An adaptive neural network state-feedback controller is designed by constructing a suitable Lyapunov function.
Upper bounds on the norm of the estimation error have been found by constructing a suitable positively invariant set.
By constructing a suitable Lyapunov functional, sufficient conditions to guarantee the systems to be mean-square asymptotically stable are established.
The sufficient conditions are established in the form of linear matrix inequalities by constructing a suitable Lyapunov Krasovskii functional with triple integral terms and using Kronecker product.
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This understanding will help lecturers to improve students' performance by constructing a suitable first year curriculum and to ascertain whether a student would benefit from additional support.
Under the framework of differential inclusions theory and set-valued maps, a state feedback controller and an adaptive updated law are designed by constructing a suitable Lyapunov functional.
By constructing a suitable Lyapunov functional and utilizing some inequality techniques, we obtain some sufficient conditions for the uniqueness and global exponential stability of the equilibrium solution.
Moreover, the role of the designed controllers is analyzed in great detail by constructing a suitable comparison system and a new method is presented to estimate the settling time by utilizing the comparison system.
By constructing a suitable Lyapunov Krasovskii functional and using free-weighting matrix approach, a new set of sufficient conditions for the solvability of the addressed problem is derived in terms of linear matrix inequalities.
