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The paper presents an efficient and simple model-based method of on-line estimation of respiration from the current values of dissolved oxygen concentration and airflow signals. Fast estimation of respiration is important because respiration appears as the most important disturbance signal in the control of dissolved oxygen concentration, and could therefore be used for improving control efficiency. A parameter scheduling PI (proportional and integral) control scheme is proposed, where the estimated respiration signal is used for scheduling of the PI controller parameters, by using local linearization of the process dynamics. The performance of the scheduling controller is compared to a conventional PI controller in simulation using the COST simulation benchmark, and experimentally on an activated sludge process pilot plant.

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Active feedback stabilization of the dominant resistive wall mode (RWM) for an ITER H-mode scenario at high plasma pressure using infinite-horizon model predictive control (MPC) is presented. The MPC approach is closely-related to linear-quadratic-Gaussian (LQG) control, improving the performance in the vicinity of constraints. The control-oriented model for MPC is obtained with model reduction from a high-dimensional model produced by CarMa code. Due to the limited time for on-line optimization, a suitable MPC formulation considering only input (coil voltage) constraints is chosen, and the primal fast gradient method is used for solving the associated quadratic programming problem. The performance is evaluated in simulation in comparison to LQG control. Sensitivity to noise, robustness to changes of unstable RWM dynamics, and size of the domain of attraction of the initial conditions of the unstable modes are examined.

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