A T-S fuzzy model with two rules is established to exactly describe the nonlinear uncertain heave dynamics of underwater vehicles with bounded heave speed.A single linear-matrix-inequality-based (LMI-based) state feed...A T-S fuzzy model with two rules is established to exactly describe the nonlinear uncertain heave dynamics of underwater vehicles with bounded heave speed.A single linear-matrix-inequality-based (LMI-based) state feedback controller is then synthesized to guarantee the global stability of the depth control system.Simulation results verify the effectiveness of the proposed approach in comparison with linear-quadratic regulator (LQR) method.Nonlinear disturbance observer is appended to the system when the underwater vehicles are affected by the gravity-buoyancy imbalance.The two-stage control method is effective to stabilize an uncertain system with both parameter uncertainties and external disturbances.展开更多
This paper presents an environmental-friendly robotic system mimicking the undulating fins of a fish.To mimic the actual flexible fin of real fish,a fin-like mechanism with a series of connecting linkages is modeled a...This paper presents an environmental-friendly robotic system mimicking the undulating fins of a fish.To mimic the actual flexible fin of real fish,a fin-like mechanism with a series of connecting linkages is modeled and attached to the robotic fish,by virtue of a specially designed strip.Each link is able to turn and slide with respect to the adjacent link.These driving linkages are then used to form a mechanical fin consisting of several fin segments,which are able to produce undulations,similar to those produced by the actual fish fins.Owing to the modular and re-configurable design of the mechanical fin,we are able to construct biomimetic robotic fish with various swimming modes by fin undulations.Some qualitative and workspace observations by experiments of the robotic fish are shown and discussed.展开更多
The net buoyancy of the deep-sea self-holding intelligent buoy(DSIB)will change with depth due to pressure hull deformation in the deep submergence process.The net buoyancy changes will affect the hovering performance...The net buoyancy of the deep-sea self-holding intelligent buoy(DSIB)will change with depth due to pressure hull deformation in the deep submergence process.The net buoyancy changes will affect the hovering performance of the DSIB.To make the DSIB have better resistance to the external disturbances caused by the net buoyancy and water resistance,a depth controller was designed to improve the depth positioning based on the active disturbance rejection control(ADRC).Firstly,a dynamic model was established based on the motion analysis of the DSIB.In addition,the extended state observer(ESO)and nonlinear state error feedback controller were designed based on the Lyapunov stability principle.Finally,semi-physical simulations for the depth control process were made by using the ADRC depth controller and traditional PID depth controller,respectively.The results of the semi-physical simulations indicate that the depth controller based on the ADRC can achieve the predefined depth control under the external disturbances.Compared with the traditional PID depth controller,the overshoot of the ADRC depth controller is 1.74%,and the depth error is within 0.5%.It not only has a better control capability to restrain the overshoot and shock caused by the external disturbances,but also can improve intelligence of the DSIB under the depth tracking task.展开更多
Aiming at the contradiction between the depth control accuracy and the energy consumption of the self-sustaining intelligent buoy,a low energy consumption depth control method based on historical array for real-time g...Aiming at the contradiction between the depth control accuracy and the energy consumption of the self-sustaining intelligent buoy,a low energy consumption depth control method based on historical array for real-time geostrophic oceanography(Argo)data is proposed.As known from the buoy kinematic model,the volume of the external oil sac only depends on the density and temperature of seawater at hovering depth.Hence,we use historical Argo data to extract the fitting curves of density and temperature,and obtain the relationship between the hovering depth and the volume of the external oil sac.Genetic algorithm is used to carry out the optimal energy consumption motion planning for the depth control process,and the specific motion strategy of depth control process is obtained.Compared with dual closed-loop fuzzy PID control method and radial basis function(RBF)-PID method,the proposed method reduces energy consumption to 1/50 with the same accuracy.Finally,a hardware-in-the-loop simulation system was used to verify this method.When the error caused by fitting curves is not considered,the average error is 2.62 m,the energy consumption is 3.214×10^(4)J,and the error of energy consumption is only 0.65%.It shows the effectiveness and reliability of the method as well as the advantages of comprehensively considering the accuracy and energy consumption.展开更多
Aimed at uncertainties and model's impreciseness, nonlinearity and time-variability of depth control system in autonomous underwater vehicle (AUV), a depth predictive control method was put forward based on rough ...Aimed at uncertainties and model's impreciseness, nonlinearity and time-variability of depth control system in autonomous underwater vehicle (AUV), a depth predictive control method was put forward based on rough set (RS) and least squares support vector machine (LSSVM). By using RS theory, the monitor data attribute of AUV was reduced to eliminate the redundant information and to improve efficiency. Then, LSSVM model was trained by using the reduced rules, and its parameters were optimized by using chaos theory for the higher accurate control. Taken an AUV typed NPS Phoenix as an example, its depth step response, horizontal rudder and pitch change were simulated. The simulation results show that the method improves the model's accuracy and has better real-time response, fault-tolerant ability, reliability and strong anti-interfere capability.展开更多
