A prescribed performance function characterizing convergence rate, maximum overshoot and steady-state error is employed to enhance the transient tracking performance.
Characterizing convergence speed is one of the most important research challenges in the design of distributed consensus algorithms for networked multi-agent systems.
When attractor points (z m *, z f * ) of Equation 10 lie on the boundary of the phenotypic space or outside of it, the shape of the equilibrium distribution cannot in general be assessed (to the best of our knowledge), and in this case, characterizing convergence stable points is not straightforward.
For a sequence of convex functions on a separable Banach space we show that both pointwise convergence of their Lipschitz regularizations and Wijsman convergence of their epigraphs are equivalent to variants of two conditions used by Attouch and Beer to characterize slice convergence.
We characterize this convergence in both the weak-⁎ topology of distributions and a weighted Sobolev norm.
We characterize the convergence speed for the distributed discrete-time consensus algorithm over a variety of random networks with arbitrary weights.
The predefined performance bounds, which characterize the convergence rate, maximum overshoot, and steady-state response of control errors, are integrated with error surfaces in consideration of underactuated constraints.
We characterize the convergence speed explicitly and provide design guidelines for maximizing it, as well as for minimizing the residual set near the source.
We formulate general conditions on prior, likelihood, and variational class that characterize the convergence rates.
In the first three results, we characterize the convergence of the odd and even subsequences of solutions of (1.1).
