Locomotion and Depth Control of Robotic Fish with Modular Undulating Fins

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.

Low energy consumption depth control method of self-sustaining intelligent buoy

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.

Depth control for a deep-sea self-holding intelligent buoy system based on active disturbance rejection control method

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.

Depth Control for AUV Based on RS-LSSVM

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.

T-S Fuzzy Model-Based Depth Control of Underwater Vehicles

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.

Depth-Trim Mapping Control of Underwater Vehicle with Fins

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.

Depth and Altitude Control of an AUV Using Buoyancy Control Device

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.

Fuzzy neural networks for control of penetration depthduring GTAW

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.

零信任的安全模型研究

零信任被认为是一种新的安全范式,从安全模型视角,揭示了零信任架构以“身份和数据”为主线的安全模型深化与整合.零信任以身份为核心建立全景管控实体链条,围绕实体属性功能生命周期等建立深度防御,并集中重定向实体间信息的流动,整合信息通道,实现层层防护和细粒度动态化访问控制,最后从攻击者视角在信息流通道关键节点设置主动防御机制.由于零信任系统一定会成为高价值资产,探讨了零信任系统演进中与业务深度融合、零信任自身安全和弹性服务能力的新趋势.通过对零信任蕴含安全模型和自身安全性的分析,期望能够为零信任在应用中的架构设计、技术演进、应用安全提供更加清晰的技术发展路径.

免耕播种机播深智能控制系统设计

免耕播种机作为免耕播种技术的核心装备,播种深度的精准控制直接影响作物的生长发育。为了进一步提高免耕播种机智能控制系统的稳定性,构建免耕播种机播深自动控制系统,系统阐述其工作原理与流程,并对关键控制模块及设备进行设计与选型。系统测试试验表明,该系统通过充分利用先进的传感器和算法技术,系统响应时间在0.12~0.18 s之间,能够根据不同作物和土壤条件,自动调整播种深度,并取得了良好的播种效果和产量表现。

基于双耳麦克风实现机器人骨铣削状态自动控制

为了提升骨铣削手术机器人的手术质量,要求机器人对于手术中的铣削状态(主要包括铣削深度和铣削角度)能够实时感知并控制。针对此问题,本文提出一种利用双耳麦克风系统来辅助完成铣削状态控制的方法。首先对铣削状态与声信号的关系进行建模,之后完成铣削深度与铣削角度的标定实验获得所提模型的相关系数,最后利用PD控制器结合所建立的模型函数来实时分析并控制机器人的铣削状态。实验结果表明,铣削期望深度为0.5 mm时,加入角度控制相较于未加角度控制的实验效果在铣削深度与期望值偏差的方面提升了7.0%,铣削稳定性方面提升了34.1%。并且证明在期望深度为0.8 mm的极端情况下引入角度控制同样有着提升铣削效果的积极作用。经过实验验证所提方法能够有效的提升骨铣削手术机器人的工作质量。

甘蔗收获机切割器入土切割深度监控系统设计与试验

入土切割是降低甘蔗收获机破头率的重要方法。为了实现甘蔗收获机切割器随地面起伏自动调控入土深度,保持切割器入土深度的一致性,基于4GQ-1型甘蔗收获机,设计一种甘蔗收获机切割器入土切割深度监测与自动控制系统,实现切割器入土深度自动调控及其参数可视化,并通过室内试验和田间试验检验系统的监控性能。室内试验结果表明,监控系统的入土深度监测精度平均值为98.90%,自动控制精度平均值为97.7%,监测精度和自动控制精度达到95.0%以上。田间试验结果表明,在20、30和50mm入土深度的条件下,监控系统的自动控制精度的平均值分别是85.3%、90.6%、94.1%,自动控制精度达到85.0%以上,监控系统总体设计合理,性能良好。研究结果可为甘蔗收获机切割器入土切割深度控制系统研究提供理论参考,对甘蔗收获机优化升级具有重要的参考意义。

基于AMESim-Matlab联合仿真的拖拉机耕深控制研究

在拖拉机电控液压耕深控制系统的研究中提出力位综合-滑转率的联合控制方法及控制策略,加入滑转率监测控制,在最优滑转率区间允许范围内调节耕深。搭建电控液压悬挂的液压物理模型,并建立仿真模型,设计滑转率模糊控制器、力位综合模糊PID控制器,试验研究基于滑转率逻辑门限的多参数控制效果。试验结果表明在超出滑转率逻辑门限值时系统有较好的响应效果,对滑转率的变化有较优的跟随效果,为拖拉机耕深控制系统多参数控制提供理论基础。