Underwater vehicle plays an important role in ocean engineering. Depth control by fin is one of the difficulties for underwater vehicle in motion control. Depth control is indirect due to the freedom coupling between ...Underwater vehicle plays an important role in ocean engineering. Depth control by fin is one of the difficulties for underwater vehicle in motion control. Depth control is indirect due to the freedom coupling between trim and axial motion. It includes the method of dynamic analysis and lift-resistance-coefficient experiment and theory algorithm. By considering the current speed and depth deviation, comprehensive interpretation is used in object-planning instruction. Expected depth is transformed into expected trim. Dynamic output fluctuation can be avoided, which is caused by linear mapping of deviation. It is steady and accurate for the motion of controlled underwater vehicles. The feasibility and efficiency of the control method are testified in the pool and natural area for experiments.展开更多
A new method for depth control was developed for a spilled oil and blow out gas tracking autonomous buoy robot called SOTAB-I by adjusting its buoyancy control device. It is aimed to work for any target depth. The new...A new method for depth control was developed for a spilled oil and blow out gas tracking autonomous buoy robot called SOTAB-I by adjusting its buoyancy control device. It is aimed to work for any target depth. The new method relies on buoyancy variation model with a depth that was established based on experimental data. The depth controller was verified at sea experiments in the Toyama Bay in Japan and showed good performance. The method could further be adapted to altitude control by combining the altitude data measured from bottom tracking through a progressive depth control. The method was verified at the sea experiments in Toyama in March 2016 and showed that the algorithm succeeded to bring the robot to the target altitude.展开更多
An intelligent system including both a neural network(NN) and a self adjusting fuzzy controller(FC) for modeling and control of the penetration depth during gas tungsten arc welding(GTAW) process is presented in this...An intelligent system including both a neural network(NN) and a self adjusting fuzzy controller(FC) for modeling and control of the penetration depth during gas tungsten arc welding(GTAW) process is presented in this paper. The discussion is mainly focused on two parts. One is the modeling of the penetration depth with NN. A visual sensor CCD is used to obtain the image of the molten pool. A neural network model is established to estimate the penetration depth from the welding current, pool width and seam gap. It is demonstrated that the proposed neural network can produce highly complex nonlinear multi variable model of the GTAW process that offer the accurate prediction of welding penetration depth. Another is the control for the penetration depth with FC.A self adjusting fuzzy controller is proposed,which used for controlling the penetration depth.The control parameters are adjusted on line automatically according to the controlling errors of penetration and the errors can be decreased sharply. The effectiveness of the proposed intelligent methods is demonstrated by the real experiments and the improved performance results are obtained.展开更多
The problem of diving control for an underactuated unmanned undersea vehicle(UUV) considering the presence of parameters perturbations and wave disturbances was addressesed.The vertical motion of an UUV was divided in...The problem of diving control for an underactuated unmanned undersea vehicle(UUV) considering the presence of parameters perturbations and wave disturbances was addressesed.The vertical motion of an UUV was divided into two noninteracting subsystems for surge velocity control and diving.To stabilize the vertical motion system,the surge velocity and the depth control controllers were proposed using backstepping technology and an integral-fast terminal sliding mode control(IFTSMC).It is proven that the proposed control scheme can guarantee that all the error signals in the whole closed-loop system globally converge to the sliding surface in finite time and asymptotically converge to the origin along the sliding surface.With a unified control parameters for different motion states,a series of numerical simulation results illustrate the effectiveness of the above designed control scheme,which also shows strong robustness against parameters perturbations and wave disturbances.展开更多
This paper bursts the bondage of conventional no-burn thought, presents an optimum strategy permitting burn appear in grinding roughing stage, but the burning layer can be summed on the following finishing stage. On t...This paper bursts the bondage of conventional no-burn thought, presents an optimum strategy permitting burn appear in grinding roughing stage, but the burning layer can be summed on the following finishing stage. On the base of the basic grinding models, the objective function and constrained functions for the multiparameter optimum grinding models had been built in this paper. By the computer simulation, the nonlinear optimum grinding control parameters had been obtained, and the truth grinding process had been controlled by these parameters. The results of simulation and the experiments proved the exactitude of the optimum models and the feasibility of the optimum strategy. This paper had also created the precondition for the grinding automation, virtual grinding and intelligent grinding system for cylindrical grinding process.展开更多
