Generalized predictive control with dual adaptation

Abstract

In this work, the recursive least squares (RLS) algorithm, which traditionally was used in the generalized predictive controller (GPC) framework solely for model adaptation purposes, was extended to cater for auto-tuning of the controller. This new combination which eases the task of controller tuning, contains both model adaptation and auto-tuning capabilities within the same controller structure. Hereafter this scheme will be referred to as the adaptive-model based self-tuning generalized predictive control (AS-GPC). 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 existing single input single output (SISO) analytical tuning expressions originally used for offline tuning of constraint-free predictive controllers. Closed loop simulation on a validated transesterification reactor model, known for inherent nonlinearities, revealed the superiority of the proposed constrained control scheme in terms of servo and regulatory control as compared to the GPC with model adaptation only, the conventional GPC as well as the conventional PID controller. The tuning expressions used, although intended for constraint-free predictive controllers, yielded good results even in the constrained case.