深海采矿移动机器人的鲁棒控制研究
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摘要
深海采矿作业技术的研究是一项复杂的高技术,是继航空航天开发之后世界各国面临的一个新的重大课题,其开发手段的研究,对我国矿产资源的可持续利用具有重要的战略意义。
深海底采矿机器人的工作环境为6000m深海底,是“极稀软”沉积物底质、无自然光、高压、强扰动、未知复杂的环境。同时,采矿机器人设计的特殊性决定了其很强的非线性。深海采矿系统要求采矿机器人能在未知的复杂海底环境中,以一定的精度按照预定的轨迹行驶、高效地采矿。作为深海采矿的载体,采矿机器人控制效果的好坏直接关系到我国大洋战略开发的实施质量,机器人工作在剪切强度较弱且随机变化的深海“稀软底”,与同类陆地履带车辆相比,更易产生打滑,严重时将深陷入沉积物中,丧失作业行走能力。而机器人设计的特殊性和作业环境的特殊性决定了相关控制问题的复杂性。因此,深海采矿移动机器人的控制相关问题是深海采矿技术诸多挑战中的重要研究问题之一,保障深海采矿机器人行驶的安全性成为一个亟待解决的问题,具有很重大的现实意义和科学意义。
本文重点研究了深海采矿机器人的鲁棒控制问题,旨在为深海采矿技术提供一些前期的理论研究和技术支持。
论文主要研究工作及研究成果如下:
(1)在深入分析深海采矿机器人机构的基础上,针对采矿机器人属于履带车辆,通过对采矿机器人行走特点分析,采用差动转向方式,建立了机器人运动学模型。基于机器人的左右履带驱动轮采用液压驱动的特点,分别针对液压驱动系统中的电液比例阀、柱塞式变量泵、液压马达建立了数学模型,实现了对深海采矿机器人运动学系统的有效描述。
分析了采矿系统运动状态和动态特性的一些影响因素。从动力学的角度对深海采矿系统进行了分析,建立了软管、硬管的动力学方程,分析了加在软管上的流体力和不同工况下软管端受力情况,建立了航向控制的数学模型,为深海采矿机器人的鲁棒控制奠定理论基础。
(2)根据深海采矿机器人定位系统的功能要求,给出了深海采矿机器人的定位系统框架。针对深海底采矿机器人的定位这一复杂问题,提出了一种基于伪长基线和航位推算的组合定位方法,给出了完整的卡尔曼滤波器结构,为提高位置估计精度,滤波器采用了自适应卡尔曼滤波算法,在线修正滤波器的增益,仿真结果说明了该方法的可行性,并且相对传统方法的估计精度有较大的提高,为深海采矿机器人的定位提供了一种新的途径。
(3)在分析了履带防滑控制原理的基础上,揭示了滑转率与附着条件的关系,基于附着系数和滑转率曲线形状的辨识方法,通过马达转矩和履带角加速度得到最佳滑转率值,建立了以滑转率控制为目标的防滑控制系统。
(4)针对深海采矿机器人作业时打滑严重,运动状态不确定、变化大,关键运动参数难以直接测量的问题,在力学分析和液压驱动原理分析的基础上,研究了基于动力学的采矿机器人路径控制方法。建立了采矿机器人驱动轮有效半径、左右履带打滑率等关键运动参数的在线估计模型,完成了对滑转率和有效驱动半径的有效估计。在关键参数的有效估计基础上,设计了基于位置误差和偏航角误差的控制器,仿真结果说明了方法的可行性。
(5)针对深海采矿机器人控制模型、结构参数和外部环境等诸多不确定性,结合鲁棒控制和神经网络,研究了深海采矿机器人的自适应鲁棒路径控制策略。用RBF神经网络逼近不确定性的上界来解决系统不确定结构未知的问题,在状态反馈基础上,构建基于虚拟输入的航向控制鲁棒自适应律,给出了在左右履带速度输入与虚拟输入之间的模糊控制推理规则,构建了采矿机器人的行走控制策略。仿真结果说明了方法的可行性。
(6)在开发的深海采矿机器人的模型车仿真系统上,对模型车进行了大量跑车试验,模型车左右履带稳定速度和过渡过程速度曲线、模型车航向角变化和航向角偏差曲线以及模型车轨迹跟踪变化曲线等试验结果验证了采矿机器人控制系统的可行性和有效性。
Exploration technique on deep seabed is a kind of complicated high technology and a new important project which follows aerospace exploration faced in the world. It has important strategic significance for our country's mineral resource continual utilization and development.
