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A recently proposed adaptive model predictive control algorithm is implemented in real time and its performance is verified experimentally on a quad-tank testbed. The algorithm relies on an iterative set membership identification procedure in order to provide a set of possible plant models at each time step. This set is then exploited by the model predictive controller in order to robustly enforce...
A novel adaptive output feedback control technique for uncertain linear systems is proposed, able to cope with input and output constraints and measurement noise. At each time step, the collected input-output data are exploited to refine the set of models that are consistent with the available information on the system. Then, the control input is computed according to a receding horizon strategy,...
Of special industrial interest is the cross-directional control of continuous coating processes, where the cross-direction refers to the direction perpendicular to the substrate movement. The objective of the controller is to maintain a uniform coating under unmeasured process disturbances based on assumptions relevant to coating processes found in industry. A model for control design is developed...
Robust control laws are presented for an undamped pair of coupled manes with a noncolocated sensor and actuator. This simple problem captures many of the features of more complex aircraft and space structure vibration control problems. The control problem is formulated in the structured singular value framework, which addresses the stability robustness to parameter variations directly. Controllers...
We show that unconstrained Model Predictive Control (MPC) based on step response models is identical to linear quadratic optimal output feedback under a particular disturbance and measurement noise assumption. More specifically, MPC in its unconstrained form is equivalent to the optimal state observer (Kalman filter) designed for step disturbances at the output and in the absence of measurement noise,...
A methodology for computing frequency domain uncertainty bounds for single-input single-output systems is presented. This methodology is applied to the identification of a fixed-bed reactor. A robust controller with a single adjustable parameter is designed for the reactor using Internal Model Control (IMC) theory. The computed uncertainty bounds are experimentally validated using the IMC controller.
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