This paper proposes a self-tuning iterative learning control method for the attitude control of a flexible solar power satellite,which is simplified as an Euler-Bernoulli beam moving in space.An orbit-attitude-structu...This paper proposes a self-tuning iterative learning control method for the attitude control of a flexible solar power satellite,which is simplified as an Euler-Bernoulli beam moving in space.An orbit-attitude-structure coupled dynamic model is established using absolute nodal coordinate formulation,and the attitude control is performed using two control moment gyros.In order to improve control accuracy of the classic proportional-derivative control method,a switched iterative learning control method is presented using the control moments of the previous periods as feedforward control moments.Although the iterative learning control is a model-free method,the parameters of the controller must be selected manually.This would be undesirable for complicated systems with multiple control parameters.Thus,a self-tuning method is proposed using fuzzy logic.The control frequency of the controller is adjusted according to the averaged control error in one control period.Simulation results show that the proposed controller increases the control accuracy greatly and reduces the influence of measurement noise.Moreover,the control frequency is automatically adjusted to a suitable value.展开更多
Computer vision,a scientific discipline enables machines to perceive visual information,aims to supplant human eyes in tasksencompassing object recognition,localization,and tracking.In traditional educational settings...Computer vision,a scientific discipline enables machines to perceive visual information,aims to supplant human eyes in tasksencompassing object recognition,localization,and tracking.In traditional educational settings,instructors or evaluators evaluate teachingperformance based on subjective judgment.However,with the continuous advancements in computer vision technology,it becomes increasinglycrucial for computers to take on the role of judges in obtaining vital information and making unbiased evaluations.Against thisbackdrop,this paper proposes a deep learning-based approach for evaluating lecture posture.First,feature information is extracted fromvarious dimensions,including head position,hand gestures,and body posture,using a human pose estimation algorithm.Second,a machinelearning-based regression model is employed to predict machine scores by comparing the extracted features with expert-assigned humanscores.The correlation between machine scores and human scores is investigated through experiment and analysis,revealing a robustoverall correlation(0.6420)between predicted machine scores and human scores.Under ideal scoring conditions(100 points),approximately51.72%of predicted machine scores exhibited deviations within a range of 10 points,while around 81.87%displayed deviationswithin a range of 20 points;only a minimal percentage of 0.12%demonstrated deviations exceeding the threshold of 50 points.Finally,tofurther optimize performance,additional features related to bodily movements are extracted by introducing facial expression recognitionand gesture recognition algorithms.The fusion of multiple models resulted in an overall average correlation improvement of 0.0226.展开更多
基金supported by the Guangdong Basic and Applied Basic Research Foundation(No.2019A1515110730)the Young Elite Scientists Sponsorship Program by China Association for Science and Technology(No.2021QNRC001)the Fundamental Research Funds for the Central Universities of Sun Yat-sen University(No.22qntd0703)。
文摘This paper proposes a self-tuning iterative learning control method for the attitude control of a flexible solar power satellite,which is simplified as an Euler-Bernoulli beam moving in space.An orbit-attitude-structure coupled dynamic model is established using absolute nodal coordinate formulation,and the attitude control is performed using two control moment gyros.In order to improve control accuracy of the classic proportional-derivative control method,a switched iterative learning control method is presented using the control moments of the previous periods as feedforward control moments.Although the iterative learning control is a model-free method,the parameters of the controller must be selected manually.This would be undesirable for complicated systems with multiple control parameters.Thus,a self-tuning method is proposed using fuzzy logic.The control frequency of the controller is adjusted according to the averaged control error in one control period.Simulation results show that the proposed controller increases the control accuracy greatly and reduces the influence of measurement noise.Moreover,the control frequency is automatically adjusted to a suitable value.
基金Supported by the Open Fund of Key Laboratory of Anhui Higher Education Institutes(CS2021-07)the National Natural Science Foundation of China(61701004)the Outstanding Young Talents Support Program of Anhui Province(gxyq2021178)。
文摘Computer vision,a scientific discipline enables machines to perceive visual information,aims to supplant human eyes in tasksencompassing object recognition,localization,and tracking.In traditional educational settings,instructors or evaluators evaluate teachingperformance based on subjective judgment.However,with the continuous advancements in computer vision technology,it becomes increasinglycrucial for computers to take on the role of judges in obtaining vital information and making unbiased evaluations.Against thisbackdrop,this paper proposes a deep learning-based approach for evaluating lecture posture.First,feature information is extracted fromvarious dimensions,including head position,hand gestures,and body posture,using a human pose estimation algorithm.Second,a machinelearning-based regression model is employed to predict machine scores by comparing the extracted features with expert-assigned humanscores.The correlation between machine scores and human scores is investigated through experiment and analysis,revealing a robustoverall correlation(0.6420)between predicted machine scores and human scores.Under ideal scoring conditions(100 points),approximately51.72%of predicted machine scores exhibited deviations within a range of 10 points,while around 81.87%displayed deviationswithin a range of 20 points;only a minimal percentage of 0.12%demonstrated deviations exceeding the threshold of 50 points.Finally,tofurther optimize performance,additional features related to bodily movements are extracted by introducing facial expression recognitionand gesture recognition algorithms.The fusion of multiple models resulted in an overall average correlation improvement of 0.0226.