Deep seabed mining robot works in 6000m-depth seabed unknown complex environment with very dilute and soft bottom, no natural light, high pressure, and strong disturbance. Moreover, the special design of mining robot leads to its strong nonlinearity. Deep seabed mining system requires mining robot works efficiently following desired path with certain accuracy in unknown complex environment. As the carrier of deep seabed mining system, the control quality of mining robot has direct influence on our countries ocean exploitation strategy. The mining robot is operating in the deep-sea sediment, which shows itself weak shear intensity and varies randomly. Compared with the similar type of tracked-vehicle on land, it is easy to slip seriously, even easy to sink into the sediment and lose its ability to run. However, the robot's special design and special working environment cause the complexity of control problem. Therefore, the research on control of mining robot for deep seabed mining is an important problem among many challenges of deep seabed mining technique. It is high time to research how to control the slip to safeguard the running of the robot. The research has great practical significance and great scientific significance.
The dissertation mainly studies the robust control problem of mining robot for deep seabed mining aims to providing some previous theoretical research and technical support for deep seabed mining.
The major innovation research achievements include:
(1) With the analyzing mechanisms of deep seabed mining robot deeply, considering mining robot belongs to a kind of tracked vehicle, analyzing robot's walking characteristics, using differential steering, the mining robot dynamics model is established. Mathematical model of electro-hydraulic proportional valve, plunger-type variable displacement pump, hydraulic motor are built. The motion system of deep seabed mining robot is described effectively.
Influential factors of mining system motion states and dynamic characteristics are analyzed. Dynamic equations of soft pipe and rigid pipe are established, fluid force analysis on soft pipe is performed, force on soft pipe end is given, and the mining robot yaw control mathematical model is established.
(2) On the basis of deep seabed mining robot localization system functional requirements, the localization system frame is proposed. Aiming at complex localization problem, an integrated location method based on pseudo long baseline and dead reckoning is proposed, and the Kalman filter structure is established. To improve precision, adaptive Kalman filtering algorithm is applied for data fusion and amending filter gain. The feasibility of this method is tested by simulation research results, estimation accuracy has a great enhancement comparing with existing method. A new location approach is put forward.
(3) On the Analysis of the principle of anti-skid control of the track reveals the relationship of the slip ratio and adhesion coefficient, the optimal slip ratio of deep-sea sediment is gained according to the ratio and the adhesion coefficient curve, an anti-slip controller is developed.
(4) Deep seabed mining robot works with high slip and great uncertain moving state change, the key moving parameters are difficult to measure. Path tracking control approach is studied based on mechanical analysis and hydraulic driving principle analysis. The parameters including tracks slip rate and effective radiuses of driving wheels identification model is built, it builds basis for optimal unbiased estimation of deep seabed mining robot key moving parameters. Controller is designed based on efficient key moving parameters estimation, position error and yaw angle, simulation experiment results show the effectiveness of the method.
(5) Considering the uncertainty of mining robot control model, structure parameter and external environment, combining robust control with neural network, path tracking adaptive robust control approach is concerned. RBF neural network is used to learn the unknown bounds of system uncertainties adaptively, adaptive heading robust control algorithm is presented based on status feedback and virtual input. And control method on the crawling of deep seabed mining moving robot is described after discussion on fuzzy rule between virtual input and tracks velocity. The feasibility of this method is tested by simulation research results.
