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Then, a novel adaptive prescribed performance neural network (NN) output feedback tracking control method is designed.
A low-complexity prescribed performance attitude control scheme is presented to solve this problem.
In this way, the original system is stabilizable and the prescribed performance is guaranteed.
A decentralized memoryless adaptive fault-tolerant (AFT) control system is designed with prescribed performance bounds.
It is also proved that all tracking errors are preserved within the prescribed performance bounds.
A transformation with respect to tracking error is introduced to implement prescribed performance tracking control.
An optimal adaptive controller based on a reset mechanism and a prescribed performance bound is devised.
The modified prescribed performance bound (PPB) is used as detection threshold.
Then we extend the theoretical development to the case when a prescribed performance measure is desired.
It is shown that the tracking error satisfies the prescribed performance bound all the time.
These controllers ensure the stability and guarantee a prescribed performance level within the uncertainty polytope.
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