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Receding horizon revisited: An easy way to robustly stabilize and LTV system. (English) Zbl 0751.93059
Summary: Stabilization schemes are often based on infinite horizon optimization of a non-singular cost functional. For such schemes to make sense in a time varying context, a fairly good knowledge of the system parameters at all times is a prerequisite. That is a major disadvantage in adaptive implementations. When applicable, the receding horizon approach overcomes this difficulty as, though it relies on general qualitative long-term properties (such as controllability), it requires quantitative knowledge only of (temporally) local parameter values. Work done on receding horizon stabilization during the 1970’s focused on LQ (=‘H 2 ’) optimization criteria. Looking for a stabilization method which carriers also the robustness properties of infinite horizon H design, we consider here local minimization of the L 2 -induced I/O norm (=‘H optimization’) as the design objective. Both state and observation based feedback scheme are derived, and relations to finite and infinite horizon optimization are discussed.
93C99Control systems, guided systems
93C35Multivariable systems, multidimensional control systems