(6) Experiments have been made on seabed mining robot vehicle model simulation system. Velocity changing curves, yaw angle changing curves, yaw angle error curves and path tracking curves test the feasibility and validity of mining robot control system.
深海底采矿机器人的工作环境为6000m深海底,是“极稀软”沉积物底质、无自然光、高压、强扰动、未知复杂的环境。同时,采矿机器人设计的特殊性决定了其很强的非线性。深海采矿系统要求采矿机器人能在未知的复杂海底环境中,以一定的精度按照预定的轨迹行驶、高效地采矿。作为深海采矿的载体,采矿机器人控制效果的好坏直接关系到我国大洋战略开发的实施质量,机器人工作在剪切强度较弱且随机变化的深海“稀软底”,与同类陆地履带车辆相比,更易产生打滑,严重时将深陷入沉积物中,丧失作业行走能力。而机器人设计的特殊性和作业环境的特殊性决定了相关控制问题的复杂性。因此,深海采矿移动机器人的控制相关问题是深海采矿技术诸多挑战中的重要研究问题之一,保障深海采矿机器人行驶的安全性成为一个亟待解决的问题,具有很重大的现实意义和科学意义。
本文重点研究了深海采矿机器人的鲁棒控制问题,旨在为深海采矿技术提供一些前期的理论研究和技术支持。
论文主要研究工作及研究成果如下:
(1)在深入分析深海采矿机器人机构的基础上,针对采矿机器人属于履带车辆,通过对采矿机器人行走特点分析,采用差动转向方式,建立了机器人运动学模型。基于机器人的左右履带驱动轮采用液压驱动的特点,分别针对液压驱动系统中的电液比例阀、柱塞式变量泵、液压马达建立了数学模型,实现了对深海采矿机器人运动学系统的有效描述。
分析了采矿系统运动状态和动态特性的一些影响因素。从动力学的角度对深海采矿系统进行了分析,建立了软管、硬管的动力学方程,分析了加在软管上的流体力和不同工况下软管端受力情况,建立了航向控制的数学模型,为深海采矿机器人的鲁棒控制奠定理论基础。
(2)根据深海采矿机器人定位系统的功能要求,给出了深海采矿机器人的定位系统框架。针对深海底采矿机器人的定位这一复杂问题,提出了一种基于伪长基线和航位推算的组合定位方法,给出了完整的卡尔曼滤波器结构,为提高位置估计精度,滤波器采用了自适应卡尔曼滤波算法,在线修正滤波器的增益,仿真结果说明了该方法的可行性,并且相对传统方法的估计精度有较大的提高,为深海采矿机器人的定位提供了一种新的途径。
(3)在分析了履带防滑控制原理的基础上,揭示了滑转率与附着条件的关系,基于附着系数和滑转率曲线形状的辨识方法,通过马达转矩和履带角加速度得到最佳滑转率值,建立了以滑转率控制为目标的防滑控制系统。
(4)针对深海采矿机器人作业时打滑严重,运动状态不确定、变化大,关键运动参数难以直接测量的问题,在力学分析和液压驱动原理分析的基础上,研究了基于动力学的采矿机器人路径控制方法。建立了采矿机器人驱动轮有效半径、左右履带打滑率等关键运动参数的在线估计模型,完成了对滑转率和有效驱动半径的有效估计。在关键参数的有效估计基础上,设计了基于位置误差和偏航角误差的控制器,仿真结果说明了方法的可行性。
(5)针对深海采矿机器人控制模型、结构参数和外部环境等诸多不确定性,结合鲁棒控制和神经网络,研究了深海采矿机器人的自适应鲁棒路径控制策略。用RBF神经网络逼近不确定性的上界来解决系统不确定结构未知的问题,在状态反馈基础上,构建基于虚拟输入的航向控制鲁棒自适应律,给出了在左右履带速度输入与虚拟输入之间的模糊控制推理规则,构建了采矿机器人的行走控制策略。仿真结果说明了方法的可行性。
(6)在开发的深海采矿机器人的模型车仿真系统上,对模型车进行了大量跑车试验,模型车左右履带稳定速度和过渡过程速度曲线、模型车航向角变化和航向角偏差曲线以及模型车轨迹跟踪变化曲线等试验结果验证了采矿机器人控制系统的可行性和有效性。
Exploration technique on deep seabed is a kind of complicated high technology and a new important project which follows aerospace exploration faced in the world. It has important strategic significance for our country's mineral resource continual utilization and development.