An artificial neural network(ANN) and a self-adjusting fuzzy logiccontroller(FLC) for modeling and control of gas tungsten arc welding(GTAW) process are presented.The discussion is mainly focused on the modeling and c...An artificial neural network(ANN) and a self-adjusting fuzzy logiccontroller(FLC) for modeling and control of gas tungsten arc welding(GTAW) process are presented.The discussion is mainly focused on the modeling and control of the weld pool depth with ANN and theintelligent control for weld seam tracking with FLC. The proposed neural network can produce highlycomplex nonlinear multi-variable model of the GTAW process that offers the accurate prediction ofwelding penetration depth. A self-adjusting fuzzy controller used for seam tracking adjusts thecontrol parameters on-line automatically according to the tracking errors so that the torch positioncan be controlled accurately.展开更多
A hybrid remotely operated underwater vehicle( HROV) capable of working to the full ocean depth has been developed. In order for the vehicle to achieve a certain survivability level,a self-repairing control system( SR...A hybrid remotely operated underwater vehicle( HROV) capable of working to the full ocean depth has been developed. In order for the vehicle to achieve a certain survivability level,a self-repairing control system( SRCS) has been designed. It consists of two basic technologies,fault diagnosis and isolation( FDI) and reconfigurable control. For FDI,a model-based hierarchical fault diagnosis system is designed for the HROV. Then,control strategies which reconfigure the control system at intervals according to information from the FDI system are presented. Combining the two technologies,it can obtain the fundamental frame of SRCS for the HROV. Considering the hazardous underwater environment at the limiting depth and the hybrid operating modes,an assessment of the HROV's survivability is vitally needed before it enters operational service. This paper presents a new definition of survivability for underwater vehicles and develops a simple survivability model for the SRCS. As a result of survivability assessment for the SRCS,we are able to figure out the survivability of SRCS and make further optimization about it. The methodology developed herein is also applicable to other types of underwater vehicles.展开更多
A integrated intelligent system for seam tracking and penetration control is given. The system received information of welding seam error and penetration depth from only one sensor, then, it realized seam tracking and...A integrated intelligent system for seam tracking and penetration control is given. The system received information of welding seam error and penetration depth from only one sensor, then, it realized seam tracking and penetration control simultaneously. This paper introduces constitution of the system, methods of information recognition, design of the neural fuzzy controller and results practically.展开更多
This study aimed to establish a new propofol target-controlled infusion（TCI） model in animals so as to study the general anesthetic mechanism at multi-levels in vivo. Twenty Japanese white rabbits were enrolled and ...This study aimed to establish a new propofol target-controlled infusion（TCI） model in animals so as to study the general anesthetic mechanism at multi-levels in vivo. Twenty Japanese white rabbits were enrolled and propofol（10 mg/kg） was administrated intravenously. Artery blood samples were collected at various time points after injection, and plasma concentrations of propofol were measured. Pharmacokinetic modeling was performed using Win Nonlin software. Propofol TCI within the acquired parameters integrated was conducted to achieve different anesthetic depths in rabbits, monitored by narcotrend. The pharmacodynamics was analyzed using a sigmoidal inhibitory maximal effect model for narcotrend index（NI） versus effect-site concentration. The results showed the pharmacokinetics of propofol in Japanese white rabbits was best described by a two-compartment model. The target plasma concentrations of propofol required at light anesthetic depth was 9.77±0.23 μg/m L, while 12.52±0.69 μg/m L at deep anesthetic depth. NI was 76.17±4.25 at light anesthetic depth, while 27.41±5.77 at deep anesthetic depth. The effect-site elimination rate constant（ke0） was 0.263/min, and the propofol dose required to achieve a 50% decrease in the NI value from baseline was 11.19 μg/m L（95% CI, 10.25–13.67）. Our results established a new propofol TCI animal model and proved the model controlled the anesthetic depth accurately and stably in rabbits. The study provides a powerful method for exploring general anesthetic mechanisms at different anesthetic depths in vivo.展开更多
This paper investigates impact of noise and signal averaging on patient control in anesthesia applications, especially in networked control system settings such as wireless connected systems, sensor networks, local ar...This paper investigates impact of noise and signal averaging on patient control in anesthesia applications, especially in networked control system settings such as wireless connected systems, sensor networks, local area networks, or tele-medicine over a wide area network. Such systems involve communication channels which introduce noises due to quantization, channel noises, and have limited communication bandwidth resources. Usually signal averaging can be used effectively in reducing noise effects when remote monitoring and diagnosis are involved. However, when feedback is intended, we show that signal averaging will lose its utility substantially. To explain this phenomenon, we analyze stability margins under signal averaging and derive some optimal strategies for selecting window sizes. A typical case of anesthe-sia depth control problems is used in this development.展开更多
文摘A T-S fuzzy model with two rules is established to exactly describe the nonlinear uncertain heave dynamics of underwater vehicles with bounded heave speed.A single linear-matrix-inequality-based (LMI-based) state feedback controller is then synthesized to guarantee the global stability of the depth control system.Simulation results verify the effectiveness of the proposed approach in comparison with linear-quadratic regulator (LQR) method.Nonlinear disturbance observer is appended to the system when the underwater vehicles are affected by the gravity-buoyancy imbalance.The two-stage control method is effective to stabilize an uncertain system with both parameter uncertainties and external disturbances.
