System identification is a quintessential measure for real-time analysis on kinematic characteristics for deep-sea mining vehicle, and thus to enhance the control performance and testing efficiency. In this study, the...System identification is a quintessential measure for real-time analysis on kinematic characteristics for deep-sea mining vehicle, and thus to enhance the control performance and testing efficiency. In this study, the system identification algorithm, recursive least square method with instrumental variables(IV-RLS), is tailored to model ‘Pioneer I’, a deep-sea mining vehicle which recently completed a 1305-meter-deep sea trial in the Xisha area of the South China Sea in August, 2021. The algorithm operates on the sensor data collected from the trial to obtain the vehicle’s kinematic model and accordingly design the parameter self-tuning controller. The performances demonstrate the accuracy of the model, and prove its generalization capability. With this model, the optimal controller has been designed, the control parameters have been self-tuned, and the response time and robustness of the system have been optimized,which validates the high efficiency on digital modelling for precision control of deep-sea mining vehicles.展开更多
The reproduction characteristics of 343 sows in six different breeding groups were analyzed to estimate the crossbreeding parameters. The results indicated that the ovulation rate and the weights of uterus and ovary a...The reproduction characteristics of 343 sows in six different breeding groups were analyzed to estimate the crossbreeding parameters. The results indicated that the ovulation rate and the weights of uterus and ovary are mainly determined by the additive genetic effects while the nonadditive genetic effects play an important role in embryonal traits and litter performance. The heterosis effects in the first litter are larger than those in the second litter because of heterosis x environment interaction. The results also showed the existence of a highly significant maternal heterosis effect on the fertility traits of sows.展开更多
For multisensor systems,when the model parameters and the noise variances are unknown,the consistent fused estimators of the model parameters and noise variances are obtained,based on the system identification algorit...For multisensor systems,when the model parameters and the noise variances are unknown,the consistent fused estimators of the model parameters and noise variances are obtained,based on the system identification algorithm,correlation method and least squares fusion criterion.Substituting these consistent estimators into the optimal weighted measurement fusion Kalman filter,a self-tuning weighted measurement fusion Kalman filter is presented.Using the dynamic error system analysis （DESA） method,the convergence of the self-tuning weighted measurement fusion Kalman filter is proved,i.e.,the self-tuning Kalman filter converges to the corresponding optimal Kalman filter in a realization.Therefore,the self-tuning weighted measurement fusion Kalman filter has asymptotic global optimality.One simulation example for a 4-sensor target tracking system verifies its effectiveness.展开更多
由于定风量空调机组(Constant Air Volume Air Handling Unit, CAVAHU)输出的新风量往往是固定的,当空调房间内的额定人员数量超员或不足时,会导致空调房间CO_(2)浓度测量值Cn高于室内CO_(2)浓度设定值Cn=Cn,set或新风负荷增大的状况。...由于定风量空调机组(Constant Air Volume Air Handling Unit, CAVAHU)输出的新风量往往是固定的,当空调房间内的额定人员数量超员或不足时,会导致空调房间CO_(2)浓度测量值Cn高于室内CO_(2)浓度设定值Cn=Cn,set或新风负荷增大的状况。对此提出了一种空调房间CO_(2)浓度二自由度内模分数阶PI控制策略和设计改进多目标人工蜂群算法(Improved Multi-Objective Artificial Bee Colony Algorithm, IMOABCA)对控制器参数实施整定的思路。首先,基于人工蜂群算法,分别对雇佣蜂和观察蜂引入自适应惯性权重和精英组策略,进行非线性递减和柯西变异的演变,并结合观察蜂搜索特性,将最小粒子角度引入外部档案集,获取相应的Pareto解集,设计IMOABCA,进而对控制器的3个参数进行整定,获得相应的最优值。最后,借助MATLAB工具,对该室内CO_(2)浓度的二自由度内模分数阶PI控制系统进行组态和仿真。结果表明:该室内CO_(2)浓度二自由度内模分数阶PI控制系统和IMOABCA是可行的,能够实现Cn=Cn,set的调节目的和获取控制器的3个参数最优值,提升室内CO_(2)浓度的调节品质。展开更多
