The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Bastien Berret - One of the best experts on this subject based on the ideXlab platform.
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stochastic optimal Open Loop Control as a theory of force and impedance planning via muscle co contraction
PLOS Computational Biology, 2020Co-Authors: Bastien Berret, Frederic JeanAbstract:Understanding the underpinnings of biological motor Control is an important issue in movement neuroscience. Optimal Control theory is a leading framework to rationalize this problem in computational terms. Previously, optimal Control models have been devised either in deterministic or in stochastic settings to account for different aspects of motor Control (e.g. average behavior versus trial-to-trial variability). While these approaches have yielded valuable insights about motor Control, they typically fail in explaining muscle co-contraction. Co-contraction of a group of muscles associated to a motor function (e.g. agonist and antagonist muscles spanning a joint) contributes to modulate the mechanical impedance of the neuromusculoskeletal system (e.g. joint viscoelasticity) and is thought to be mainly under the influence of descending signals from the brain. Here we present a theory suggesting that one primary goal of motor planning may be to issue feedforward (Open-Loop) motor commands that optimally specify both force and impedance, according to noisy neuromusculoskeletal dynamics and to optimality criteria based on effort and variance. We show that the proposed framework naturally accounts for several previous experimental findings regarding the regulation of force and impedance via muscle co-contraction in the upper-limb. Stochastic optimal (closed-Loop) Control, preprogramming feedback gains but requiring on-line state estimation processes through long-latency sensory feedback Loops, may then complement this nominal feedforward motor command to fully determine the limb’s mechanical impedance. The proposed stochastic optimal Open-Loop Control theory may provide new insights about the general articulation of feedforward/feedback Control mechanisms and justify the occurrence of muscle co-contraction in the neural Control of movement.
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stochastic optimal Open Loop Control as a theory of force and impedance planning via muscle co contraction
bioRxiv, 2019Co-Authors: Bastien Berret, Frederic JeanAbstract:Abstract Understanding the underpinnings of biological motor Control is an important issue in movement neuroscience. Optimal Control theory is a leading framework to rationalize this problem in computational terms. Previously, optimal Control models have been devised either in deterministic or in stochastic settings to account for different aspects of motor Control (e.g. average behavior versus trial-to-trial variability). While these approaches have yielded valuable insights about motor Control, they typically fail explain a common phenomenon known as muscle co-contraction. Co-contraction of agonist and antagonist muscles contributes to modulate the mechanical impedance of the neuromusculoskeletal system (e.g. joint stiffness) and is thought to be mainly under the influence of descending signals from the brain. Here we present a theory suggesting that one primary goal of motor planning may be to issue feedforward (Open-Loop) motor commands that optimally specify both force and impedance, according to the noisy neuromusculoskeletal dynamics and to optimality criteria based on effort and variance. We show that the proposed framework naturally accounts for several previous experimental findings regarding the regulation of force and impedance via muscle co-contraction in the upper-limb. Optimal feedback (closedLoop) Control, preprogramming feedback gains but requiring on-line state estimation processes through long-latency sensory feedback Loops, may then complement this nominal feedforward motor command to fully determine the limb’s mechanical impedance. The stochastic optimal Open-Loop Control theory may provide new insights about the general articulation of feedforward/feedback Control mechanisms and justify the occurrence of muscle co-contraction in the neural Control of movement. Author summary This study presents a novel computational theory to explain the planning of force and impedance (e.g. stiffness) in the neural Control of movement. It assumes that one main goal of motor planning is to elaborate feedforward motor commands that determine both the force and the impedance required for the task at hand. These feedforward motor commands (i.e. that are defined prior to movement execution) are designed to minimize effort and variance costs considering the uncertainty arising from sensorimotor noise. A major outcome of this mathematical framework is the explanation of a long-known phenomenon called muscle co-contraction (i.e. the concurrent contraction of opposing muscles). Muscle co-contraction has been shown to occur in many situations but previous modeling works struggled to account for it. Although effortful, co-contraction contributes to increase the robustness of motor behavior (e.g. small variance) upstream of sophisticated optimal feedback Control processes that require state estimation from delayed sensory feedback to function. This work may have implications regarding our understanding of the neural Control of movement in computational terms. It also provides a theoretical ground to explain how to optimally plan force and impedance within a general and versatile framework.
Pierre Rouchon - One of the best experts on this subject based on the ideXlab platform.
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Flatness-based Control of a single qubit gate
IEEE Transactions on Automatic Control, 2008Co-Authors: Paulo Sergio Pereira Da Silva, Pierre RouchonAbstract:This work considers the Open-Loop Control problem of steering a two-level quantum system from any initial to any final condition. The model of this system evolves on the state space , having two inputs that correspond to the complex amplitude of a resonant laser field. A symmetry preserving flat output is constructed using a fully geometric construction and quaternion computations. Simulation results of this flatness-based Open-Loop Control are provided.
