By constructing a mode-dependent Lyapunov Krasovskii functional, a sufficient condition for the existence of the desired estimator is derived in terms of certain linear matrix inequalities (LMIs).
Some sufficient conditions to ensure the presence of the controller are obtained by constructing a mode-dependent Lyapunov Krasovskii functional and employing some novel inequalities.
By constructing a mode- and failure-dependent Lyapunov function, the polytopic sensor failures are well handled and a sufficient condition is developed to ensure that the estimation error system is stochastically passive.
By constructing a mode-dependent Lyapunov function and combining free weighting matrices method, less conservative results on the non-fragile l2−l∞ filtering are obtained which guarantee the resulting error dynamic systems are robust stochastically stable and satisfy a prescribed l2−l∞ performance index.
By constructing a mode-dependent and parameter-dependent Lyapunov Krasovskii functional based on delay-partitioning approach, we propose a sufficient condition for the existence of a partially mode-dependent dissipative filter, which ensures that the resulting filtering error system is stochastically stable and dissipative.
By constructing a novel mode-dependent Lyapunov Krasovskii functional, some new criteria are established for the existence and the solvability of the desired observer-based sliding mode controller.
A mode-dependent approach is proposed by constructing a novel Lyapunov functional, where some terms involving triple or quadruple integrals are taken into account.
The procedure is illustrated by constructing a finite element model of a cantilever beam from two or three modes and frequencies.
Random forests is an ensemble learning method for classification that operates by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes output by individual trees.
The improved optimal SM controller is designed by constructing an augmented optimal sliding mode manifold function, which includes all of the information of the structure and expected performance of the suspension.
This limit can be treated by constructing electron proton– X mode states, with the same procedure used to obtain electron proton states in eqs 12.16– 12.22 but in the presence of two nuclear modes (R and X).
