Iterative learning parametrization for pointwise pole assignment in linear dynamical systems

摘要: By means of iterative learning parametrization for state/output feedback gains in linear time invariant systems, pointwise pole assignment (PPA) is re-formulated and addressed a complex-domain fashion, whereas implementation issues are also examined. Technical features include: (i) no assumptions other than controllability observability needed; (ii) the algorithms numerically tractable robust against initial values matrix uncertainties; (iii) suggested significant achieving PPA-related control strategies, where data-modeling data-driving techniques employed. Numerical examples included to illustrate main results.