打滑状态下的多移动机器人编队自适应控制

本文针对由领航跟随控制策略协调运动的多移动机器人编队,研究跟随机器人存在打滑状态的自适应控制器设计问题.首先,通过移动机器人打滑状态的运动学特性分析,建立“距离–角度”编队控制模型.然后,利用径向基函数神经网络(RBF NN)对系统中由打滑引起的未知信息,构建非线性逼近器;并根据李雅普诺夫稳定性理论和非线性有界扰动稳定性理论,证明了设计的嵌入了RBF NN的自适应控制器能保证闭环控制系统状态的收敛和有界.通过分析编队误差控制模型,可将不打滑状态视为系统的一种特殊情况,而嵌入控制器中的RBF NN能自适应打滑和不打滑两种状态,从而使得控制器在两种状态下均有效.最后利用仿真研究,验证了本文所提方法的正确性和有效性.     

含驱动器动力学的移动机器人编队自适应控制

针对含有驱动器及编队动力学的多非完整移动机器人编队控制问题,基于领航者-跟随者[l-ψ]控制结构,通过反步法设计了一种将运动学控制器与驱动器输入电压控制器相结合的新型控制策略。采用径向基神经网络（RBFNN）对跟随者及领航者动力学非线性不确定部分进行在线估计,并通过自适应鲁棒控制器对神经网络建模误差进行补偿。该方法不但解决了移动机器人编队控制的参数与非参数不确定性问题,同时也确保了机器人编队在期望队形下对指定轨迹的跟踪;基于Lyapunov方法的设计过程,保证了控制系统的稳定与收敛;仿真结果表明了该方法的有效性。     

具有速度调节机制的船舶事件触发编队控制

针对实际海洋环境下, 欠驱动水面船舶(USV)编队控制任务中存在控制输入频繁抖振、模型结构未知和航行速度难以控制等问题, 本文提出一种具有速度调节性能的事件触发编队控制算法. 首先, 该算法采用径向基神经网络(RBF–NN)对模型结构不确定进行逼近. 同时, 为了减少控制输入频繁抖振和通信信道占用次数, 设计了一种满足控制器与神经网络权重估计器同步触发的事件触发机制. 其次, 针对现有领导–跟随方法中存在的领导船速度信息不可知、跟随船速度不可控的问题, 设计了一种自适应速度调节器, 使跟随船在不需要领导船速度信息的情况下实时地调节航行速度. 利用李雅普诺夫稳定性理论证明了所提控制算法满足半全局一致最终有界收敛. 最后, 通过仿真实验验证了所提出算法的有效性.     

含未知信息的轮式移动机器人编队确定学习控制

本文研究含未知信息的轮式移动机器人(wheeled mobile robots,WMR)的编队控制问题.首先,基于领航–跟随法和虚拟结构法,将WMR编队控制问题转化为跟随机器人对参考虚拟机器人的跟踪控制问题.然后,利用径向基函数神经网络(radial basis function neural networks,RBF NN)对WMR的未知系统动态进行学习,以及根据李雅普诺夫稳定性理论设计了稳定的自适应RBF NN控制器和RBF NN权值估计的学习率.依据确定学习理论,闭环系统内部信号在对回归轨迹实现跟踪控制的过程中满足部分持续激励(persistent excitation,PE)条件.随着PE条件的满足,RBF NN权值估计收敛到其理想权值,实现了对未知闭环系统动态的准确学习.最后,利用学习结果设计了RBF NN学习控制器,保证了控制系统的稳定与收敛,实现了闭环稳定性和改进了控制性能,并通过仿真验证了所提控制方法的正确性和有效性.     

移动机器人编队的递归模糊神经网络滑模控制

研究非完整移动机器人编队控制优化问题,由于动态模型存在诸多不稳定性,针对领航者-跟随者l-ψ控制结构,提出了一种Back stepping运动学控制器与自适应神经滑模控制器相结合的新型控制策略.采用动态递归模糊神经网络(dynam-ic recurrent fuzzy neural network,DRFNN)对跟随者及领航者动力学非线性不确定部分进行在线估计,并通过自适应鲁棒控制器对神经网络建模误差进行补偿.所提方法不但解决了移动机器人编队控制的参数与非参数不确定性问题,同时也确保了机器人编队在期望队形下对指定轨迹的跟踪;根据Lyapunov方法的设计过程,保证了控制系统的稳定;仿真结果表明了改进方法对机器人编队优化控制的有效性.     

基于轨迹跟踪车式移动机器人编队控制

针对车式移动机器人的运动学模型特点, 提出一种基于轨迹跟踪多机器人编队控制方法. 首先利用编队结构参数确定队形, 根据编队轨迹和相关参数生成虚拟机器人, 把编队控制转化为跟随机器人对虚拟机器人的轨迹跟踪; 然后运用反步法构造车式移动机器人轨迹跟踪系统的Lyapunov 函数, 通过使该函数负定, 得到跟随机器人的轨迹跟踪控制器; 最后在Microsoft robotics developer studio 4 (MRDS4) 中搭建3D 仿真平台, 设计了3 组实验, 所得结果表明了所提出方法的有效性.     

