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In this paper, a convergence-enhanced gradient neural network (CEGNN) is proposed and investigated for solving online Sylvester equation that is widely used in the control community. Different from the conventional gradient neural network (CGNN), the proposed CEGNN model possesses a specially-constructed nonlinear activation function, and thus possesses the better convergence performance (i.e., finite-time...
In this paper, the problem of network-based leader-following consensus is addressed for linear multi-agent systems with input saturation. First, a network-based consensus protocol with input saturation constraints is introduced to accommodate some network-induced effects such as delay, data quantization and time-varying sampling interval. Next, the Lyapunov-Krasovskii method is utilized to show the...
A sparse grid method for the time‐dependent Navier–Stokes equations based on hyperbolic cross approximation is considered in this article. Subsequent truncation of the associated series expansion results in a sparse grid discretization. Stability and convergence of the fully discrete sparse grid method are established. Finally, the numerical experiment is presented to show the effectiveness of this...
In this paper, we presents a new nonlinear iterative learning control law for a class of nonlinear systems, denoted NP-D-INP-D, which is composed by limiting the action of error in PID learning law, that is, use a bounded nonlinear function of error instead of error, and adding a differential feedback in the integrator of PID learning law to inject the suited damping. By using Bellman-Gronwall lemma,...
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