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

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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 ...

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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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 ...

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 ...

Quadruped animal locomotion emerges from the interactions between the spinal central pattern generator (CPG), sensory feedback, and supraspinal drive signals from the brain. Computational models of CPGs have been widely used for investigating the spinal co ...

Nitrate is formed through the chemical production of gas-phase nitric acid and subsequent partitioning to the aerosol phase during the daytime. Many studies in the past separated these two aspects, even though they occur simultaneously in the atmosphere. T ...

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 ...

This paper presents a distributed shadow dynamic consensus (DSDC) framework to address the generation company (GENCO) strategic bidding problem in the independent system operator (ISO) operation. First, each sub-group is defined according to the communicat ...

Operation of a tokamak device requires the coordinated operation of a multitude of systems. The sequence of operations during a discharge is both too complex and fast for human interaction. A common way of operation is a predefined sequence of operations w ...

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 ...

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, ...

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 ...

This thesis is situated at the crossroads between machine learning and control engineering. Our contributions are both theoretical, through proposing a new uncertainty quantification methodology in a kernelized context; and experimental, through investigat ...

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

Chattez avec Graph Search

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