Model-Based Development Strategy

In order to tackle the problem of uncertainties in the available model, nonlinear robust and adaptive strategies have been developed, while, in the absence of full state measurements, output-feedback control schemes can be adopted, where the unmeasurable state variables can be estimated by resorting to state observers. The development of model-based nonlinear strategies has been fostered by the development of efficient experimental identification methods for nonlinear models and by significantly improved capabilities of computer-control hardware and software. 

The Generic Model Control (GMC) is a model-based control strategy developed by Lee and Sullivan in 1988 [41], It can be shown that GMC is an input-output linearization technique for processes with unitary relative order [31],... 

A classical approach to this problem involves developing a se wence of models, as shown in Fig. 2. The first step in this chain, labelled 1 in the figure, is the development of a fundamental model Mf describing the dynamic interplay between dominant chemical and physical process phenomena. This model is optimized with respect to the first two model validity criteria— ability to predict process behavior accurately and physical interpretability—and advances in model development tools are improving the quality of fundamental models with respect to the fourth criterion (ease of development). Because their complexity is determined by process details, however, fundamental models typically suffer badly with respect to the third criterion they are not directly compatible with most model-based control strategies. 

Develop a control strategy for a new process. A dynamic model of the process allows alternative control strategies to be evaluated. For example, a dynamic model can help identify the process variables that should be controlled and those that should be manipulated. For model-based control strategies (Chapters 16 and 20), the process model is part of the control law. 

Model predictive control is an important model-based control strategy devised for large multiple-input, multiple-output control problems with inequality constraints on the inputs and/or outputs. This chapter has considered both the theoretical and practical aspects of MFC. Applications typically involve two types of calculations (1) a steady-state optimization to determine the optimum set points for the control calculations, and (2) control calculations to determine the MV changes that will drive the process to the set points. The success of model-based control strategies such as MFC depends strongly on the availability of a reasonably accurate process model. Consequently, model development is the most critical step in applying MFC. As Rawlings (2000) has noted, feedback can overcome some effects... 

In their simplest form, mathematical models have been developed that describe the interactions of healthy CD4+ T cells, infected CD4+ T cells, and free viruses in the form of three coupled ordinary differential equations (Craig and Xia, 2005). Such a model can be the basis of simple model-based feedback strategies for control and can also be extended to generate more complex models suitable for a model predictive control strategy (Zurakowski et al., 2004). 

The details of the control strategy have received much less attention. The theoretical (159) and experimental (160) analyses of the transfer function for CZ growth are notable exceptions. Algorithms for model-based control are just being developed. 

The objective was to develop a site assessment model based on the Analytic Hierarchy Process that can be used both for site selection and site ranking/controlling purposes. While the different application situations to some extent require different criteria the intention was to develop a model as uniform as possible to achieve consistency in site strategy processes. 

When an accurate model of the reaction kinetics is not available (e.g., due to the lack of reliable data for identification), the previously developed approach may be ineffective and model-free strategies for the estimation of the effect of the heat released by the reaction, aq, must be adopted. In detail, the approach in [27] can be considered, where aq is estimated, together with the heat transfer coefficient, via a suitably designed nonlinear observer [24], Other model-free approaches can be adopted, e.g., those based on the adoption of universal interpolators (neural networks, polynomials) for the direct online estimation of the heat [16] and purely neural approaches [11], Approaches based on the combination of neural and model-based paradigms [2] or on tendency models [25] can be considered as well. 

In this case study a simulation strategy, based on a mechanistic PK/PD model, was developed to predict the outcome of the first time in man (FTIM) and proof of concept (POC) study of a new erythropoietin receptor agonist (ERA). A description of the erythropoiesis model, along with the procedures to scale the pharmacokinetics and pharmacodynamics based on preclinical in vivo and in vitro information is presented. The Phase I study design is described and finally the model-based predictions are shown and discussed. 

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