This paper concerns the problem of designing an EID-based robust output-feedback modified repetitive-control system (ROFMRCS) that provides satisfactory aperiodic-disturbance rejection performance for a class of plants with time-varying structured uncertainties. An equivalent-input-disturbance (EID) estimator is added to the ROFMRCS that estimates the influences of all types of disturbances and compensates them. A continuous-discrete two-dimensional model is built to describe the EID-based ROFMRCS that accurately presents the features of repetitive control, thereby enabling the control and learning actions to be preferentially adjusted. A robust stability condition for the closed-loop system is given in terms of a linear matrix inequality. It yields the parameters of the repetitive controller, the output-feedback controller, and the EID-estimator. Finally, a numerical example demonstrates the validity of the method.
Maritime information services supporting European agencies such as the FRONTEX require European‐wide forecast solutions. Following a consistent approach, regional and global forecasts of the sea surface conditions from Copernicus Marine Service and national met‐ocean services are aggregated in space and time to provide a European‐wide forecast service on a common grid for the assistance of Search and Rescue operations. The best regional oceanographic model solutions are selected in regional seas with seamless transition to the global products covering the Atlantic Ocean. The regional forecast models cover the Black Sea, Mediterranean Sea, Baltic Sea, North Sea and combine the North Sea – Baltic Sea at the Danish straits. Two global models have been added to cover the entire model domain, including the regional models. The aggregated product is required to have an update frequency of 4 times a day and a forecasting range of 7 days, which most of the regional models do not provide. Therefore, smooth transition in time, from the shorter timerange, regional forecast models to the global model with longer forecast range are applied. The set of parameter required for Search and Rescue operations include sea surface temperature and currents, waves and winds. The current version of the aggregation method was developed for surface temperature and surface currents but it will be extended to waves in latter stages. The method relies on the calculation of aggregation weights for individual models. For sea surface temperature (SST), near real‐time satellite data at clear‐sky locations for the past days is used to determine the aggregation weights of individual forecast models. A more complicated method is to use a weighted multi‐model ensemble (MME) approach based on best forecast features of individual models and possibly including near real time observations. The developed method explores how satellite observations can be used to assess spatially varying, near real time weights of different forecasts. The results showed that, although a MME based on multiple forecasts only may improve the forecast, if the forecasts are unbiased, it is essential to use observations in the MME approach so that proper weights from different models can be calculated and forecast bias can be corrected. It is also noted that, in some months, e.g., June in Baltic Sea, even SST was assimilated, the forecast still show quite high error. There are also visible difference between different Copernicus Marine Environment Monitoring Service (CMEMS) satellite products, e.g. OSTIA and regional SST products, which can lead different forecast quality if different SST observation products are assimilated.
This paper is concerned with the problem of designing a robust modified repetitive-control system with a dynamic output feedback controller for a class of strictly proper plants. Employing the continuous lifting technique, a continuous-discrete two-dimensional (2D) model is built that accurately describes the features of repetitive control. The 2D control input contains the direct sum of the effects of control and learning, which allows us to adjust control and learning preferentially. The singular-value decomposition of the output matrix and Lyapunov stability theory are used to derive an asymptotic stability condition based on a Linear Matrix Inequality (LMI). Two tuning parameters in the LMI manipulate the preferential adjustment of control and learning. A numerical example illustrates the tuning procedure and demonstrates the effectiveness of the method.
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Molecularly imprinted polymers (MIPs) were synthesized by imprinting a new template—S(-)-1,1′-binaphthalene-2,2′-diamine (S-DABN) and applied as chiral stationary phases for chiral separation of DABN racemates by high-performance liquid chromatography (HPLC). The influence of some key factors on the chiral recognition ability of MIPs, such as the type of functional monomers and porogen and the molar ratio of template to monomer, was systematically investigated. The chromatographic conditions, such as mobile phase composition, sample loading, and flow rate, were also measured. The chiral separation for DABN racemates under the optimum chromatographic conditions by using MIP chiral stationary phase (CSP) of P3, prepared with the S-DABN/MAA ratio = 1/4 and used acetonitrile (2 mL) and chloroform (4 mL) as porogen, showed the highest separation factor (2.14). Frontal analysis was used to evaluate affinity to the target molecule of MIPs. The binding sites (Bt) of MIPs and dissociation constant (Kd) were estimated as 4.56 μmol g−1 and 1.40 mmol L−1, respectively. In comparison with the previous studies, this approach had the advantages, such as the higher separation factor, easy preparation, and cost-effectiveness, it not only has the value for research but also has a potential in industrial application.
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