Abstract We present a new approach—the estimator—to estimation of asymptotic variance in sequential Monte Carlo methods, or, particle filters. The method, which adjusts adaptively the lag estimator proposed Olsson and Douc (Bernoulli 25(2):1504–1535) applies to very general distribution flows filters, including auxiliary filters with adaptive resampling. algorithm operates entirely online, sens...

This is where is the estimated posteriori probability that xj belongs to term i, is the estimated mixing coefficient, is the estimated mean, and is the estimated variance of the ith term. We have written our estimate as to explicitly draw attention to the dependence of it on the estimated parameter set . The reader is referred to the works of McLachlan, G.J., and Basford, K.E. (1988) and Titf̂ x...

The authors consider the problem of estimating the density g of independent and identically distributed variables Xi, from a sample Z1, . . . , Zn where Zi = Xi + σεi, i = 1, . . . , n, ε is a noise independent of X, with σε having known distribution. They present a model selection procedure allowing to construct an adaptive estimator of g and to find non-asymptotic bounds for its L2(R)-risk. T...

Stripper columns are used for sweetening crude oil, and they must hold product hydrogen sulfide content as near the set points as possible in the faces of upsets. Since product    quality cannot be measured easily and economically online, the control of product quality is often achieved by maintaining a suitable tray temperature near its set point. Tray temperature control method, however, is n...

A fundamental question about model predictive control (MPC) is its robustness to model uncertainty. In this paper, we present a robust constrained output feedback MPC algorithm that can stabilize plants with both polytopic uncertainty and norm-bound uncertainty. The design procedure involves off-line design of a robust constrained state feedback MPC law and a state estimator using linear matrix...

model predictive controller is widely used in industrial plants. uncertainty is one of the critical issues in real systems. in this paper, the direct adaptive simplified model predictive control (smpc) is proposed for unknown or time varying plants with uncertainties. by estimating the plant step response in each sample, the controller is designed and the controller coefficients are directly ca...

This paper relates generally to sliding mode control (SMC) system based on uncertainty and disturbance estimation (UDE) and more particularly to a system, with the presence of high frequency measurement noise and unmodeled dynamics. Higher order dynamics are difficult to identify and remain excluded from theoretical model of the system, generally the main cause of instability. The presence of u...

Bit error rate (BER) performance of DS-CDMA receiver which consists of a RAKE receiver followed an adaptive MLSE diversity receiver is proposed to simultaneously suppress multi user interferences and multi path fading interferences. A new adaptive equalization in the channel estimator and predictor of MLSE receiver is introduced. RLS algorithm is used for adaptive channel estimator. The Euclide...