文摘This paper presents an environmental-friendly robotic system mimicking the undulating fins of a fish.To mimic the actual flexible fin of real fish,a fin-like mechanism with a series of connecting linkages is modeled and attached to the robotic fish,by virtue of a specially designed strip.Each link is able to turn and slide with respect to the adjacent link.These driving linkages are then used to form a mechanical fin consisting of several fin segments,which are able to produce undulations,similar to those produced by the actual fish fins.Owing to the modular and re-configurable design of the mechanical fin,we are able to construct biomimetic robotic fish with various swimming modes by fin undulations.Some qualitative and workspace observations by experiments of the robotic fish are shown and discussed.
基金Wenhai Program of Qingdao National Laboratory for Marine Science and Technology(No.ZR2016WH01)Tianjin Marine Economic Innovation and Development of Regional Demonstration Projects of State Oceanic Administration(No.BHSF2017-27)。
文摘The net buoyancy of the deep-sea self-holding intelligent buoy(DSIB)will change with depth due to pressure hull deformation in the deep submergence process.The net buoyancy changes will affect the hovering performance of the DSIB.To make the DSIB have better resistance to the external disturbances caused by the net buoyancy and water resistance,a depth controller was designed to improve the depth positioning based on the active disturbance rejection control(ADRC).Firstly,a dynamic model was established based on the motion analysis of the DSIB.In addition,the extended state observer(ESO)and nonlinear state error feedback controller were designed based on the Lyapunov stability principle.Finally,semi-physical simulations for the depth control process were made by using the ADRC depth controller and traditional PID depth controller,respectively.The results of the semi-physical simulations indicate that the depth controller based on the ADRC can achieve the predefined depth control under the external disturbances.Compared with the traditional PID depth controller,the overshoot of the ADRC depth controller is 1.74%,and the depth error is within 0.5%.It not only has a better control capability to restrain the overshoot and shock caused by the external disturbances,but also can improve intelligence of the DSIB under the depth tracking task.
基金Qingdao Entrepreneurship and Innovation Leading Researchers Program(No.19-3-2-40-zhc)Key Research and Development Program of Shandong Province(Nos.2019GHY112072,2019GHY112051)Project Supported by State Key Laboratory of Precision Measuring Technology and Instruments(No.pilab1906).
文摘Aiming at the contradiction between the depth control accuracy and the energy consumption of the self-sustaining intelligent buoy,a low energy consumption depth control method based on historical array for real-time geostrophic oceanography(Argo)data is proposed.As known from the buoy kinematic model,the volume of the external oil sac only depends on the density and temperature of seawater at hovering depth.Hence,we use historical Argo data to extract the fitting curves of density and temperature,and obtain the relationship between the hovering depth and the volume of the external oil sac.Genetic algorithm is used to carry out the optimal energy consumption motion planning for the depth control process,and the specific motion strategy of depth control process is obtained.Compared with dual closed-loop fuzzy PID control method and radial basis function(RBF)-PID method,the proposed method reduces energy consumption to 1/50 with the same accuracy.Finally,a hardware-in-the-loop simulation system was used to verify this method.When the error caused by fitting curves is not considered,the average error is 2.62 m,the energy consumption is 3.214×10^(4)J,and the error of energy consumption is only 0.65%.It shows the effectiveness and reliability of the method as well as the advantages of comprehensively considering the accuracy and energy consumption.
