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In this paper, both full state and output feedback adaptive neural network (NN) controllers are presented for a class of strict-feedback discrete-time nonlinear systems.
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Both the full state and partial state cases are considered for the design of first a fault tolerant controller and then of a fault accommodating controller using LMI's.
Both the case of full state and partial state availability are considered and in the special case of Riesz-spectral operators, one can obtain an explicit bound on the convergence rate of the pairwise state errors.
The presented approach requires full state and force measurements at both the master and slave sides.
An augmented observer is designed to estimate the full state and the disturbance.
Therefore, sliding mode unknown input observer (UIO) is proposed for full state and force estimations.
To deal with the absence of both full-state measurement and parameter knowledge, we introduce a pre-transformation (which happens to be based on backstepping) of the system into an observer canonical form.
In the numerical optimizations, both random and deterministic road inputs and both perfect measurement of full state variables and estimated limited state variables are considered.
In particular, coordinated control strategies are presented for both the full state feedback case and the relative velocity unavailability case, respectively.
We provide both the distributed full state feedback control and the distributed partial state feedback control without velocity measurement.
His funeral was given full state honours, and he was buried in Westminster Abbey in London.
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