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A nonlinear model predictive control scheme for sensor fault tolerance in observation processes

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Publications associées (47)

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Dynamical System (DS)-based closed-loop control is a simple and effective way to generate reactive motion policies that well generalize to the robotic workspace, while retaining stability guarantees. Lately the formalism has been expanded in order to handl ...

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Vincent Kaufmann, Luca Giovanni Pattaroni, Guillaume Simon Joseph Drevon, Marc-Edouard Baptiste Grégoire Schultheiss, Fiona Ines Del Puppo

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Methods for Grid-aware Operation and Planning of Active Distribution Networks

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The thesis presents methods for controlling and planning distributed energy resources (DERs) in active distribution networks (ADNs). It deals with three main challenges: (i) developing and experimentally validating grid-aware real-time control frameworks, ...

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From Nano to Macro An overview of the IEEE Bio Image and Signal Processing Technical Committee

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The Bio Image and Signal Processing (BISP) Technical Committee (TC) of the IEEE Signal Processing Society (SPS) promotes activities within the broad technical field of biomedical image and signal processing. Areas of interest include medical and biological ...

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Homothetic Tube Model Predictive Control With Multi-Step Predictors

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We present a robust model predictive control (MPC) framework for linear systems facing bounded parametric uncertainty and bounded disturbances. Our approach deviates from standard MPC formulations by integrating multi-step predictors, which provide reduced ...

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Chronic pain presents a tremendous personal, societal, and financial burden. Treatment options are often limited to managing symptoms as opposed to treating the causes, and the consequences of pharmacological treatments can be immensely harmful as evidence ...

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Heating, Ventilation, and Air Conditioning (HVAC) Systems utilize much energy, accounting for 40% of total building energy use. The temperatures in buildings are commonly held within narrow limits, leading to higher energy use. Measurements from office bui ...

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Pore-pressure coefficients for unsaturated soils: generalized effective stress approach

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Geotechnical problems with total stress changes occurring rapidly relative to the soil consolidation time can be conveniently addressed using an effective stress-based approach. For saturated states, analytical formulations of pore-pressure coefficients ad ...

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Frequency Response Function (FRF)-based control synthesis methods for Linear Time-Invariant (LTI) systems have been widely used in control theory and industry. Recently, there has been renewed interest in these methods, employing numerical optimization too ...

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In this article, we consider decentralized optimization problems where agents have individual cost functions to minimize subject to subspace constraints that require the minimizers across the network to lie in low-dimensional subspaces. This constrained fo ...

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The decentralisation and unpredictability of new renewable energy sources require rethinking our energy system. Data-driven approaches, such as reinforcement learning (RL), have emerged as new control strategies for operating these systems, but they have n ...

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Background and Objective: Event-based analog-to-digital converters allow for sparse bio-signal acquisition, enabling local sub-Nyquist sampling frequency. However, aggressive event selection can cause the loss of important bio-markers, not recoverable with ...

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A Proximal-Point Lagrangian-Based Parallelizable Nonconvex Solver for Bilinear Model Predictive Control

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Nonlinear model predictive control (NMPC) has been widely adopted to manipulate bilinear systems with dynamics that include products of the inputs and the states. These systems are ubiquitous in chemical processes, mechanical systems, and quantum physics, ...

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Model Predictive Control for Multi-Agent Systems under Limited Communication and Time-Varying Network Topology

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In control system networks, reconfiguration of the controller when agents are leaving or joining the network is still an open challenge, in particular when operation constraints that depend on each agent's behavior must be met. Drawing our motivation from ...

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