文摘Aimed at uncertainties and model's impreciseness, nonlinearity and time-variability of depth control system in autonomous underwater vehicle (AUV), a depth predictive control method was put forward based on rough set (RS) and least squares support vector machine (LSSVM). By using RS theory, the monitor data attribute of AUV was reduced to eliminate the redundant information and to improve efficiency. Then, LSSVM model was trained by using the reduced rules, and its parameters were optimized by using chaos theory for the higher accurate control. Taken an AUV typed NPS Phoenix as an example, its depth step response, horizontal rudder and pitch change were simulated. The simulation results show that the method improves the model's accuracy and has better real-time response, fault-tolerant ability, reliability and strong anti-interfere capability.
基金supported by the National 863 High Technology Development Plan Project (Grant No. 2008AA092301)National Natural Science Foundation of China (Grant Nos. 50909025 and 51179035)the Fundamental Research Funds for the Central Universities (HEUCFZ1003)
文摘Underwater vehicle plays an important role in ocean engineering. Depth control by fin is one of the difficulties for underwater vehicle in motion control. Depth control is indirect due to the freedom coupling between trim and axial motion. It includes the method of dynamic analysis and lift-resistance-coefficient experiment and theory algorithm. By considering the current speed and depth deviation, comprehensive interpretation is used in object-planning instruction. Expected depth is transformed into expected trim. Dynamic output fluctuation can be avoided, which is caused by linear mapping of deviation. It is steady and accurate for the motion of controlled underwater vehicles. The feasibility and efficiency of the control method are testified in the pool and natural area for experiments.
文摘A new method for depth control was developed for a spilled oil and blow out gas tracking autonomous buoy robot called SOTAB-I by adjusting its buoyancy control device. It is aimed to work for any target depth. The new method relies on buoyancy variation model with a depth that was established based on experimental data. The depth controller was verified at sea experiments in the Toyama Bay in Japan and showed good performance. The method could further be adapted to altitude control by combining the altitude data measured from bottom tracking through a progressive depth control. The method was verified at the sea experiments in Toyama in March 2016 and showed that the algorithm succeeded to bring the robot to the target altitude.
文摘An intelligent system including both a neural network(NN) and a self adjusting fuzzy controller(FC) for modeling and control of the penetration depth during gas tungsten arc welding(GTAW) process is presented in this paper. The discussion is mainly focused on two parts. One is the modeling of the penetration depth with NN. A visual sensor CCD is used to obtain the image of the molten pool. A neural network model is established to estimate the penetration depth from the welding current, pool width and seam gap. It is demonstrated that the proposed neural network can produce highly complex nonlinear multi variable model of the GTAW process that offer the accurate prediction of welding penetration depth. Another is the control for the penetration depth with FC.A self adjusting fuzzy controller is proposed,which used for controlling the penetration depth.The control parameters are adjusted on line automatically according to the controlling errors of penetration and the errors can be decreased sharply. The effectiveness of the proposed intelligent methods is demonstrated by the real experiments and the improved performance results are obtained.
基金Projects (51179038,51309067) supported by the National Natural Science Foundation of China
文摘The problem of diving control for an underactuated unmanned undersea vehicle(UUV) considering the presence of parameters perturbations and wave disturbances was addressesed.The vertical motion of an UUV was divided into two noninteracting subsystems for surge velocity control and diving.To stabilize the vertical motion system,the surge velocity and the depth control controllers were proposed using backstepping technology and an integral-fast terminal sliding mode control(IFTSMC).It is proven that the proposed control scheme can guarantee that all the error signals in the whole closed-loop system globally converge to the sliding surface in finite time and asymptotically converge to the origin along the sliding surface.With a unified control parameters for different motion states,a series of numerical simulation results illustrate the effectiveness of the above designed control scheme,which also shows strong robustness against parameters perturbations and wave disturbances.
文摘This paper bursts the bondage of conventional no-burn thought, presents an optimum strategy permitting burn appear in grinding roughing stage, but the burning layer can be summed on the following finishing stage. On the base of the basic grinding models, the objective function and constrained functions for the multiparameter optimum grinding models had been built in this paper. By the computer simulation, the nonlinear optimum grinding control parameters had been obtained, and the truth grinding process had been controlled by these parameters. The results of simulation and the experiments proved the exactitude of the optimum models and the feasibility of the optimum strategy. This paper had also created the precondition for the grinding automation, virtual grinding and intelligent grinding system for cylindrical grinding process.