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flatness based Control of a single qubit gate
European Control Conference, 2007Co-Authors: Paulo Sergio Pereira Da Silva, Pierre RouchonAbstract:This work considers the Open-Loop Control problem of steering a two level quantum system, from an initial to a final condition. The model of this system evolves on the state space X = SU(2), having two inputs that corresponds to the complex amplitude of a resonant laser field. A symmetry preserving flat output is constructed using a fully geometric setting. Using a particular parametrization, some simulation results of the flatness based Control are presented for the Hadamard gate.
Frederic Jean - One of the best experts on this subject based on the ideXlab platform.
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stochastic optimal Open Loop Control as a theory of force and impedance planning via muscle co contraction
PLOS Computational Biology, 2020Co-Authors: Bastien Berret, Frederic JeanAbstract:Understanding the underpinnings of biological motor Control is an important issue in movement neuroscience. Optimal Control theory is a leading framework to rationalize this problem in computational terms. Previously, optimal Control models have been devised either in deterministic or in stochastic settings to account for different aspects of motor Control (e.g. average behavior versus trial-to-trial variability). While these approaches have yielded valuable insights about motor Control, they typically fail in explaining muscle co-contraction. Co-contraction of a group of muscles associated to a motor function (e.g. agonist and antagonist muscles spanning a joint) contributes to modulate the mechanical impedance of the neuromusculoskeletal system (e.g. joint viscoelasticity) and is thought to be mainly under the influence of descending signals from the brain. Here we present a theory suggesting that one primary goal of motor planning may be to issue feedforward (Open-Loop) motor commands that optimally specify both force and impedance, according to noisy neuromusculoskeletal dynamics and to optimality criteria based on effort and variance. We show that the proposed framework naturally accounts for several previous experimental findings regarding the regulation of force and impedance via muscle co-contraction in the upper-limb. Stochastic optimal (closed-Loop) Control, preprogramming feedback gains but requiring on-line state estimation processes through long-latency sensory feedback Loops, may then complement this nominal feedforward motor command to fully determine the limb’s mechanical impedance. The proposed stochastic optimal Open-Loop Control theory may provide new insights about the general articulation of feedforward/feedback Control mechanisms and justify the occurrence of muscle co-contraction in the neural Control of movement.
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stochastic optimal Open Loop Control as a theory of force and impedance planning via muscle co contraction
bioRxiv, 2019Co-Authors: Bastien Berret, Frederic JeanAbstract:Abstract Understanding the underpinnings of biological motor Control is an important issue in movement neuroscience. Optimal Control theory is a leading framework to rationalize this problem in computational terms. Previously, optimal Control models have been devised either in deterministic or in stochastic settings to account for different aspects of motor Control (e.g. average behavior versus trial-to-trial variability). While these approaches have yielded valuable insights about motor Control, they typically fail explain a common phenomenon known as muscle co-contraction. Co-contraction of agonist and antagonist muscles contributes to modulate the mechanical impedance of the neuromusculoskeletal system (e.g. joint stiffness) and is thought to be mainly under the influence of descending signals from the brain. Here we present a theory suggesting that one primary goal of motor planning may be to issue feedforward (Open-Loop) motor commands that optimally specify both force and impedance, according to the noisy neuromusculoskeletal dynamics and to optimality criteria based on effort and variance. We show that the proposed framework naturally accounts for several previous experimental findings regarding the regulation of force and impedance via muscle co-contraction in the upper-limb. Optimal feedback (closedLoop) Control, preprogramming feedback gains but requiring on-line state estimation processes through long-latency sensory feedback Loops, may then complement this nominal feedforward motor command to fully determine the limb’s mechanical impedance. The stochastic optimal Open-Loop Control theory may provide new insights about the general articulation of feedforward/feedback Control mechanisms and justify the occurrence of muscle co-contraction in the neural Control of movement. Author summary This study presents a novel computational theory to explain the planning of force and impedance (e.g. stiffness) in the neural Control of movement. It assumes that one main goal of motor planning is to elaborate feedforward motor commands that determine both the force and the impedance required for the task at hand. These feedforward motor commands (i.e. that are defined prior to movement execution) are designed to minimize effort and variance costs considering the uncertainty arising from sensorimotor noise. A major outcome of this mathematical framework is the explanation of a long-known phenomenon called muscle co-contraction (i.e. the concurrent contraction of opposing muscles). Muscle co-contraction has been shown to occur in many situations but previous modeling works struggled to account for it. Although effortful, co-contraction contributes to increase the robustness of motor behavior (e.g. small variance) upstream of sophisticated optimal feedback Control processes that require state estimation from delayed sensory feedback to function. This work may have implications regarding our understanding of the neural Control of movement in computational terms. It also provides a theoretical ground to explain how to optimally plan force and impedance within a general and versatile framework.
