Uncertainty exogenous

In view of this, a robust scheme based on the Hoo control theory [24] is developed in the present work. The algorithm guarantees both stability and performance for a family of perturbed plants with model uncertainties and exogenous inputs (i.e., chamber disturbances and sensor noises) over a wide range of operating conditions, an advantage especially desired for combustion dynamics problems. 

The second part of the work involves implementing a robust controller. The key issue in the controller design is the treatment of system dynamics uncertainties and rejection of exogenous disturbances, while optimizing the flow responses and control inputs. Parameter uncertainties in the wave equation and time delays associated with the distributed control process are formally included. Finally, a series of numerical simulations of the entire system are carried out to examine the performance of the proposed controller design. The relationships among the uncertainty bound of system dynamics, the response of flow oscillation, and controller performance are investigated systematically. 

The robust controller consists of two main components the first is an observer, which estimates the states of the generalized plant described by Eq. (22.29), and consequently the dynamics in the combustion chamber. It is capable of treating exogenous inputs and uncertainty-induced disturbances. The second is a state-feedback control gain, which determines the control action based on the estimated states x. The final configuration of the controller is plotted in Fig. 22.3. 

In conclusion, while exogenous ROI such as H2O2 do not seem to affect NF-kB activation in neutrophils, some uncertainty subsists concerning whether endogenous ROI can affect the ability of neutrophils to activate NF-kB. Clearly, further work is required to definitively settle the issue. 

Based on the above literature review, the following conceptual model (Figure 1) can be drawn. The model has five constructs asset specificity, uncertainty, vertical coordination, quality management practices and firm performance. Firm performance will be measured by tangible and intangible variables. They are used as latent dependent (endogenous) variables. The other four variables will be used as latent-independent (exogenous) variables. For operationalization, each construct contains a set of indicators (See Appendix). 

The solution of the mathematical problem of Eq. (1), (d" , X ), depends on the values of the model parameters and exogeneous inputs (disturbances) to the process. If the optimal operating point lies at the intersection of process constraints, a most commonly situation, model uncertainty may cause the steady state actual operating point to violate process constraints. Therefore, the implemented optimal operating point should be compromised to... 

Hence, the aforementioned enterprise-wide model is extended to a stochastic program that takes exogenous uncertainty into account, namely, demand, price and interest rates variability. To tackle the resulting problem, scenario-based multi-stage stochastic mixed integer modeling techniques are applied. Then, the stochastic model is introduced into a model predictive controller to capture the dynamics of SC processes and their environment. The ensuing model can be used as a support tool... 

Using S-Graph to Address Exogenous Uncertainty in Processes Scheduling... 

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