基金National Natural Science Foundation of China and Provincial Natural Science Foundafion of Guangdong, China.
文摘An artificial neural network(ANN) and a self-adjusting fuzzy logiccontroller(FLC) for modeling and control of gas tungsten arc welding(GTAW) process are presented.The discussion is mainly focused on the modeling and control of the weld pool depth with ANN and theintelligent control for weld seam tracking with FLC. The proposed neural network can produce highlycomplex nonlinear multi-variable model of the GTAW process that offers the accurate prediction ofwelding penetration depth. A self-adjusting fuzzy controller used for seam tracking adjusts thecontrol parameters on-line automatically according to the tracking errors so that the torch positioncan be controlled accurately.
基金Sponsored by the National Natural Science Foundation of China(Grant No.51109132)Specialized Research Fund for the Doctoral Program of Higher Education of China(Grant No.20110073120015)
文摘A hybrid remotely operated underwater vehicle( HROV) capable of working to the full ocean depth has been developed. In order for the vehicle to achieve a certain survivability level,a self-repairing control system( SRCS) has been designed. It consists of two basic technologies,fault diagnosis and isolation( FDI) and reconfigurable control. For FDI,a model-based hierarchical fault diagnosis system is designed for the HROV. Then,control strategies which reconfigure the control system at intervals according to information from the FDI system are presented. Combining the two technologies,it can obtain the fundamental frame of SRCS for the HROV. Considering the hazardous underwater environment at the limiting depth and the hybrid operating modes,an assessment of the HROV's survivability is vitally needed before it enters operational service. This paper presents a new definition of survivability for underwater vehicles and develops a simple survivability model for the SRCS. As a result of survivability assessment for the SRCS,we are able to figure out the survivability of SRCS and make further optimization about it. The methodology developed herein is also applicable to other types of underwater vehicles.
文摘A integrated intelligent system for seam tracking and penetration control is given. The system received information of welding seam error and penetration depth from only one sensor, then, it realized seam tracking and penetration control simultaneously. This paper introduces constitution of the system, methods of information recognition, design of the neural fuzzy controller and results practically.
基金supported by a grant from Shenzhen Baoan Hospital Affiliated to Southern Medical University
文摘This study aimed to establish a new propofol target-controlled infusion（TCI） model in animals so as to study the general anesthetic mechanism at multi-levels in vivo. Twenty Japanese white rabbits were enrolled and propofol（10 mg/kg） was administrated intravenously. Artery blood samples were collected at various time points after injection, and plasma concentrations of propofol were measured. Pharmacokinetic modeling was performed using Win Nonlin software. Propofol TCI within the acquired parameters integrated was conducted to achieve different anesthetic depths in rabbits, monitored by narcotrend. The pharmacodynamics was analyzed using a sigmoidal inhibitory maximal effect model for narcotrend index（NI） versus effect-site concentration. The results showed the pharmacokinetics of propofol in Japanese white rabbits was best described by a two-compartment model. The target plasma concentrations of propofol required at light anesthetic depth was 9.77±0.23 μg/m L, while 12.52±0.69 μg/m L at deep anesthetic depth. NI was 76.17±4.25 at light anesthetic depth, while 27.41±5.77 at deep anesthetic depth. The effect-site elimination rate constant（ke0） was 0.263/min, and the propofol dose required to achieve a 50% decrease in the NI value from baseline was 11.19 μg/m L（95% CI, 10.25–13.67）. Our results established a new propofol TCI animal model and proved the model controlled the anesthetic depth accurately and stably in rabbits. The study provides a powerful method for exploring general anesthetic mechanisms at different anesthetic depths in vivo.
文摘This paper investigates impact of noise and signal averaging on patient control in anesthesia applications, especially in networked control system settings such as wireless connected systems, sensor networks, local area networks, or tele-medicine over a wide area network. Such systems involve communication channels which introduce noises due to quantization, channel noises, and have limited communication bandwidth resources. Usually signal averaging can be used effectively in reducing noise effects when remote monitoring and diagnosis are involved. However, when feedback is intended, we show that signal averaging will lose its utility substantially. To explain this phenomenon, we analyze stability margins under signal averaging and derive some optimal strategies for selecting window sizes. A typical case of anesthe-sia depth control problems is used in this development.