多移动机器人的领航-跟随编队避障控制

针对多移动机器人的编队控制问题,提出了一种结合Polar Histogram避障法的领航-跟随协调编队控制算法。该算法在领航-跟随l-φ编队控制结构的基础上引入虚拟跟随机器人,将编队控制转化为跟随机器人对虚拟跟随机器人的轨迹跟踪控制。结合移动机器人自身传感器技术,在简单甚至复杂的环境下为机器人提供相应的路径运动策略,实现实时导航的目的。以两轮差动Qbot移动机器人为研究对象,搭建半实物仿真平台,进行仿真实验。仿真结果表明:该方法可以有效地实现多移动机器人协调编队和避障控制。     

基于径向基函数神经网络的移动机器人多变量固定时间编队控制

针对带多不确定性的一组非完整移动机器人的编队控制收敛问题,提出了基于径向基函数神经网络的移动机器人多变量固定时间领航者-跟随者编队控制算法.RBFNN补偿了系统所受的多不确定性,并消除了鲁棒控制的抖振现象.基于固定时间理论和Lyapunov方法进行了控制算法设计,使所提出的控制方法保证了编队控制系统中的所有信号全局固定时间收敛,在任意系统初始条件下,在通过参数设计的固定时间内,使机器人编队达到期望编队.仿真结果显示了所提出算法的有效性.     

一类非完整移动机器人编队控制方法

针对大部分两轮非完整移动机器人轮轴中心与几何中心不重合的特点, 提出一种多机器人协调编队控制算法. 构造队形参数矩阵确定编队形状, 根据领航机器人和相关队形参数生成虚拟机器人, 把编队控制分解为跟随机器人对虚拟机器人的轨迹跟踪. 建立虚拟机器人与跟随机器人之间误差系统模型, 利用Lyapunov 理论设计相应控制器, 从而实现队形保持和变换. 应用microsoft robotics developer studio 4(MRDS4) 搭建3D 仿真平台, 设计3 组实验, 结果进一步验证了所提出方法的有效性.

     

基于神经网络的不确定移动机器人鲁棒自适应跟踪控制

针对含运动学未知参数以及动力学模型不确定的非完整轮式移动机器人轨迹跟踪问题,基于Radical Basis Function（径向基函数）神经网络,提出了一种鲁棒自适应控制器.首先,考虑移动机器人运动学参数未知的情况,提出了一种含自适应参数的运动学控制器,用以补偿参数不确定性导致的系统误差;其次,利用神经网络控制技术,对于机器人在移动中动力学模型不确定问题,提出了一种具有鲁棒性的动力学控制器,使得移动机器人可以在不知道具体动力学模型的情况下跟踪到目标轨迹;最后利用Lyapunov稳定性理论证明了整个系统的稳定性.通过数值仿真验证了所设计的控制器的可行性.     

Asymptotic Adaptive Neural Network Tracking Control of Nonholonomic Mobile Robot Formations

In this paper, asymptotically stable control laws are developed for leader–follower based formation control using backstepping in order to accommodate the dynamics of the robots and the formation. First, a kinematic controller is developed around control strategies for single mobile robots and the idea of virtual leaders. The virtual leader is replaced with a physical mobile robot leader, and an auxiliary velocity control law is developed in order to prove the global asymptotic stability of the followers which in turn allows the local asymptotic stability of the entire formation. A novel approach is taken in the development of the dynamical controller such that the torque control inputs for the follower robots include the dynamics of the follower robot as well as the dynamics of its leader, and two cases are considered—the case when the robot dynamics are known and the case when they are unknown. In the first case, a robust adaptive control term is utilized to account for unmodeled dynamics. For the latter, a robust adaptive term is augmented with a NN control law to achieve asymptotic tracking performance in contrast with most NN controllers where a bounded tracking error result is shown. Additionally, the NN approximation error is assumed to be a function of tracking errors instead of a constant upper bound, which is commonly found in the literature. The stability of the follower robots as well as the entire formation is demonstrated in each case using Lyapunov methods and numerical results are provided.     

