459124 Safety-Based Model Predictive Control with Data-Based Determination of Safety Level Sets
In this work, we improve the practical application of the safety-based LEMPC method proposed in  by developing a safety-based controller formulation in the context of Lyapunov-based model predictive control (LMPC) with a quadratic objective function for consistency with the tracking model predictive control (MPC) formulation commonly used in the chemical process industries . In addition, we develop a data-based method for both the LMPC and LEMPC cases for determining the probability that the closed-loop state will leave a given safety level set, and the distance from the level set that the process states may move before returning to the safety level set, due to process disturbances and sensor noise. The knowledge gained from this data-based method can then be used by the safety logic unit as it evaluates safe regions of operation to quantify the probability that, for a given safety level set, the closed-loop state may move far enough from the safety level set to enter a region that the safety logic unit has determined to be unsafe. Furthermore, this knowledge may cause the safety logic unit to select smaller safety level sets for process operation if the disturbances and noise are known to move the process state significantly. In addition, closed-loop simulations of a chemical process example showed that LMPC with safety-based constraints was able to drive the process state into a required safety region more quickly than tracking LMPC alone, showing that the proposed safety-based model predictive control formulation can enhance the safety of current chemical process systems.
 Crowl DA, Louvar JF. Chemical Process Safety: Fundamentals with Applications, 3rded. Upper Saddle River, NJ: Pearson Education, 2011.
 Leveson NG, Stephanopoulos G. A system-theoretic, control-inspired view and approach to process safety. AIChE Journal. 2014;60:2-14.
 Albalawi F, Alanqar A, Durand H, Christofides PD. A feedback control framework for safe and economically-optimal operation of nonlinear processes. AIChE Journal. 2016; in press.
 Heidarinejad M, Liu J, Christofides PD. Economic model predictive control of nonlinear process systems using Lyapunov techniques. AIChE Journal. 2012;58:855-870.
 Qin SJ, Badgwell TA. A survey of industrial model predictive control technology. Control Engineering Practice. 2003;11:733-764.