J Lopezcoronado - One of the best experts on this subject based on the ideXlab platform.
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neuro inspired spike based motion from dynamic vision sensor to robot motor Open Loop Control through spike vite
Sensors, 2013Co-Authors: Fernando Perezpena, Arturo Morgadoestevez, A Linaresbarranco, A Jimenezfernandez, F Gomezrodriguez, Gabriel Jimenezmoreno, J LopezcoronadoAbstract:In this paper we present a complete spike-based architecture: from a Dynamic Vision Sensor (retina) to a stereo head robotic platform. The aim of this research is to reproduce intended movements performed by humans taking into account as many features as possible from the biological point of view. This paper fills the gap between current spike silicon sensors and robotic actuators by applying a spike processing strategy to the data flows in real time. The architecture is divided into layers: the retina, visual information processing, the trajectory generator layer which uses a neuroinspired algorithm (SVITE) that can be replicated into as many times as DoF the robot has; and finally the actuation layer to supply the spikes to the robot (using PFM). All the layers do their tasks in a spike-processing mode, and they communicate each other through the neuro-inspired AER protocol. The Open-Loop Controller is implemented on FPGA using AER interfaces developed by RTC Lab. Experimental results reveal the viability of this spike-based Controller. Two main advantages are: low hardware resources (2% of a Xilinx Spartan 6) and power requirements (3.4 W) to Control a robot with a high number of DoF (up to 100 for a Xilinx Spartan 6). It also evidences the suitable use of AER as a communication protocol between processing and actuation.
Bruce Macintosh - One of the best experts on this subject based on the ideXlab platform.
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Performance of MEMS-based visible-light adaptive optics at Lick Observatory: closed- and Open-Loop Control
Proceedings of SPIE, 2010Co-Authors: Katie M. Morzinski, Bryant Grigsby, Donald Gavel, Marc Reinig, Luke Johnson, Daren Dillon, Bruce MacintoshAbstract:At the University of California's Lick Observatory, we have implemented an on-sky testbed for next-generation adaptive optics (AO) technologies. The Visible-Light Laser Guidestar Experiments instrument (ViLLaGEs) includes visible-light AO, a micro-electro-mechanical-systems (MEMS) deformable mirror, and Open-Loop Control of said MEMS on the 1-meter Nickel telescope at Mt. Hamilton. (Open-Loop in this sense refers to the MEMS being separated optically from the wavefront sensing path; the MEMS is still included in the Control Loop.) Future upgrades include predictive Control with wind estimation and pyramid wavefront sensing. Our unique optical layout allows the wavefronts along the Open- and closed-Loop paths to be measured simultaneously, facilitating comparison between the two Control methods. In this paper we evaluate the performance of ViLLaGEs in Openand closed-Loop Control, finding that both Control methods give equivalent Strehl ratios of up to ~ 7% in I-band and similar rejection of temporal power. Therefore, we find that Open-Loop Control of MEMS on-sky is as effective as closed-Loop Control. Furthermore, after operating the system for three years, we find MEMS technology to function well in the observatory environment. We construct an error budget for the system, accounting for 130 nm of wavefront error out of 190 nm error in the science-camera PSFs. We find that the dominant known term is internal static error, and that the known contributions to the error budget from Open-Loop Control (MEMS model, position repeatability, hysteresis, and WFS linearity) are negligible.
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performance of mems based visible light adaptive optics at lick observatory closed and Open Loop Control
arXiv: Instrumentation and Methods for Astrophysics, 2010Co-Authors: Katie M. Morzinski, Bryant Grigsby, Donald Gavel, Marc Reinig, Luke Johnson, Bruce Macintosh, Daren DillonAbstract:At the University of California's Lick Observatory, we have implemented an on-sky testbed for next-generation adaptive optics (AO) technologies. The Visible-Light Laser Guidestar Experiments instrument (ViLLaGEs) includes visible-light AO, a micro-electro-mechanical-systems (MEMS) deformable mirror, and Open-Loop Control of said MEMS on the 1-meter Nickel telescope at Mt. Hamilton. In this paper we evaluate the performance of ViLLaGEs in Open- and closed-Loop Control, finding that both Control methods give equivalent Strehl ratios of up to ~ 7% in I-band and similar rejection of temporal power. Therefore, we find that Open-Loop Control of MEMS on-sky is as effective as closed-Loop Control. Furthermore, after operating the system for three years, we find MEMS technology to function well in the observatory environment. We construct an error budget for the system, accounting for 130 nm of wavefront error out of 190 nm error in the science-camera PSFs. We find that the dominant known term is internal static error, and that the known contributions to the error budget from Open-Loop Control (MEMS model, position repeatability, hysteresis, and WFS linearity) are negligible.
