Error bounds for conditional algorithms in restricted complexity set membership identification

Restricted complexity estimation is a major topic in control-oriented identification. Conditional algorithms are used to identify linear finite-dimensional models of complex systems, the aim being to minimize the worst-case identification error. High computational complexity of optimal solutions suggests employing suboptimal estimation algorithms. The paper studies different classes of conditional estimators and provides results that assess the reliability level of suboptimal algorithms.