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Probabilistic movement primitive based motion learning for a lower limb exoskeleton with black-box optimization

Frontiers of Information Technology & Electronic Engineering 2023年第1期24卷 104-116页

作者： Jiaqi WANG Yongzhuo GAO Dongmei WU Wei DONG State Key Laboratory of Robotics and Systems Harbin Institute of TechnologyHarbin 150001China

As a wearable robot,an exoskeleton provides a direct transfer of mechanical power to assist or augment the wearer’s movement with an anthropomorphic configuration.When an exoskeleton is used to facilitate the wearer’s movement,a motion generation process often plays an important role in high-level control.One of the main challenges in this area is to generate in real time a reference trajectory that is parallel with human intention and can adapt to different situations.In this paper,we first describe a novel motion modeling method based on probabilistic movement primitive(ProMP)for a lower limb exoskeleton,which is a new and powerful representative tool for generating motion trajectories.To adapt the trajectory to different situations when the exoskeleton is used by different wearers,we propose a novel motion learning scheme based on black-box optimization(BBO)PIBB combined with ProMP.The motion model is first learned by ProMP offline,which can generate reference trajectories for use by exoskeleton controllers online.PIBB is adopted to learn and update the model for online trajectory generation,which provides the capability of adaptation of the system and eliminates the effects of uncertainties.Simulations and experiments involving six subjects using the lower limb exoskeleton HEXO demonstrate the effectiveness of the proposed methods.

关键词： Lower limb exoskeleton Human-robot interaction Motion learning Trajectory generation Movement primitive black-box optimization

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optimization of operating conditions in the steam turbine blade cascade using the black-box method

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Propulsion and Power Research 2023年第4期12卷 467-485页

作者： Vahid Sadrian Esmail Lakzian Davood Hoseinzade Behrad Haghighi M.M.Rashidi Heuy Dong Kim Center of Computational Energy Department of Mechanical EngineeringHakim Sabzevari UniversitySabzevarIran Department of Mechanical Engineering Andong National UniversityAndongSouth Korea Department of Mechanical Engineering Vali-e-Asr University of RafsanjanRafsanjanIran Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of ChinaChengdu 610054China Faculty of Science University of JohannesburgPO Box 524Auckland Park 2006South Africa

Water droplets cause corrosion and erosion,condensation loss,and thermal efficiency reduction in low-pressure steam turbines.In this study,multi-objective optimization was carried out using the black-box method through the automatic linking of a genetic algorithm(GA)and a computational fluid dynamics(CFD)code to find the optimal values of two design variables(inlet stagnation temperature and cascade pressure ratio)to reduce wetness in the last stages of turbines.The wet steam flow numerical model was used to calculate the optimization parameters,including wetness fraction rate,mean droplet radius,erosion rate,condensation loss rate,kinetic energy rate,and mass flow rate.Examining the validation results showed a good agreement between the experimental data and the numerical outcomes.According to the optimization results,the inlet stagnation temperature and the cascade pressure ratio were proposed to be 388.67(K)and 0.55(-),respectively.In particular,the suggested optimaltemperature and pressure ratio improved the liquid mass fraction and mean droplet radius by about 32%and 29%,respectively.Also,in the identified optimal operating state,the ratios of erosion,condensation loss,and kinetic energy fell by 76%,32.7%,and 15.85%,respectively,while the mass flow rate ratio rose by 0.68%.

关键词： black-box optimization Wet steam flow Steam turbine cascade Erosion rate Condensation loss

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Dynamical learning of a photonics quantum-state engineering process

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Advanced Photonics 2021年第6期3卷 48-58页

作者： Alessia Suprano Danilo Zia Emanuele Polino Taira Giordani Luca Innocenti Alessandro Ferraro Mauro Paternostro Nicolo Spagnolo Fabio Sciarrino Sapienza Universitàdi Roma Dipartimento di FisicaRomaItaly Palacky University Department of OpticsOlomoucCzech Republic Queen’s University Belfast School of Mathematics and PhysicsCentre for Theoretical AtomicMolecularand Optical PhysicsBelfastUnited Kingdom Universita degli Studi di Palermo Dipartimento di Fisica e Chimica-Emilio SegrePalermoItaly

Experimental engineering of high-dimensional quantum states is a crucial task for several quantum information protocols.However,a high degree of precision in the characterization of the noisy experimental apparatus is required to apply existing quantum-state engineering protocols.This is often lacking in practical scenarios,affecting the quality of the engineered states.We implement,experimentally,an automated adaptive optimization protocol to engineer photonic orbital angular momentum(OAM)states.The protocol,given a target output state,performs an online estimation of the quality of the currently produced states,relying on output measurement statistics,and determines how to tune the experimental parameters to optimize the state generation.To achieve this,the algorithm does not need to be imbued with a description of the generation apparatus itself.Rather,it operates in a fully black-box scenario,making the scheme applicable in a wide variety of circumstances.The handles controlled by the algorithm are the rotation angles of a series of waveplates and can be used to probabilistically generate arbitrary four-dimensional OAM states.We showcase our scheme on different target states both in classical and quantum regimes and prove its robustness to external perturbations on the control parameters.This approach represents a powerful tool for automated optimizations of noisy experimental tasks for quantum information protocols and technologies.

关键词： orbital angular momentum state engineering black-box optimization algorithm quantum walk

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