网络环境下多自主水下航行器编队控制研究
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摘要
多自主水下航行器(Autonomous Underwater Vehicles,AUVs)的协同作业对于海洋科学考察和海洋开发等具有重要意义。通过对多AUVs的编队控制可以显著提高AUVs的包括海洋采样、成像、监视和通信在内众多应用方面的能力。根据需要编队的水下航行器的空间分布规模,将编队控制问题划分为两类尺度上的编队问题进行研究。分别是针对远距离、大规模编队的大尺度编队控制问题和需要考虑水下航行器驶向角的小尺度编队控制问题。论文的主要工作和研究成果如下:
1.研究多AUVs编队控制框架,提出一种融合主从(Leader-follower)和虚拟结构(Virtual Structure)思想的虚拟-主从递阶结构编队控制策略。考虑到水下远距离水声通信信号衰减和失真约束,将AUVs按在队列中的相对位置分为若干个簇。簇间各Leader的编队控制采用虚拟结构。簇内各Follower以该簇Leader的路径作为跟踪期望参考路径,以实现Follower跟踪Leader的编队控制。仿真验证了控制策略的有效性。
2.研究有限时间跟踪控制器设计问题。基于反馈线性化和变结构控制思想提出一种复合的有限时间线性化跟踪控制算法。首先AUV驶向角初始误差快速收敛到滑模边界层,然后采用连续平滑状态反馈控制律来实现驶向角误差的无抖振快速镇定,同时将原系统转化为低阶系统。其次针对低阶系统设计了位置误差的状态反馈控制律,实现了位置误差的有限时间镇定。数值仿真表明了AUV能够在有限时间内无抖振地完全跟踪上期望轨迹。
3.研究基于非线性滤波的水下航行器轨迹跟踪控制问题。在第二章研究基础上,针对有系统噪声和量测噪声的误差系统,分别利用扩展卡尔曼滤波(Extended Kalman Filter, EKF)和基于上三角-对角矩阵(Upper triangular and Diagonal matrix, UD)分解误差协方差矩阵的无迹滤波(Unscented Kalman Filter, UKF)进行误差状态估计,并以估计值构成反馈控制律。通过数值仿真验证了控制策略的有效性。
4.研究基于一阶动态智能体系统的一致性算法来实现大尺度多自主水下航行器的编队控制问题。针对各AUV拥有不同虚拟领航者信息(参考信息)的情况,提出在一个仿真步长时间内对参考信息和各AUV信息分别进行一致性协同的两层有限时间一致性算法。分别研究了基于虚拟Leader的多智能体编队控制、基于Leader-follower的单Leader编队控制、基于Leader-follower的递阶式多智能体编队控制和具有通信距离约束的智能体的编队控制,提出了一系列的多智能体有限时间一致性跟踪控制协议。
5.研究基于二阶一致性算法的多自主水下航行器大尺度分布式协同控制问题。针对大尺度协同控制导致的时延和数据掉包问题,基于数据采样和保持原理,将时延和数据掉包统一考虑,存储数据并利用历史数据代替已丢失数据进行一致性协调。结合有限时间一致性和人工势场来构造控制律以实现多AUVs的无碰撞协调控制。仿真分析了通信距离受限以及掉队情形下的具有可变通信拓扑的多智能体系统的协同控制。针对环境噪声和外扰动的影响,研究基于卡尔曼滤波器的多智能体系统的前馈和反馈最优一致性跟踪问题。
6.研究多自主水下航行器小尺度编队控制问题。首先研究了二维平面内的有限时间编队控制。将各AUVs相对于虚拟领航者的相对位置转换为各自的期望位置,并利用第三章提出的有限时间控制律来实现各AUVs在有限时间内的跟踪控制。其次研究多自主水下航行器在空间位置和姿态上的有限时间编队控制问题。提出一种有限时间的二阶一致性控制算法,对AUV的速度(线速度和角速度)和位移(平移和角度)进行一致性协商。仿真研究了不同通信距离下的多AUV编队和具有最大速率约束下的多AUV编队控制,表明各个AUV能在有限时间内实现位置和速度的一致性。
Cooperative control of multiple autonomous underwater vehicles (AUVs) plays an important role on marine scientific investigation and marine development. The formation of multiple AUVs can significantly enhance the application capacities on the marine sampling, imaging, surveillance and communications, etc. According to the spatial distribution of the formation of multiple underwater vehicles, we classify the formation control problem into two formation categories on spatial scales. We consider the large-scale formation control with many AUVs and with long ranges among them and the small scale formation control referring steering angles of AUVs, respectively. The major results are as follows:
1. The framework of formation control is investigated and a virtual-leader-follower formation control scheme combinating leader-follower with virtual structure is introduced. Considering the signal attentuation and distortion resulted from underwater communication range constraints, we divide AUVs in space into some clusters according to their relative postions. Leaders of all clusters can track their desired trajectories based on virtual strcture. Followers within all clusters follow their corresponding leader with the leader’s disired tracking trajectory, which realizes the leader-following formation. Numerical simulation results show the effectiveness of the control strategy.
2. The design method of finite-time tracking controller is investigated. A compound finite-time tracking control linearization algorithm based on feedback linearization and variable structure is proposed. Firstly, the reaching-control law is used to drive the steering angle trajectory with inertial error in finite time toward the given switching surface neighborhood. When the steering angle trajectory reaches inside the neighborhood, the chattering-free control law based on continuous and smooth state-feedback drives the trajectory to the switching surface precisely while converting the original nonlinear system into a reduced system. Secondy, the state-feedback control law for the reduced system is designed to stabilize the position error in finite time. Numerical simulations show the desired trajectory of AUV is attained fully in finite time without chattering.
