Design of a computationally efficient observer-based nonlinear model predictive control for a continuous stirred tank reactor

Nonlinear model predictive control (NMPC) proves to be a suitable technique for controlling nonlinear systems, However, the need for the online non-convex nonlinear optimization in the NMPC algorithm increases its computational burden and leads to unreliable or even unstable control. In this paper, a computationally efficient observer-based NMPC approach has been proposed for multiple-input multiple-output (MIMO) systems. After estimating the states, a multivariable NMPC algorithm with nonlinear prediction and linearization (NPL) was used. The efficacy of the proposed NMPC scheme has been demonstrated by conducting simulation studies on a jacketed CSTR used in the production process of propylene glycol.