State Observer Flaws Associated with Time-Varying Parametric System and its proposed Solution by Model Predictive Framework

control engineer uses the model of plant in many industrial processes. In the real scenario, any model is an approximate mathematical expression with constant parameters and variables that can mimic the behaviour of the system. During the processing, the constant parameter of model- undertaken tends to vary due to several factors. These varying parameters play a vital role in system behaviour study and control design as well it reveals many hidden facts also. There is a robust observer-based control strategy for control task assignment in this kind of variation parameter system. Flaws associated with the robust observer are described in this paper. In this paper, the authors attempt to develop an algorithm that can predict parameter variation. Here in this paper authors uses model predictive kind dynamic optimization with receding horizon. The developed algorithm is tested on two different systems the first kind has all Lipchitz functions. Second has non- Lipchitz term. Both two system is subjected to Constant and time-varying perturbation in parameter and further analysis of the effectiveness of the proposed algorithm is discussed with the broad area of future scope. The authors of this paper find that the model predictive approach is fully capable to find the parameter variation of the model of the system, conditionally if the model comprises only Lipchitz functions. The entire computational task is carried at MATLAB2016b environment