Abstract
This paper addresses identification problem of switched linear(SL) systems from input-output data. The main challenge is the partitions of data points correspond to different subsystems are unavailable. Inspired by compressed sensing theory, we pursue the sparsity of estimation error and propose `0-norm optimization algorithm to identify parameters. Unfortunately, the computational complexity of this approach is intractable. To overcome this difficulty, we replace `0-norm by `1-norm, which retains sparse property. We not only provide recoverable conditions for identifying SL systems via `1-norm minimization program, but also show that `1-norm estimator is robust to bounded noise. Numerical experiments are included to demonstrate the performance of our algorithms.
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