The intersection of all the switching surfaces is referred to as the sliding manifold.
Sliding modes are used to determine best values for parameters in neural network learning rules, thereby robustness in learning control can be improved.
Partitions are typically non-loadbearing, lightweight, easy to put up and dismantle or, in the case of sliding, folding screens, 'elastic' in behaviour.
The slow, halting, sliding steps appear as though the feet are adhering to the floor.
These manifolds are defined by the selection of the sliding mode functions.
First, the sliding motion is insensitive with respect to the matched perturbation: it has been successfully rejected.
Experimental results using the integral sliding mode and time-varying state feedback controllers in a more complex map.
The main steps of the proof are: (1) game-theoretic first moment; (2) game-theoretic higher moments ('sliding potentials'); and (3) game-theoretic independence.