基金financially supported by the Hainan Provincial Joint Project of Sanya Yazhou Bay Science and Technology City(Grant No.2021JJLH0078)the Science and Technology Commission of Shanghai Municipality (Grant No.19DZ1207300)the Major Projects of Strategic Emerging Industries in Shanghai。
文摘System identification is a quintessential measure for real-time analysis on kinematic characteristics for deep-sea mining vehicle, and thus to enhance the control performance and testing efficiency. In this study, the system identification algorithm, recursive least square method with instrumental variables(IV-RLS), is tailored to model ‘Pioneer I’, a deep-sea mining vehicle which recently completed a 1305-meter-deep sea trial in the Xisha area of the South China Sea in August, 2021. The algorithm operates on the sensor data collected from the trial to obtain the vehicle’s kinematic model and accordingly design the parameter self-tuning controller. The performances demonstrate the accuracy of the model, and prove its generalization capability. With this model, the optimal controller has been designed, the control parameters have been self-tuned, and the response time and robustness of the system have been optimized,which validates the high efficiency on digital modelling for precision control of deep-sea mining vehicles.
文摘The reproduction characteristics of 343 sows in six different breeding groups were analyzed to estimate the crossbreeding parameters. The results indicated that the ovulation rate and the weights of uterus and ovary are mainly determined by the additive genetic effects while the nonadditive genetic effects play an important role in embryonal traits and litter performance. The heterosis effects in the first litter are larger than those in the second litter because of heterosis x environment interaction. The results also showed the existence of a highly significant maternal heterosis effect on the fertility traits of sows.
基金supported by the National Natural Science Foundation of China(No.60874063)the Innovation Scientific Research Foundation for Graduate Students of Heilongjiang Province(No.YJSCX2008-018HLJ),and the Automatic Control Key Laboratory of Heilongjiang University
文摘For multisensor systems,when the model parameters and the noise variances are unknown,the consistent fused estimators of the model parameters and noise variances are obtained,based on the system identification algorithm,correlation method and least squares fusion criterion.Substituting these consistent estimators into the optimal weighted measurement fusion Kalman filter,a self-tuning weighted measurement fusion Kalman filter is presented.Using the dynamic error system analysis （DESA） method,the convergence of the self-tuning weighted measurement fusion Kalman filter is proved,i.e.,the self-tuning Kalman filter converges to the corresponding optimal Kalman filter in a realization.Therefore,the self-tuning weighted measurement fusion Kalman filter has asymptotic global optimality.One simulation example for a 4-sensor target tracking system verifies its effectiveness.
文摘由于定风量空调机组(Constant Air Volume Air Handling Unit, CAVAHU)输出的新风量往往是固定的,当空调房间内的额定人员数量超员或不足时,会导致空调房间CO_(2)浓度测量值Cn高于室内CO_(2)浓度设定值Cn=Cn,set或新风负荷增大的状况。对此提出了一种空调房间CO_(2)浓度二自由度内模分数阶PI控制策略和设计改进多目标人工蜂群算法(Improved Multi-Objective Artificial Bee Colony Algorithm, IMOABCA)对控制器参数实施整定的思路。首先,基于人工蜂群算法,分别对雇佣蜂和观察蜂引入自适应惯性权重和精英组策略,进行非线性递减和柯西变异的演变,并结合观察蜂搜索特性,将最小粒子角度引入外部档案集,获取相应的Pareto解集,设计IMOABCA,进而对控制器的3个参数进行整定,获得相应的最优值。最后,借助MATLAB工具,对该室内CO_(2)浓度的二自由度内模分数阶PI控制系统进行组态和仿真。结果表明:该室内CO_(2)浓度二自由度内模分数阶PI控制系统和IMOABCA是可行的,能够实现Cn=Cn,set的调节目的和获取控制器的3个参数最优值,提升室内CO_(2)浓度的调节品质。
