Adaptive-Model Based Self-Tuning Generalized Predictive Control of a Biodiesel Reactor

<p>Traditionally, the Recursive Least Squares (RLS) algorithm was used in the Generalized Predictive Control (GPC) framework solely for model adaptation purposes. In this work, the RLS algorithm was extended to also cater for self-tuning of the controller. Specifically, the analytical expressions proposed by Shridhar and Cooper (1997b) for offline tuning of the move suppression weight was deployed for online tuning. This new combination, denoted as the Adaptive-Model Based Self-Tuning Generalized Predictive Control (AS-GPC), contains both model adaptation and self-tuning capabilities within the same controller structure. Several RLS algorithms were screened and the Variable Forgetting Factor Recursive Least Squares (VFF-RLS) algorithm was selected to capture the dynamics of the process online for the purpose of model adaptation in the controller. Based on the evolution of the process dynamics given by the VFF-RLS algorithm in the form of First Order Plus Dead Time (FOPDT) model parameters, the move suppression weight for the AS-GPC was recalculated automatically at every time step based on the analytical tuning expressions. The proposed control scheme was tested and implemented on a validated mechanistic transesterification process, known for inherent nonlinearities. Closed loop simulation of the transesterification reactor revealed the superiority of the proposed control scheme in terms of servo and regulatory control as compared to other variants of advanced controllers and the conventional PID controller. Not only is the proposed control scheme adept in tackling issues of process nonlinearities, it also minimizes user involvement in the tuning of the controller and consequently reduces process interruptions.