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Alessandro Astolfi
Researcher at Imperial College London
Publications - 571
Citations - 16109
Alessandro Astolfi is an academic researcher from Imperial College London. The author has contributed to research in topics: Nonlinear system & Linear system. The author has an hindex of 56, co-authored 553 publications receiving 14223 citations. Previous affiliations of Alessandro Astolfi include Supélec & Instituto Politécnico Nacional.
Papers
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Disturbance attenuation and H/sub infinity /-control via measurement feedback in nonlinear systems
TL;DR: In this article, a solution to the problem of disturbance attenuation via measurement feedback with internal stability is presented for an affine nonlinear system, in which the concept of truncated L/sub 2/ norms can be given an interpretation in terms of the response to periodic inputs in the sense of RMS amplitude, even in the nonlinear setup.
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Discontinuous control of nonholonomic systems
TL;DR: In this article, the problem of local asymptotic stabilization for a class of discontinuous nonholonomic control systems via discontinuous control is addressed and solved from a new point of view.
Journal ArticleDOI
Immersion and invariance: a new tool for stabilization and adaptive control of nonlinear systems
Alessandro Astolfi,Romeo Ortega +1 more
TL;DR: It is shown that in adaptive control problems the method yields stabilizing schemes that counter the effect of the uncertain parameters adopting a robustness perspective, and the proposed approach is directly applicable to systems in feedback and feedforward form, yielding new stabilizing control laws.
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Homogeneous Approximation, Recursive Observer Design, and Output Feedback
TL;DR: A new global asymptotic stabilization result by output feedback for feedback and feedforward systems is proposed by combining a new recursive observer design procedure for a chain of integrator.
Book
Nonlinear and adaptive control with applications
TL;DR: In this article, the authors provide a detailed treatment of the design of robust adaptive controllers for nonlinear systems with uncertainties, based on the ideas of system immersion and manifold invariance.