This work develops robust diffusion recursiveleast-squaresalgorithms to mitigate the performance degradation often experienced in networks of agents in the presence of impulsive noise. The first algorithm minimizes ...
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This work develops robust diffusion recursiveleast-squaresalgorithms to mitigate the performance degradation often experienced in networks of agents in the presence of impulsive noise. The first algorithm minimizes an exponentially weighted least-squares cost function subject to a time-dependent constraint on the squared norm of the intermediate update at each node. A recursive strategy for computing the constraint is proposed using side information from the neighboring nodes to further improve the robustness. We also analyze the mean-square convergence behavior of the proposed algorithm. The second proposed algorithm is a modification of the first one based on the dichotomous coordinate descent iterations. It has a performance similar to that of the former, however, its complexity is significantly lower especially when input regressors of agents have a shift structure and it is well suited to practical implementation. Simulations show the superiority of the proposed algorithms over previously reported techniques in various impulsive noise scenarios.
Train basic resistance is important for the design of the automatic train operation, which influences the efficiency, punctuality, stop precision, energy consumption, and the safety of the train. The multi-innovation ...
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This paper presents a dynamic model for a differential drive mobile robot, including the actuators effects and a methodology to parameterize the model in a linear fashion, enabling the use of the recursiveleast squar...
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Control of a system exhibiting input multiplicity at an optimum (singular) operating point poses a challenging control problem due to loss of invertibility and change in the sign of the steady state gain in the neighb...
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This work explores the application of Adaptive Model Predictive Control (AMPC) to quadrotor altitude control. Model Predictive Control (MPC) is a very powerful method of Advanced Control, utilizing an implicit model t...
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Biodynamic feedthrough (BDFT) occurs when vehicle accelerations feed through the body of a human operator, causing involuntary limb motions, which in turn result in involuntary control inputs. Manual control of many d...
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This paper starts by reviewing the mathematical model for tumor growth as well as the pharmacokinetics and pharmacodynamics models of the drug, so that the therapy can be as close as possible to reality. A Nonlinear M...
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A novel method for nonlinear time-varying systems identification based on multi-dimensional Taylor network and variable forgetting factor recursiveleastsquares algorithm is proposed. In this paper, the connection we...
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A novel control system for an automotive semi-active suspension based on a Quarter of Vehicle (QoV) model is proposed. In typical conditions the mass of the vehicle body varies due to several variables, such as the nu...
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A decomposition-based recursiveleastsquares algorithm is developed for Wiener nonlinear systems described by finite impulse response moving average models. After transferring a finite impulse response moving average...
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A decomposition-based recursiveleastsquares algorithm is developed for Wiener nonlinear systems described by finite impulse response moving average models. After transferring a finite impulse response moving average (FIR-MA) model to a controlled autoregressive model, we compute the parameters by combining the decomposition principle and the leastsquares method and using the filtering idea. The simulation results validate the proposed algorithm.
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