This paper considers the necessary condition of the parameter identification problem dudt=(A+B(q))u u(0)=x x∈X with the cost functional J(q)≡12∫ T 0‖Cu(t;q)-y(t)‖ 2 H d t It is proved that the optimal...This paper considers the necessary condition of the parameter identification problem dudt=(A+B(q))u u(0)=x x∈X with the cost functional J(q)≡12∫ T 0‖Cu(t;q)-y(t)‖ 2 H d t It is proved that the optimal estimate q 0 is determined by the optimal system which consists of the sate equation,the adjoint equation and the optimal condition.展开更多
We introduce the artificial fish swarm algorithm for heading motion model identification and control parameter optimization problems for the“Ocean Rambler”unmanned wave glider(UWG).First,under certain assumptions,th...We introduce the artificial fish swarm algorithm for heading motion model identification and control parameter optimization problems for the“Ocean Rambler”unmanned wave glider(UWG).First,under certain assumptions,the rigid-flexible multi-body system of the UWG was simplified as a rigid system composed of“thruster+float body”,based on which a planar motion model of the UWG was established.Second,we obtained the model parameters using an empirical method combined with parameter identification,which means that some parameters were estimated by the empirical method.In view of the specificity and importance of the heading control,heading model parameters were identified through the artificial fish swarm algorithm based on tank test data,so that we could take full advantage of the limited trial data to factually describe the dynamic characteristics of the system.Based on the established heading motion model,parameters of the heading S-surface controller were optimized using the artificial fish swarm algorithm.Heading motion comparison and maritime control experiments of the“Ocean Rambler”UWG were completed.Tank test results show high precision of heading motion prediction including heading angle and yawing angular velocity.The UWG shows good control performance in tank tests and sea trials.The efficiency of the proposed method is verified.展开更多
Based on 1,3-propanediol production from batch fermentation of glycerol by Klebsiella pneurnoniae, a multistage dynamic system and its parameter identification are discussed in this paper. The batch fermentation proce...Based on 1,3-propanediol production from batch fermentation of glycerol by Klebsiella pneurnoniae, a multistage dynamic system and its parameter identification are discussed in this paper. The batch fermentation process is divided into three stages exhibiting different dynamic behaviors and characteristics, from which a corresponding nonlinear multistage dynamic system is built. We then propose a parameter identification optimization model whose objective function is the average relative error. The model is solved by particle swarm optimization weighted by inertia, and the result shows that the relative error of our proposed model is 2-10%smaller than those of existing models.展开更多
基金Supported by the National Natural Science Foundation of China(No.697740 1 2 )
文摘This paper considers the necessary condition of the parameter identification problem dudt=(A+B(q))u u(0)=x x∈X with the cost functional J(q)≡12∫ T 0‖Cu(t;q)-y(t)‖ 2 H d t It is proved that the optimal estimate q 0 is determined by the optimal system which consists of the sate equation,the adjoint equation and the optimal condition.
基金Project(51779052)supported by the National Natural Science Foundation of ChinaProject(QC2016062)supported by the Natural Science Foundation of Heilongjiang Province,China+2 种基金Project(614221503091701)supported by the Research Fund from Science and Technology on Underwater Vehicle Laboratory,ChinaProject(LBH-Q17046)supported by the Heilongjiang Postdoctoral Funds for Scientific Research Initiation,ChinaProject(HEUCFP201741)supported by the Fundamental Research Funds for the Central Universities,China
文摘We introduce the artificial fish swarm algorithm for heading motion model identification and control parameter optimization problems for the“Ocean Rambler”unmanned wave glider(UWG).First,under certain assumptions,the rigid-flexible multi-body system of the UWG was simplified as a rigid system composed of“thruster+float body”,based on which a planar motion model of the UWG was established.Second,we obtained the model parameters using an empirical method combined with parameter identification,which means that some parameters were estimated by the empirical method.In view of the specificity and importance of the heading control,heading model parameters were identified through the artificial fish swarm algorithm based on tank test data,so that we could take full advantage of the limited trial data to factually describe the dynamic characteristics of the system.Based on the established heading motion model,parameters of the heading S-surface controller were optimized using the artificial fish swarm algorithm.Heading motion comparison and maritime control experiments of the“Ocean Rambler”UWG were completed.Tank test results show high precision of heading motion prediction including heading angle and yawing angular velocity.The UWG shows good control performance in tank tests and sea trials.The efficiency of the proposed method is verified.
基金Acknowledgments This work was supported by the National Natural Science Foundation of China （Grant No. 10871033）, ＂863＂ Program （No. 2007AA02Z208） and ＂973＂ Program （No. 2007CB71430c）.
文摘Based on 1,3-propanediol production from batch fermentation of glycerol by Klebsiella pneurnoniae, a multistage dynamic system and its parameter identification are discussed in this paper. The batch fermentation process is divided into three stages exhibiting different dynamic behaviors and characteristics, from which a corresponding nonlinear multistage dynamic system is built. We then propose a parameter identification optimization model whose objective function is the average relative error. The model is solved by particle swarm optimization weighted by inertia, and the result shows that the relative error of our proposed model is 2-10%smaller than those of existing models.
