This paper deals with an adaptive nonlinear model predictive control (NMPC) based estimator in cases of mismatch modeling, presence of perturbations and/or parameter variations. Thus, we propose an adaptive nonlinear predictive controller based on the second-order divided difference filter (DDF) for multivariable systems. The controller uses a nonlinear state-space model for parameters and state estimation and for the control law synthesis. Two nonlinear optimization layers are included in the proposed algorithm. The first optimization problem is based on the output error (OE) model with a tuning factor, and it is dedicated to minimize the error between the model and the system at each sample time by estimating unknown parameters when assuming that all system states are available. The second optimization layer is used by the centralized nonlinear predictive controller to generate the control law which minimizes the error between future setpoints and future outputs along the prediction horizon. The proposed algorithm leads to a good tracking performance with an offset-free output and an effectiveness in perturbation attenuation. Practical results on a real setup show the reliability of the proposed approach.
Given the complexity of water quality data sets, water resources pose a significant problem for global public order in terms of water quality protection and management. In this study, surface water quality for drinking and irrigation purposes was evaluated by calculating the Water Quality Index (WQI) and Irrigation Water Quality Index (IWQI) based on nine hydrochemical parameters. The discriminant analysis (DA) method was used to identify the variables that are most responsible for spatial differentiation. The results indicate that the surface water quality for drinking is of poor and very poor quality according to the WQI values, however, the IWQI values indicate that the water is acceptable for irrigation with restrictions for salinity sensitive plants. The discriminate analysis method identified pH, potassium, chloride, sulfate, and bicarbonate as the significant parameters that discriminate between the different stations and contribute to spatial variation of the surface water quality. The findings of this study provide valuable information for decision-makers to address the important problem of water quality management and protection.
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