Tackling problem nonlinearities & delays via asymmetric, state-dependent objective costs in MPC of an artificial pancreas. Issue 23 (2015)

• Record Type: Journal Article
• Authors: Gondhalekar, Ravi; Dassau, Eyal; Doyle III, Francis J.
• Is Part Of: IFAC-PapersOnLine. Volume 48:Issue 23(2015)
• Page Start: 154
• Page End: 159
• Publication Date: 2015
• Subjects: Model predictive control -- asymmetric objective cost -- state-dependent objective cost -- nonlinear optimization -- safety critical control -- artificial pancreas -- type 1 diabetes; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2015.11.276
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: The design of a Model Predictive Control (MPC) law for an Artificial Pancreas (AP) that automatically delivers insulin to people with type 1 diabetes mellitus is considered. An MPC law was recently proposed that exploits the simplicity of linear dynamical models, but is in two ways a 'nonlinear' departure of standard linear MPC, while circumnavigating the complexity of cumbersome, fully nonlinear MPC approaches. The first of two issues focused on is the nonlinearity of the control problem, and it is demonstrated how this can be tackled via asymmetric objective functions. The second issue is controller induced hypoglycemia resulting from the large delay in actuation and sensing. The proposed MPC strategy employs an asymmetric, state-dependent objective function that leads to a nonlinear optimization problem. The result is an AP controller with significantly elevated safety and comparable control performance. The contribution of this paper is a detailed in-silico analysis of the proposed control law, and a clinical demonstration of the benefits of asymmetric objective functions.