Model predictive control tuning parameters

Zafiriou, E., On the effect of tuning parameters and constraints on the robustness of model predictive controllers, Proceedings of Chemical Process Control—CPC TV, 363-393 (1991). 

Song et al (2006) proposed a multivariable purity control scheme using the m-parameters as manipulated variables and a model predictive control scheme based on linear models that are identified from nonlinear simulations. The approach proposed by Schramm, Griiner, and Kienle (2003) for purity control has been modified by several authors (Kleinert and Lunze, 2008 Fiitterer, 2008). It gives rise to relatively simple, decentralized controllers for the front positions, but an additional purity control layer is needed to cope with plant-model mismatch and sensor errors. Vilas and Van de Wouwer (2011) augmented it by an MPG controller based on a POD (proper orthogonal collocation) model of the plant for parameter tuning of the local PI controllers to cope with the process nonlinearity. 

Model predictive control, 238 control horizon, 239 prediction horizon, 239 tuning parameters, 242... 

The MPC control problem illustrated in Eqs. (8-66) to (8-71) contains a variety of design parameters model horizon N, prediction horizon p, control horizon m, weighting factors Wj, move suppression factor 6, the constraint limits Bj, Q, and Dj, and the sampling period At. Some of these parameters can be used to tune the MPC strategy, notably the move suppression faclor 6, but details remain largely proprietary. One commercial controller, Honeywell s RMPCT (Robust Multivariable Predictive Control Technology), provides default tuning parameters based on the dynamic process model and the model uncertainty. 

Decades of research by numerous individuals has uncovered numerous minute facts about this complex soil-side and air-side coupled chemical release process. While lab.-specific and controlled conditions allow sophisticated theoretical mathematical models to be developed that can match measured flux data very well, such elaborate protocols are of little practical use in most field applications. Vignette EC models that capture the theoretical essence of the significant processes and contain a minimum number of parameters that can be transparently adjusted for fine-tuning the model to the site data appears to be the most realistic approach. The following equation is a transient EC flux model that is simple but is theoretically consistent with the known major processes and capable of making quantitative predictions using a few key parameters ... 

Co using their respective metal nitrate and glycine mixture. Nickel was chosen as a model to successfully verify the thermodynamic predictions. It can be concluded from the results and discussion that the fuel to oxidizer ratio, (p, is an important parameter in SCS systems and significantly influences the synthesized nanoparticles. The q> value not only affects the combustion temperature but also the nature of the solid product (metal or metal-oxide), porosity and crystalhte size. It is anticipated that the other metal-systems (Cu and Co) will also follow a similar trend. The properties of the synthesized nanoparticles can be controlled and fine-tuned by adjusting the fuel to oxidizer ratio in SCS processes. 

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