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A Fast Smoothing-Based Algorithm to Generate l∞-Norm Constrained Signals for Multivariable Experiment Design
Handling peak amplitude constraints, or equivalently $l_{\infty }$ -norm constraints, is an important application demand in experiment design for system identification. The aim of this letter is to present a… Click to show full abstract
Handling peak amplitude constraints, or equivalently $l_{\infty }$ -norm constraints, is an important application demand in experiment design for system identification. The aim of this letter is to present a method for the design of excitation signals with prescribed power spectrum under $l_{\infty }$ -norm constraints for systems with many inputs and outputs. The method exploits an exponential smoothing function in an iterative algorithm. Fast convergence is achieved by a computationally efficient construction of the gradient and the Hessian matrix. Experimental results show excellent convergence behavior that overcomes local minima, while significantly reducing computation time compared to existing techniques.
