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
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 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
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
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.
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 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.
