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Finite-horizon discrete-time robust guaranteed cost state estimation for non-linear stochastic uncertain systems

Finite-horizon discrete-time robust guaranteed cost state estimation for non-linear stochastic uncertain systems

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This study presents a new approach to discrete-time robust non-linear state estimation based on the use of sum quadratic constraints. The approach involves a class of state estimators that include copies on the system non-linearities in the state estimator. The non-linearities being considered are those that satisfy a certain generalised monotonicity condition. The linear part of the state estimator is synthesised using minimax LQG control theory which is closely related to H∞ control theory and this leads to a non-linear state estimator that gives an upper bound on an estimation error cost functional.

Inspec keywords: stochastic systems; linear quadratic Gaussian control; H∞ control; uncertain systems; discrete time systems; robust control; nonlinear control systems; state estimation; control nonlinearities; minimax techniques

Other keywords: minimax LQ.G control theory; nonlinear stochastic uncertain systems; H∞ control theory; finite-horizon discrete-time robust guaranteed cost state estimation; generalised monotonicity condition; sum quadratic constraints; system nonlinearities; estimation error cost functional

Subjects: Simulation, modelling and identification; Stability in control theory; Time-varying control systems; Nonlinear control systems; Optimisation techniques; Discrete control systems; Optimal control

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