3. The trajectory-tracking problem for AUV based on nonlinear filtering is considered. Based on the research in the second chapter, the estimation states of the error-system with system noise and measurement noise are used to construct the feedback control law based on extended kalman filter and unscented kalman filter with upper triangular and diagonal matrix decomposition of the error covariance. Numerical simulations show the effectiveness of the control scheme.
4. The large-scale formation control of multiple autonomous underwater vehicles based on the consensus algorithms of multi-agent systems with first-order dynamics is investigated. For the consensus problem of AUVs with different virtual leader reference information, we propose two-level finite-time consensus algorithms within a sampling period, which simultaneously carry out the consensus on different reference information and the consensus on different AUVs’states, respectively. Formation control with virtual leader for multiple agents, formation control with single leader based on Leader-follower, hierarchical formation control for multiple agents based on Leader-follower and formation control for multiple agents with constraints on communication ranges are investiaged respectively, and a series of finite-time consensus tracking control protocol for multiple agents are presented.
5. The large-scale distributed cooperative control of autonomous underwater vehicles is investigated. We consider the network-induced time-delay and data dropout under large-scale cooperative control in a unified framework. AUVs store received state information from their neighbors and use the historical information to make the consensus when data links has failed or information has been lost based on data sampling and holding. The collicsion-free coordinated control of AUVs is carried out based on finite-time consensus algorithms and artificial potential field. The cooperative control of multiple agents system with variable communication topologies under communication ranges constraints and some agents’dropping-out is analysed with illustrative examples. To suppress the influence of environmental noise and external disturbance, the feedforward and feedback optimal consensus tracking control of multiple agents system based on kalman filtering is studied.
6. The small-scale formation control of autonomous underwater vehicles is investigated. Firstly, the finite-time formation control in two-dimension space is considered. The relative positions to the virtual leader are transformed into their desired positions, and the control law in the third chapter is proposed to make the AUVs track the desired trajectories in finite time. Secondly, the finite-time formation control of autonomous underwater vehicles with positions and attitudes in three-dimension space is investigated. A finite-time consensus algorithm for second-order system is proposed, the consensus on velocities of AUV (linear velocity and angular velocity) and positions (displacement and angles) are carried out. We demonstrate the formation control of multiple AUVs with different communication ranges and with constraints on maximum velocity, which show that the finite-time consensus on positions and velocities is obtained.
