Data-Driven Control and Optimization under Noisy and Uncertain Conditions
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A new method for H-infinity gain-scheduled controller design by convex optimization is proposed that uses only frequency-domain data. The method is based on loop shaping in the Nyquist diagram with constraints on the weighted infinity norm of closed-loop t ...
Linear optimal gains Gopt(ω) are computed for the separated boundary-layer flow past a two-dimensional bump in the subcritical regime. Very large values are found, making it possible for small-amplitude noise to be strongly amplified and to desta ...
Soft constrained model predictive control (MPC) is frequently applied in practice in order to ensure feasibility of the optimization during online operation. Standard techniques offer global feasibility by relaxing state or output constraints, but cannot e ...
Institute of Electrical and Electronics Engineers2014
In this work, we study the task of distributed optimization over a network of learners in which each learner possesses a convex cost function, a set of affine equality constraints, and a set of convex inequality constraints. We propose a fully distributed ...
Institute of Electrical and Electronics Engineers2014
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A new method for the design of fixed-order dynamic output-feedback Linear Parameter Varying (LPV) controllers for discrete-time LPV systems with bounded scheduling parameter variations is presented in this paper. Sufficient conditions for the stability, H2 ...