Generalized Filippov solutions for systems with prescribed-time convergence

Dynamical systems with prescribed-time convergence sometimes feature a right-hand side exhibiting a singularity at the prescribed convergence time instant. In an open neighborhood of this singularity, classical absolutely continuous Filippov solutions may fail to exist, preventing indefinite continuation of such solutions. This note introduces a generalized Filippov solution definition based on the notion of generalized absolute continuity in the restricted sense. Conditions for the continuability of such generalized solutions are presented and it is shown, in particular, that generalized Filippov solutions of systems with an equilibrium that is attractive, in prescribed time or otherwise, can always be continued indefinitely. The results are demonstrated by applying them to a prescribed-time controller for a perturbed second-order integrator chain

Worst-case error bounds for the super-twisting differentiator in presence of measurement noise

The super-twisting differentiator, also known as the first-order robust exact differentiator, is a well known sliding mode differentiator. In the absence of measurement noise, it achieves exact reconstruction of the time derivative of a function with bounded second derivative. This note proposes an upper bound for its worst-case differentiation error in the presence of bounded measurement noise, based on a novel Lipschitz continuous Lyapunov function. It is shown that the bound can be made arbitrarily tight and never exceeds the true worst-case differentiation error by more than a factor of two. A numerical simulation illustrates the results and also demonstrates the non-conservativeness of the proposed bound

Optimal Robust Exact Differentiation via Linear Adaptive Techniques

The problem of differentiating a function with bounded second derivative in the presence of bounded measurement noise is considered in both continuous-time and sampled-data settings. Fundamental performance limitations of causal differentiators, in terms of the smallest achievable worst-case differentiation error, are shown. A robust exact differentiator is then constructed via the adaptation of a single parameter of a linear differentiator. It is demonstrated that the resulting differentiator exhibits a combination of properties that outperforms existing continuous-time differentiators: it is robust with respect to noise, it instantaneously converges to the exact derivative in the absence of noise, and it attains the smallest possible -- hence optimal -- upper bound on its differentiation error under noisy measurements. For sample-based differentiators, the concept of quasi-exactness is introduced to classify differentiators that achieve the lowest possible worst-case error based on sampled measurements in the absence of noise. A straightforward sample-based implementation of the proposed linear adaptive continuous-time differentiator is shown to achieve quasi-exactness after a single sampling step as well as a theoretically optimal differentiation error bound that, in addition, converges to the continuous-time optimal one as the sampling period becomes arbitrarily small. A numerical simulation illustrates the presented formal results

Robust exact differentiators with predefined convergence time

The problem of exactly differentiating a signal with bounded second derivative is considered. A class of differentiators is proposed, which converge to the derivative of such a signal within a fixed, i.e., a finite and uniformly bounded convergence time. A tuning procedure is derived that allows to assign an arbitrary, predefined upper bound for this convergence time. It is furthermore shown that this bound can be made arbitrarily tight by appropriate tuning. The usefulness of the procedure is demonstrated by applying it to the well-known uniform robust exact differentiator, which the considered class of differentiators includes as a special case

Organizing to Win: Introduction

[Excerpt] The American labor movement is at a watershed. For the first time since the early years of industrial unionism sixty years ago, there is near-universal agreement among union leaders that the future of the movement depends on massive new organizing. In October 1995, John Sweeney, Richard Trumka, and Linda Chavez-Thompson were swept into the top offices of the AFL-CIO, following a campaign that promised organizing at an unprecedented pace and scale. Since taking office, the new AFL-CIO leadership team has created a separate organizing department and has committed $20 million to support coordinated large-scale industry-based organizing drives. In addition, in the summer of 1996, the AFL-CIO launched the Union Summer program, which placed more than a thousand college students and young workers in organizing campaigns across the country