Offset-free ARX-based adaptive model predictive control applied to a nonlinear process.

An adaptive steady state constrained autoregressive with extra input (SSARX) model is developed and embedded into a model predictive control (MPC) framework to regulate a nonlinear process for setpoint tracking. The resulting linear MPC can minimize the deviation of output from the setpoint along the entire prediction horizon and finally achieve zero-offset control even with model mismatch. Our contributions lie in three aspects: First, we develop a steady state constrained ARX model identification method. Second, we design an information matrix based index for model re-identification using online closed-loop data. Third, when steady state input values are unknown, our algorithm can adaptively adjust parameters in the objective function and model to track the setpoint crossing a wide range of operating conditions. A fermenter control problem is studied to demonstrate the effectiveness of the proposed approach.

Offset-free MPC strategy for nonzero regulation of linear impulsive systems.

In various biomedical applications, drug administration treatment can be modeled as an impulsive control system. Despite the development of different control strategies for impulsive systems, the elimination of the offset generated by a plant-model mismatch has not yet been researched. In biomedical systems, this mismatch is a consequence of physiological changes and can result in inaccurate treatment of patients. Therefore, control techniques that accomplish the objectives by compensating the effect of variations are required. The present paper proposes and substantiates a novel offset-free model predictive control (MPC) strategy for impulsive systems. To that aim, an impulsive disturbance model is introduced, and an observer design is developed that includes new observability criteria for estimating the disturbance and the state. Further, it is demonstrated that the proposed control strategy achieves zero offset tracking from an analysis of the observer and the controller at steady state. Additionally, the controller incorporates a recent MPC formulation to steer the state to an equilibrium set using artificial/intermediary variables to achieve nonzero regulation. Finally, these results are evaluated and illustrated using a dynamical model for type 1 diabetic patients.

An offset-free MPC formulation for nonlinear systems using adaptive integral controller.

This paper investigates a novel offset-free control scheme based on a multiple model predictive controller (MMPC) and an adaptive integral action controller for nonlinear processes. Firstly, the multiple model description captures the essence of the nonlinear process, while keeping the MPC optimization linear. Multiple models also enable the controller to deal with the uncertainty associated with changing setpoint. Then, a min-max approach is utilized to counter the effect of parametric uncertainty between the linear models and the nonlinear process. Finally, to deal with other uncertainties, such as input and output disturbances, an adaptive integral action controller is run in parallel to the MMPC. Thus creating a novel offset-free approach for nonlinear systems that is more easily tuned than observer-based MPC. Simulation results for a pH-controller, which acts as an example of a nonlinear process, are presented to demonstrate the usefulness of the technique compared to using an observer-based MPC.