1.研究多AUVs编队控制框架,提出一种融合主从(Leader-follower)和虚拟结构(Virtual Structure)思想的虚拟-主从递阶结构编队控制策略。考虑到水下远距离水声通信信号衰减和失真约束,将AUVs按在队列中的相对位置分为若干个簇。簇间各Leader的编队控制采用虚拟结构。簇内各Follower以该簇Leader的路径作为跟踪期望参考路径,以实现Follower跟踪Leader的编队控制。仿真验证了控制策略的有效性。
2.研究有限时间跟踪控制器设计问题。基于反馈线性化和变结构控制思想提出一种复合的有限时间线性化跟踪控制算法。首先AUV驶向角初始误差快速收敛到滑模边界层,然后采用连续平滑状态反馈控制律来实现驶向角误差的无抖振快速镇定,同时将原系统转化为低阶系统。其次针对低阶系统设计了位置误差的状态反馈控制律,实现了位置误差的有限时间镇定。数值仿真表明了AUV能够在有限时间内无抖振地完全跟踪上期望轨迹。
3.研究基于非线性滤波的水下航行器轨迹跟踪控制问题。在第二章研究基础上,针对有系统噪声和量测噪声的误差系统,分别利用扩展卡尔曼滤波(Extended Kalman Filter, EKF)和基于上三角-对角矩阵(Upper triangular and Diagonal matrix, UD)分解误差协方差矩阵的无迹滤波(Unscented Kalman Filter, UKF)进行误差状态估计,并以估计值构成反馈控制律。通过数值仿真验证了控制策略的有效性。
4.研究基于一阶动态智能体系统的一致性算法来实现大尺度多自主水下航行器的编队控制问题。针对各AUV拥有不同虚拟领航者信息(参考信息)的情况,提出在一个仿真步长时间内对参考信息和各AUV信息分别进行一致性协同的两层有限时间一致性算法。分别研究了基于虚拟Leader的多智能体编队控制、基于Leader-follower的单Leader编队控制、基于Leader-follower的递阶式多智能体编队控制和具有通信距离约束的智能体的编队控制,提出了一系列的多智能体有限时间一致性跟踪控制协议。
5.研究基于二阶一致性算法的多自主水下航行器大尺度分布式协同控制问题。针对大尺度协同控制导致的时延和数据掉包问题,基于数据采样和保持原理,将时延和数据掉包统一考虑,存储数据并利用历史数据代替已丢失数据进行一致性协调。结合有限时间一致性和人工势场来构造控制律以实现多AUVs的无碰撞协调控制。仿真分析了通信距离受限以及掉队情形下的具有可变通信拓扑的多智能体系统的协同控制。针对环境噪声和外扰动的影响,研究基于卡尔曼滤波器的多智能体系统的前馈和反馈最优一致性跟踪问题。
6.研究多自主水下航行器小尺度编队控制问题。首先研究了二维平面内的有限时间编队控制。将各AUVs相对于虚拟领航者的相对位置转换为各自的期望位置,并利用第三章提出的有限时间控制律来实现各AUVs在有限时间内的跟踪控制。其次研究多自主水下航行器在空间位置和姿态上的有限时间编队控制问题。提出一种有限时间的二阶一致性控制算法,对AUV的速度(线速度和角速度)和位移(平移和角度)进行一致性协商。仿真研究了不同通信距离下的多AUV编队和具有最大速率约束下的多AUV编队控制,表明各个AUV能在有限时间内实现位置和速度的一致性。
Cooperative control of multiple autonomous underwater vehicles (AUVs) plays an important role on marine scientific investigation and marine development. The formation of multiple AUVs can significantly enhance the application capacities on the marine sampling, imaging, surveillance and communications, etc. According to the spatial distribution of the formation of multiple underwater vehicles, we classify the formation control problem into two formation categories on spatial scales. We consider the large-scale formation control with many AUVs and with long ranges among them and the small scale formation control referring steering angles of AUVs, respectively. The major results are as follows:
1. The framework of formation control is investigated and a virtual-leader-follower formation control scheme combinating leader-follower with virtual structure is introduced. Considering the signal attentuation and distortion resulted from underwater communication range constraints, we divide AUVs in space into some clusters according to their relative postions. Leaders of all clusters can track their desired trajectories based on virtual strcture. Followers within all clusters follow their corresponding leader with the leader’s disired tracking trajectory, which realizes the leader-following formation. Numerical simulation results show the effectiveness of the control strategy.
