CRF) model. We evaluate our method both quantitatively and qualitatively on a set of RGBD images of NYU dataset. The...
This paper investigates the robustness with plant parameter variation of a sliding mode controller (SMC) that was recently proposed. Nonlinear chaotic systems are used as they are sensitive to parameter change and initial conditions. Synchronization of chaotic systems is used as it solves the tracking problem in a dynamic environment. The convergence time as a measure of robustness is also observed...
In this paper, a fast tube-based algorithm is proposed for robust model predictive control (RMPC) using an uncertain finite step response (FSR) input-output model with time-invariant bounded uncertainty. The use of an FSR model, in place of the high-dimensional state-space model, allows the assurance of good performance while dramatically reducing the online cost of the controller. Bounds on the uncertain...
This paper presents a method to design a predictive controller for a Rotational Inverted Pendulum (RIP) system. The design goal is to balance the pendulum in the inverted position. After linearization, the model of RIP system is transformed to a linear polytopic system with bounded disturbances and a new robust model predictive control (RMPC) strategy is developed for this system. Simulation results...
Hidden Markov models using Gaussian mixture models as their hidden state distributions have been successfully applied in text-dependent speaker identification applications. Nevertheless, it is well-known that Gaussian mixture models are very vulnerable to the presence of outliers in the fitting set used for their estimation. Student's-t mixture models have been proposed recently as a heavy-tailed,...
A complexity theory for unbounded fan-in parallelism is developed where the complexity measure is the simultaneous measure (number of processors, parallel time). Two models of unbounded fan-in parallelism are (1) parallel random access machines that allow simultaneous reading from or writing to the same common memory location, and (2) circuits containing AND's, OR's and NOT's with no bound placed...
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