Robot interaction control with variable impedance parameters may conform to task requirements during continuous interaction with dynamic environments.Iterative learning(IL)is effective to learn desired impedance param...Robot interaction control with variable impedance parameters may conform to task requirements during continuous interaction with dynamic environments.Iterative learning(IL)is effective to learn desired impedance parameters for robots under unknown environments,and Gaussian process(GP)is a nonparametric Bayesian approach that models complicated functions with provable confidence using limited data.In this paper,we propose an impedance IL method enhanced by a sparse online Gaussian process(SOGP)to speed up learning convergence and improve generalization.The SOGP for variable impedance modeling is updated in the same iteration by removing similar data points from previous iterations while learning impedance parameters in multiple iterations.The proposed IL-SOGP method is verified by high-fidelity simulations of a collaborative robot with 7 degrees of freedom based on the admittance control framework.It is shown that the proposed method accelerates iterative convergence and improves generalization compared to the classical IL-based impedance learning method.展开更多
A novel model named Multi-scale Gaussian Processes (MGP) is proposed. Motivated by the ideas of multi-scale representations in the wavelet theory, in the new model, a Gaussian process is represented at a scale by a li...A novel model named Multi-scale Gaussian Processes (MGP) is proposed. Motivated by the ideas of multi-scale representations in the wavelet theory, in the new model, a Gaussian process is represented at a scale by a linear basis that is composed of a scale function and its different translations. Finally the distribution of the targets of the given samples can be obtained at different scales. Compared with the standard Gaussian Processes (GP) model, the MGP model can control its complexity conveniently just by adjusting the scale pa-rameter. So it can trade-off the generalization ability and the empirical risk rapidly. Experiments verify the fea-sibility of the MGP model, and exhibit that its performance is superior to the GP model if appropriate scales are chosen.展开更多
Knowledge-based engineering(KBE) has made success in automobile and molding design industry, and it is introduced into the ship structural design in this paper. From the implementation of KBE, the deterministic design...Knowledge-based engineering(KBE) has made success in automobile and molding design industry, and it is introduced into the ship structural design in this paper. From the implementation of KBE, the deterministic design solutions for both rules design method(RDM) and interpolation design method(IDM) are generated. The corresponding finite element model is generated. Gaussian process(GP) is then employed to build the surrogate model for finite element analysis, in order to increase efficiency and maintain accuracy at the same time, and the multi-modal adaptive importance sampling method is adopted to calculate the corresponding structural reliability.An example is given to validate the proposed method. Finally, the reliabilities of the structures' strength caused by uncertainty lying in water corrosion, static and wave moments are calculated, and the ship structures are optimized to resist the water corrosion by multi-island genetic algorithm. Deterministic design results from the RDM and IDM are compared with each separate robust design result. The proposed method shows great efficiency and accuracy.展开更多
It has long been thought that bioprocess, with their inherent measurement difficulties and complex dynamics, posed almost insurmountable problems to engineers. A novel software sensor is proposed to make more effectiv...It has long been thought that bioprocess, with their inherent measurement difficulties and complex dynamics, posed almost insurmountable problems to engineers. A novel software sensor is proposed to make more effective use of those meas- urements that are already available, which enable improvement in fermentation process control. The proposed method is based on mixtures of Gaussian processes (GP) with expectation maximization (EM) algorithm employed for parameter estimation of mixture of models. The mixture model can alleviate computational complexity of GP and also accord with changes of operating condition in fermentation processes, i.e., it would certainly be able to examine what types of process-knowledge would be most relevant for local models’ specific operating points of the process and then combine them into a global one. Demonstrated by on-line estimate of yeast concentration in fermentation industry as an example, it is shown that soft sensor based state estimation is a powerful technique for both enhancing automatic control performance of biological systems and implementing on-line moni- toring and optimization.展开更多
基金supported in part by the National Research Foundation of Korea(NRF)Grant Funded by the Korea Government(MSIT)(RS-2025-00555064).Recommended by Associate Editor Zengguang Hou.
文摘Robot interaction control with variable impedance parameters may conform to task requirements during continuous interaction with dynamic environments.Iterative learning(IL)is effective to learn desired impedance parameters for robots under unknown environments,and Gaussian process(GP)is a nonparametric Bayesian approach that models complicated functions with provable confidence using limited data.In this paper,we propose an impedance IL method enhanced by a sparse online Gaussian process(SOGP)to speed up learning convergence and improve generalization.The SOGP for variable impedance modeling is updated in the same iteration by removing similar data points from previous iterations while learning impedance parameters in multiple iterations.The proposed IL-SOGP method is verified by high-fidelity simulations of a collaborative robot with 7 degrees of freedom based on the admittance control framework.It is shown that the proposed method accelerates iterative convergence and improves generalization compared to the classical IL-based impedance learning method.
文摘A novel model named Multi-scale Gaussian Processes (MGP) is proposed. Motivated by the ideas of multi-scale representations in the wavelet theory, in the new model, a Gaussian process is represented at a scale by a linear basis that is composed of a scale function and its different translations. Finally the distribution of the targets of the given samples can be obtained at different scales. Compared with the standard Gaussian Processes (GP) model, the MGP model can control its complexity conveniently just by adjusting the scale pa-rameter. So it can trade-off the generalization ability and the empirical risk rapidly. Experiments verify the fea-sibility of the MGP model, and exhibit that its performance is superior to the GP model if appropriate scales are chosen.
基金The authors would like to thank the research group that took part in the study for their generous cooperation. Project 50965003 supported by National Natural Science Foundation of China.
基金the Project of Ministry of Finance andMinistry of Education of China(Nos.200512 and201335)the State Key Laboratory of Ocean Engineering Foundation of Shanghai Jiao Tong University(No.GKZD010053-10)
文摘Knowledge-based engineering(KBE) has made success in automobile and molding design industry, and it is introduced into the ship structural design in this paper. From the implementation of KBE, the deterministic design solutions for both rules design method(RDM) and interpolation design method(IDM) are generated. The corresponding finite element model is generated. Gaussian process(GP) is then employed to build the surrogate model for finite element analysis, in order to increase efficiency and maintain accuracy at the same time, and the multi-modal adaptive importance sampling method is adopted to calculate the corresponding structural reliability.An example is given to validate the proposed method. Finally, the reliabilities of the structures' strength caused by uncertainty lying in water corrosion, static and wave moments are calculated, and the ship structures are optimized to resist the water corrosion by multi-island genetic algorithm. Deterministic design results from the RDM and IDM are compared with each separate robust design result. The proposed method shows great efficiency and accuracy.
基金Project (No. 2002AA412010) supported by the National High-TechResearch and Development Program (863) of China
文摘It has long been thought that bioprocess, with their inherent measurement difficulties and complex dynamics, posed almost insurmountable problems to engineers. A novel software sensor is proposed to make more effective use of those meas- urements that are already available, which enable improvement in fermentation process control. The proposed method is based on mixtures of Gaussian processes (GP) with expectation maximization (EM) algorithm employed for parameter estimation of mixture of models. The mixture model can alleviate computational complexity of GP and also accord with changes of operating condition in fermentation processes, i.e., it would certainly be able to examine what types of process-knowledge would be most relevant for local models’ specific operating points of the process and then combine them into a global one. Demonstrated by on-line estimate of yeast concentration in fermentation industry as an example, it is shown that soft sensor based state estimation is a powerful technique for both enhancing automatic control performance of biological systems and implementing on-line moni- toring and optimization.