Deep seabed mining robot works in 6000m-depth seabed unknown complex environment with very dilute and soft bottom, no natural light, high pressure, and strong disturbance. Moreover, the special design of mining robot leads to its strong nonlinearity. Deep seabed mining system requires mining robot works efficiently following desired path with certain accuracy in unknown complex environment. As the carrier of deep seabed mining system, the control quality of mining robot has direct influence on our countries ocean exploitation strategy. The mining robot is operating in the deep-sea sediment, which shows itself weak shear intensity and varies randomly. Compared with the similar type of tracked-vehicle on land, it is easy to slip seriously, even easy to sink into the sediment and lose its ability to run. However, the robot's special design and special working environment cause the complexity of control problem. Therefore, the research on control of mining robot for deep seabed mining is an important problem among many challenges of deep seabed mining technique. It is high time to research how to control the slip to safeguard the running of the robot. The research has great practical significance and great scientific significance.
The dissertation mainly studies the robust control problem of mining robot for deep seabed mining aims to providing some previous theoretical research and technical support for deep seabed mining.
The major innovation research achievements include:
(1) With the analyzing mechanisms of deep seabed mining robot deeply, considering mining robot belongs to a kind of tracked vehicle, analyzing robot's walking characteristics, using differential steering, the mining robot dynamics model is established. Mathematical model of electro-hydraulic proportional valve, plunger-type variable displacement pump, hydraulic motor are built. The motion system of deep seabed mining robot is described effectively.
Influential factors of mining system motion states and dynamic characteristics are analyzed. Dynamic equations of soft pipe and rigid pipe are established, fluid force analysis on soft pipe is performed, force on soft pipe end is given, and the mining robot yaw control mathematical model is established.
(2) On the basis of deep seabed mining robot localization system functional requirements, the localization system frame is proposed. Aiming at complex localization problem, an integrated location method based on pseudo long baseline and dead reckoning is proposed, and the Kalman filter structure is established. To improve precision, adaptive Kalman filtering algorithm is applied for data fusion and amending filter gain. The feasibility of this method is tested by simulation research results, estimation accuracy has a great enhancement comparing with existing method. A new location approach is put forward.
(3) On the Analysis of the principle of anti-skid control of the track reveals the relationship of the slip ratio and adhesion coefficient, the optimal slip ratio of deep-sea sediment is gained according to the ratio and the adhesion coefficient curve, an anti-slip controller is developed.
(4) Deep seabed mining robot works with high slip and great uncertain moving state change, the key moving parameters are difficult to measure. Path tracking control approach is studied based on mechanical analysis and hydraulic driving principle analysis. The parameters including tracks slip rate and effective radiuses of driving wheels identification model is built, it builds basis for optimal unbiased estimation of deep seabed mining robot key moving parameters. Controller is designed based on efficient key moving parameters estimation, position error and yaw angle, simulation experiment results show the effectiveness of the method.
(5) Considering the uncertainty of mining robot control model, structure parameter and external environment, combining robust control with neural network, path tracking adaptive robust control approach is concerned. RBF neural network is used to learn the unknown bounds of system uncertainties adaptively, adaptive heading robust control algorithm is presented based on status feedback and virtual input. And control method on the crawling of deep seabed mining moving robot is described after discussion on fuzzy rule between virtual input and tracks velocity. The feasibility of this method is tested by simulation research results.
(6) Experiments have been made on seabed mining robot vehicle model simulation system. Velocity changing curves, yaw angle changing curves, yaw angle error curves and path tracking curves test the feasibility and validity of mining robot control system.
引文
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