基于上下文感知的强化学习AUV控制器研究

为了提升基于强化学习的自主水下航行器(Autonomous Underwater vehicle,AUV)控制器在复杂海况中对环境干扰的鲁棒性,设计一种利用上下文信息进行环境感知的强化学习控制器。结合水下机器人运动学及动力学方程对深度跟踪任务进行建模,构建了基于PPO-clip算法的深度控制器,并在算法中融入了上下文变量和域随机化方法。在仿真环境中分别进行海流干扰、暗涌干扰以及两者共同干扰环境的深度跟踪任务,仿真结果表明,本文提出的方法对强化学习控制器的抗干扰能力有明显的提升,在多种环境干扰下更精准地完成深度跟踪任务。

多波束测深声呐自主数据质量监测及门限控制

为满足水面无人艇等海洋无人平台的需求,确保多波束测深声呐系统在无人监控和参数调节下具备可靠自主工作能力,本文提出一种数据质量在线监测及门限参数控制方法。通过建立基于质量和异常因子的监测模型,结合粒子滤波技术,提高海底地形跟踪门限控制抗差性。仿真和湖上试验显示该方法能判定数据有效性、适应地形变化,并提供合理深度门限,适用于多波束测深声呐系统的在线数据质量监测和无人自主控制。

水基四元超分子微球功能评价及深部调剖适应性

为解决海上高含水油藏聚合物微球耐温、耐盐和抗剪切性能差导致应用效果变差的问题,采用分散聚合方法制备了一种水基四元超分子微球,并进行了性能表征及深部调驱能力评价。结果表明,该超分子微球在温度90℃、矿化度7×10^(4)mg/L、剪切速率3 000 s^(-1)条件下具有良好的稳定性,表现出良好的耐温、耐盐和抗剪切性能;在100~3 000 mD的岩心中具有良好的注入性与封堵性;对于高含水非均质岩心可实现采收率增幅达16.68%~20.56%,具有良好的应用潜力。

纵深防御理念下的核电机组调频能力深化研究

通过调研总结了国内、国外核电机组在调频方面的设计和运行经验,以国和一号调频能力深化研究为例,提出了开展核电机组调频能力优化评估的基本思路:首先是确定调频能力的优化目标;然后基于优化目标开展可行性评估,包括全寿期内反应堆冷却剂压力边界的完整性,控制棒和控制棒驱动机构的使用情况,以及运行中可能的技术风险;最后基于核安全纵深防御的理念提出了调频运行相关的安全改进措施,包括调频运行的投运和退出限制条件。深化研究表明,以包络EUR、URD和我国火电机组下限调频能力要求为优化目标,并考虑必要的纵深防御安全措施,国和一号具备进一步开展调频能力优化的技术可行性。

基于无模型自抗扰的AUV深度控制研究

针对自主式水下航行器(AUV)在海洋中易受海流海浪扰动的问题,本文提出一种基于自抗扰的无模型自适应深度控制方法(ADR-MFAC)。首先,通过引入无模型自适应控制律,解决AUV难以建立精确的数学模型的问题;其次,设计出AUV深度控制器,结合自抗扰控制算法来进一步提高控制器干扰性能,采用跟踪微分器对输入信号进行跟踪,并利用线性状态观测器来估计系统所受扰动。最后,通过仿真和海试验证了ADRMFAC的控制性能:与PID控制相比,ADR-MFAC在保持较高控制精度的同时,超调量减小了51%,深度调节时间减小了21.4%,证明了ADR-MFAC在受到海流干扰时也可以实现稳定的深度控制,算法具有较强的鲁棒性。

耐高温水基微球调驱剂性能评价

评价了一种耐高温水基微球调驱剂性能。实验结果表明:不同浓度耐温水基微球调驱剂注入不同渗透率岩心的阻力系数均小于等于5,注入性好;浓度为4 000 mg/L的耐温水基微球调驱剂在岩心中的有效封堵渗透率范围最大,封堵率最高超过90%;该水基微球调驱剂在150℃下有效封堵时间45 d左右,耐温性良好;该耐温水基微球调驱剂可提高渤海油田非均质储层原油采收率15.41%~17.92%。