From Trajectory Tracking Control to Leader–Follower Formation Control

Abstract

This work investigates the leader–follower formation control of multiple nonholonomic mobile robots. First, the formation control problem is converted into a trajectory tracking problem and a tracking controller based on the dynamic feedback linearization technique drives each follower robot toward its corresponding reference trajectory in order to achieve the formation. The desired orientation for each follower is selected such that the nonholonomic constraint of the robot is respected, and thus the tracking of the reference trajectory for each follower is feasible. An adaptive dynamic controller that considers the actuators dynamics in the design procedure is proposed. The dynamic model of the robots includes the actuators dynamics in order to obtain the velocities as control inputs instead of torques or voltages. Using Lyapunov control theory, the tracking errors are proven to be asymptotically stable and the formation is achieved despite the uncertainty of the dynamic model parameters. In order to assess the proposed control laws, a ROS-framework is developed to conduct real experiments using four ROS-enabled mobile robots TURTLEBOTs. Moreover, the leader fault problem, which is considered as the main drawback of the leader–follower approach, is solved under ROS. An experiment is conducted where in order to overcome this problem, the desired formation and the leader role are modified dynamically during the experiment.     

多机器人主—从行星式编队控制

研究了二阶积分器描述的多机器人主—从行星式编队控制问题,提出了将多机器人编队分解为每个机器人对各自具有时变速度的虚拟机器人的跟踪控制,使得每个机器人相对于虚拟机器人的位置与速度跟踪误差收敛为零且彼此不相碰撞,此时编队系统收敛到理想队形.在统一的算法框架下,分别实现了跟随者以领航者为中心的公转运动编队(revolution formation,RF)模式和跟随者与领航者保持期望距离、期望速度的编队(desiredformation,DF)模式.公转运动编队(RF)模式适用于异构多机器人系统的环境探索任务;保持期望距离、期望速度的编队(DF)模式适用于自主水下机器人(AUV)、无人机(UAV)等合作与协调任务.应用李亚普诺夫稳定性理论对控制算法的稳定性进行了分析,并通过计算机仿真验证了该方法的有效性.     

Formation control of mobile robots with obstacle avoidance based on GOACM using onboard sensors

This paper deals with the problem of formation control for nonholonomic mobile robots under a cluttered environment. When the obstacles are not detected, the follower robot calculates its waypoint to track, based on the leader robot’s state. The proposed geometric obstacle avoidance control method (GOACM) guarantees that the robot avoids the static and dynamic obstacles using onboard sensors. Due to the difficulty for the robot to simultaneously get overall safe boundary of an obstacle in practice, a safe line, which is perpendicular to the obstacle surface, is used instead of the safe boundary. Since GOACM is executed to find a safe waypoint for the robot, GOACM can effectively cooperate with the formation control method. Moreover, the adaptive controllers guarantee that the trajectory and velocity tracking errors converge to zero with the consideration of the parametric uncertainties of both kinematic and dynamic models. Simulation and experiment results present that the robots effectively form and maintain formation avoiding the obstacles.     

Adaptive leader-follower formation control of mobile robots with unknown skidding and slipping effects

This paper investigates an adaptive leader-follower formation control problem of multiple mobile robots in the presence of unknown skidding and slipping. First, we employ the concept of virtual robots to achieve the desired formation and derive the kinematics of the virtual leader and follower robots considering skidding and slipping effects. Then, we design an adaptive formation controller based on a two-dimensional error surface where the adaptive technique is used for compensating the unknown skidding and slipping effects that influence the follower robots. From Lyapunov stability theorem, we show that all errors of the closed-loop system are uniformly ultimately bounded, and thus the desired formation is successfully achieved regardless of the presence of unknown skidding and slipping effects. Simulation results are provided to demonstrate the effectiveness of the proposed formation control scheme.     

Leader–follower formation control of nonholonomic mobile robots based on a bioinspired neurodynamic based approach

This paper investigates the leader–follower formation control problem for nonholonomic mobile robots based on a bioinspired neurodynamics based approach. The trajectory tracking control for a single nonholonomic mobile robot is extended to the formation control for multiple nonholonomic mobile robots based on the backstepping technique, in which the follower can track its real-time leader by the proposed kinematic controller. An auxiliary angular velocity control law is proposed to guarantee the global asymptotic stability of the followers and to further guarantee the local asymptotic stability of the entire formation. Also a bioinspired neurodynamics based approach is further developed to solve the impractical velocity jumps problem. The rigorous proofs are given by using Lyapunov theory. Simulations are also given to verify the effectiveness of the theoretical results.     

带有未知参数和有界干扰的移动机器人轨迹跟踪控

针对模型参数未知和存在有界干扰的非完整移动机器人的轨迹跟踪控制问题,本文提出了一种鲁棒自适应轨迹跟踪控制器方法.非完整移动机器人的控制难点在于它的运动学系统是欠驱动的.针对这一难点,本文利用横截函数的思想,引入新的辅助控制器,使得非完整移动机器人系统不再是一个欠驱动系统,缩减了控制器设计的难度,进而利用非线性自适应算法和参数映射方法构造李雅谱诺夫函数.通过李雅普诺夫方法设计控制器和参数自适应器,从而使得非完整移动机器人的跟随误差任意小,即可以任意小的误差来跟随任意给定的参考轨迹.仿真结果证明了方法的有效性.