2. The design method of finite-time tracking controller is investigated. A compound finite-time tracking control linearization algorithm based on feedback linearization and variable structure is proposed. Firstly, the reaching-control law is used to drive the steering angle trajectory with inertial error in finite time toward the given switching surface neighborhood. When the steering angle trajectory reaches inside the neighborhood, the chattering-free control law based on continuous and smooth state-feedback drives the trajectory to the switching surface precisely while converting the original nonlinear system into a reduced system. Secondy, the state-feedback control law for the reduced system is designed to stabilize the position error in finite time. Numerical simulations show the desired trajectory of AUV is attained fully in finite time without chattering.
3. The trajectory-tracking problem for AUV based on nonlinear filtering is considered. Based on the research in the second chapter, the estimation states of the error-system with system noise and measurement noise are used to construct the feedback control law based on extended kalman filter and unscented kalman filter with upper triangular and diagonal matrix decomposition of the error covariance. Numerical simulations show the effectiveness of the control scheme.
4. The large-scale formation control of multiple autonomous underwater vehicles based on the consensus algorithms of multi-agent systems with first-order dynamics is investigated. For the consensus problem of AUVs with different virtual leader reference information, we propose two-level finite-time consensus algorithms within a sampling period, which simultaneously carry out the consensus on different reference information and the consensus on different AUVs’states, respectively. Formation control with virtual leader for multiple agents, formation control with single leader based on Leader-follower, hierarchical formation control for multiple agents based on Leader-follower and formation control for multiple agents with constraints on communication ranges are investiaged respectively, and a series of finite-time consensus tracking control protocol for multiple agents are presented.
5. The large-scale distributed cooperative control of autonomous underwater vehicles is investigated. We consider the network-induced time-delay and data dropout under large-scale cooperative control in a unified framework. AUVs store received state information from their neighbors and use the historical information to make the consensus when data links has failed or information has been lost based on data sampling and holding. The collicsion-free coordinated control of AUVs is carried out based on finite-time consensus algorithms and artificial potential field. The cooperative control of multiple agents system with variable communication topologies under communication ranges constraints and some agents’dropping-out is analysed with illustrative examples. To suppress the influence of environmental noise and external disturbance, the feedforward and feedback optimal consensus tracking control of multiple agents system based on kalman filtering is studied.
6. The small-scale formation control of autonomous underwater vehicles is investigated. Firstly, the finite-time formation control in two-dimension space is considered. The relative positions to the virtual leader are transformed into their desired positions, and the control law in the third chapter is proposed to make the AUVs track the desired trajectories in finite time. Secondly, the finite-time formation control of autonomous underwater vehicles with positions and attitudes in three-dimension space is investigated. A finite-time consensus algorithm for second-order system is proposed, the consensus on velocities of AUV (linear velocity and angular velocity) and positions (displacement and angles) are carried out. We demonstrate the formation control of multiple AUVs with different communication ranges and with constraints on maximum velocity, which show that the finite-time consensus on positions and velocities is obtained.
引文
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