Output-feedback robust control of uncertain systems without observer
A novel data-driven learning method to achieve static output-feedback robust control of unmatched dynamic systems was proposed,which uses the techniques originally developed for optimal control.The robust control was first transformed into the optimal control of an augmented system,taking unmatched dynamics into consideration.Then,to design the output-feedback optimal control,an output-feedback algebraic Riccati equation was derived by tailoring its state-feedback control counterpart.Once more,an adaptive online learning method was designed to avoid using the observer,where two operations(i.e.,vectorization and Kronecker's product)were adopted to reconstruct the output-feedback algebraic Riccati equation.Finally,the required persistent excitation condition was further relaxed to realize the rapid convergence of the estimated parameters.Simulation results show the effectiveness of the proposed control method and learning algorithm.
control theory and control engineeringdata-driven learningrobust controloptimal controlpersistent excitation condition
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