Results 1 to 10 of about 1,874,418 (326)
Predictive Control for Linear and Hybrid Systems [PDF]
, 2017 Model Predictive Control (MPC), the dominant advanced control approach in industry over the past twenty-five years, is presented comprehensively in this unique book.
F. Borrelli, A. Bemporad, M. Morari
semanticscholar +4 more sources
Some open problems in matrix theory arising in linear systems and control
Linear Algebra and its Applications, 1992 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
D. Bernstein
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Compound matrices in systems and control theory: a tutorial [PDF]
MCSS. Mathematics of Control, Signals and Systems, 2022 The multiplicative and additive compounds of a matrix play an important role in several fields of mathematics including geometry, multi-linear algebra, graph theory, combinatorics, and the analysis of nonlinear time-varying dynamical systems.
Eyal Bar-Shalom +2 more
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Carleman inequality for a class of super strong degenerate parabolic operators and applications
Electronic Journal of Qualitative Theory of Differential Equations, 2023 In this paper, we present a new Carleman estimate for the adjoint equations associated to a class of super strong degenerate parabolic linear problems. Our approach considers a standard geometric imposition on the control domain, which can not be removed
Bruno Sérgio Araújo +2 more
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Exponential stability of discrete‐time delayed neural networks with saturated impulsive control
IET Control Theory & Applications, 2021 This paper examines the problem of the locally exponentially stability for impulsive discrete‐time delayed neural networks (IDDNNs) with actuator saturation.
Zhilong He +3 more
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IET Control Theory & Applications, 2021 This paper is concerned with necessary and sufficient conditions for stabilisability of time‐varying discrete‐time switched systems. Starting with an asymptotically stable function, an exponentially stable function and a uniformly exponentially stable ...
Junjie Lu, Zhikun She, Fucheng Liao
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Linear Systems can be Hard to Learn [PDF]
IEEE Conference on Decision and Control, 2021 In this paper, we investigate when system identification is statistically easy or hard, in the finite sample regime. Statistically easy to learn linear system classes have sample complexity that is polynomial with the system dimension.
A. Tsiamis, George Pappas
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Stabilizing non‐linear model predictive control using linear parameter‐varying embeddings and tubes
IET Control Theory & Applications, 2021 This paper proposes a model predictive control (MPC) approach for non‐linear systems where the non‐linear dynamics are embedded inside a linear parameter‐varying (LPV) representation. The non‐linear MPC problem is therefore replaced by an LPV MPC problem,
Jurre Hanema, Roland Tóth, Mircea Lazar
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Coordination of multi‐agent systems with arbitrary convergence time
IET Control Theory & Applications, 2021 This paper proposes fixed‐time solutions to several problems in multi‐agent systems. First, a fixed‐time consensus protocol is investigated and under the assumption that the lower bound of smallest positive eigenvalue of the Laplacian matrix is known in ...
Thanh Truong Nguyen +4 more
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IET Control Theory & Applications, 2021 Mathematical modelings of many electric and mechanical systems involve piecewise linear system. Piecewise linear system can possess a periodic solution called a limit‐cycle oscillation (LCO), which can seriously undermine the system performance ...
Yongeun Yoon, Eric N Johnson
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