Implementation of model predictive control with modified minimal model on low-power RISC microcontrollers

Due to the ability of modeling multivariable systems and handling constraints in the control framework, model predictive control (MPC) has received a lot of interest from both academic and industrial communities. Although it is an established control technique, implementing MPC on small-scale devices is a challenge since we need to handle complicated issues of the control framework using limited computational power and hardware resources. This paper presents our implementation of MPC with constraints on the Texas Instruments MSP430 16-bit microcontroller platform. The MPC operational constraints which are supported in our design include rate of change, amplitude and output constraints, while the associated optimization problem is solved using a primal-dual interior-point algorithm based on predicator-corrector method. Our implementation is demonstrated in a prototype of a real-time close-loop blood glucose regulation system using a modification of the minimal model. Experimental results show that our system is able to achieve desired diabetes management, and the chosen microprocessor is capable of performing the MPC algorithm accurately with high energy-efficiency and in real-time.

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