This paper deals with the design and application of nonlinear model-based control schemes for stable and nonlinear benchmark industrial processes. The primary control objective is to facilitate set-point (constant/time-varying) tracking in the presence of external disturbances, process noise, measurement noise, parametric uncertainty, and model mismatch. We first propose a “noninferential-type” model-based control scheme which involves a finite-dimensional, nonlinear, and deterministic process model to generate the model states. Secondly, an “inferential-type” model-based control scheme has been introduced particularly to take into account the stochastic uncertainties such as process noise and measurement noise. The second scheme exploits the dual extended Kalman filter for estimating the immeasurable states and the process parameters through which disturbance is injected. Unlike fixed-parameter controllers, the proposed schemes update the controller gains at each step depending on the real-time process gains. In order to demonstrate the usefulness of the proposed closed-loop tracking control schemes, two exhaustive case studies have been carried out on the CSTR and Van de Vusse reactor processes, which are considered to be benchmark industrial processes due to highly nonlinear and unpredictable behaviour and due to nonminimum phase property. Finally, the performance of the proposed schemes are compared with an EKF-based adaptive PI control framework and the simulation results reveal that the transient performance of the proposed schemes are better than that of the aforementioned PI technique especially in perturbed condition (ie, in presence of model mismatch and measurement noise).
- model predictive control
- nonlinear model-based control
- state and parameter estimation