(162b) Stiction Compensation through Economic Model Predictive Control

Chemical process control typically involves extensive use of control valves as the final control element of a control system.  Though control valve dynamics are often assumed to be very fast compared to the process dynamics or to behave linearly, significant issues, such as control loop oscillations, are often experienced in industry due to nonlinear valve dynamics [1]-[2].  One major source of valve nonlinearity is stiction, the sticking of a control valve upon a direction change of the control signal that results in stickband and slipjump [1].  Because stiction prevents control actions from being implemented when requested, there has been an interest in the development of mathematical models and compensation techniques for stiction [2].  Stiction has not yet been addressed, however, in the context of economic model predictive control (EMPC) [3]-[5].  Because EMPC is able to predict future process state trajectories and adjust the control actions accordingly to obtain economically optimal process operation under the given process model and constraints, EMPC is an alternative to other stiction compensation strategies [2].

This work extends the efforts in [6] that examined the effect of the inclusion of linear actuator dynamics in EMPC by accounting for valve stiction and its effect in the closed-loop system under EMPC.  A Lyapunov-based economic model predictive control (LEMPC) strategy is developed that explicitly accounts for stiction dynamics that can be described by a nonlinear state-space model.  This EMPC uses an economics-based cost function and adopts suitable constraints on the magnitude and rate of change of the calculated control actions to mitigate the impact of stiction in the closed-loop system.  Closed-loop stability of nonlinear processes with valve dynamics affected by stiction is examined under the proposed LEMPC and stability conditions are derived.  A chemical process example illustrates the importance of including stiction dynamics in EMPC for optimal process operation and for constraint satisfaction.

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