Iterative identification of output error model for industrial processes with time delay subject to colored noise

To deal with colored noise and unexpected load disturbance in identification of industrial processes with time delay, a bias-eliminated iterative least-squares(ILS) identification method is proposed in this paper to estimate the output error model parameters and time delay simultaneously. An extended observation vector is constructed to establish an ILS identification algorithm. Moreover, a variable forgetting factor is introduced to enhance the convergence rate of parameter estimation. For consistent estimation, an instrumental variable method is given to deal with the colored noise. The convergence and upper bound error of parameter estimation are analyzed. Two illustrative examples are used to show the effectiveness and merits of the proposed method.

Output feedback robust model predictive control with unmeasurable model parameters and bounded disturbance

The output feedback model predictive control(MPC),for a linear parameter varying(LPV) process system including unmeasurable model parameters and disturbance(all lying in known polytopes),is considered.Some previously developed tools,including the norm-bounding technique for relaxing the disturbance-related constraint handling,the dynamic output feedback law,the notion of quadratic boundedness for specifying the closed-loop stability,and the ellipsoidal state estimation error bound for guaranteeing the recursive feasibility,are merged in the control design.Some previous approaches are shown to be the special cases.An example of continuous stirred tank reactor(CSTR) is given to show the effectiveness of the proposed approaches.

Recursive Zero-COVID model and quantitation of control efforts of the Omicron epidemic in Jilin province

Since the beginning of March 2022,the epidemic due to the Omicron variant has developed rapidly in Jilin Province.To figure out the key controlling factors and validate the model to show the success of the Zero-COVID policy in the province,we constructed a Recursive Zero-COVID Model quantifying the strength of the control measures,and defined the control reproduction number as an index for describing the intensity of interventions.Parameter estimation and sensitivity analysis were employed to estimate and validate the impact of changes in the strength of different measures on the intensity of public health preventions qualitatively and quantitatively.The recursive Zero-COVID model predicted that the dates of elimination of cases at the community level of Changchun and Jilin Cities to be on April 8 and April 17,respectively,which are consistent with the real situation.Our results showed that the strict implementation of control measures and adherence of the public are crucial for controlling the epidemic.It is also essential to strengthen the control intensity even at the final stage to avoid the rebound of the epidemic.In addition,the control reproduction number we defined in the paper is a novel index to measure the intensity of the prevention and control measures of public health.

RECURSIVE METH0D FOR ARMA MODEL ESTIMATION (Ⅱ)

In this paper a new recursive method for ARMA model estimation is given. Same as in [1], theorder's estimator is strongly consistent, and the parameter's estimators defer to CLT and LILunder a natural condition. Compared with the previous metheds suggested by Hannan & Kavalieris(1984), Wang Shouren & Chen Zhaoguo (1985) and Franke (1985), this methed has some advantages:the amount of calculat on work is smaller, the minimum-phase property of coeffcient estimators canbe guaranteed,the BAN estimators for MA or AR model can be obtained directly,and the simulationshows that this method is more accurate in estimating the order and parameters.