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The paper considers design of Robustly Feasible Model Predictive Controller which achieves recursive robust feasibility of the generated control actions when applied to nonlinear network systems with state constraint under bounded disturbances.
A robust feasibility criterion helps select design and operating conditions for the entire network that can be realized in practice.
The optimizing control algorithm with safety zones modifying the model based state constraints is derived to achieve recursive robust feasibility.
Robust feasibility and stability analyses are incorporated within the proposed methodology to ensure process dynamic operability and asymptotic stability.
Robust feasibility is ensured by imposing more conservative constraints in the optimization problem to take into account explicitly the effect of the uncertainty.
Goherna, Jeyakumar, Li, and Perez [21] introduced the definition of radius of robust feasibility and analyzed the robust weakly efficient solution of a multiobjective linear programming problems with data uncertainty both in the objective function and constraints.
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The approach does not only significantly speed up the online computation, but furthermore allows to suitably tighten the constraints to guarantee robust recursive feasibility when bounds on the uncertain variables are provided.
Having K good plans to choose from has many benefits: it makes the synthesis planning process much more robust towards actual feasibility when in later stages adding functionalization reactions and other chemical details, it allows one to combine several quality measures, and it provides a way to deal with imprecise yield estimates.
The solution is also robust with respect to feasibility if it stays "almost" feasible for any probable scenario ( s in,varOmega ).
In the following, we discuss theoretical and practical feasibility of robust recovery with a compressed sensing perspective.
It will be shown that this method can guarantee the recursive feasibility and robust stability.
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