滑动与打滑条件下的轮式移动机器人自抗扰跟踪控制

针对具有未知的滑动与打滑的轮式移动机器人(WMR),提出了一种基于自抗扰思想的跟踪控制策略.首先建立了滑动与打滑条件下的轮式移动机器人动力学模型.其次,由反步法设计运动学控制器,基于模型设计线性扩张观测器和动力学控制器,并给出了控制器稳定性分析.最后与积分滑模控制进行了仿真对比,结果表明该控制方法的误差收敛速度更快.观测器能够精确估计滑动与打滑及动力学不确定性对机器人的扰动,提高了轮式移动机器人轨迹跟踪的鲁棒性.     

基于生存理论的非完整约束轮式机器人高速避障控制

轮式移动机器人现有的避障控制方法大多需要在避障过程中进行减速处理, 会影响移动效率. 鉴于此, 将生存理论应用于轮式移动机器人的反应式避障控制. 分析非完整约束轮式机器人的仿射非线性系统模型和约束条件, 利用弹性边界升维和控制模型退化的方法给出系统的生存性设计, 并利用最优化方法得出机器人高速避障控制器. 最后通过仿真实验, 表明了轮式机器人高速避障控制的有效性.

     

Approximation-based adaptive control for a class of mobile robots with unknown skidding and slipping

An adaptive tracking control approach using function approximation technique is proposed for trajectory tracking of Type (2,0) wheeled mobile robots with unknown skidding and slipping in polar coordinates and at the dynamic level. The nonlinear disturbance observer (NDO) is used to estimate a nonlinear disturbance term including unknown skidding and slipping. The adaptive control system is designed via the function approximation technique using neural networks employed to compensate the NDO error. It is proved that all signals of the controlled closed-loop system are uniformly bounded and the point tracking errors converge to an adjustable neighborhood of the origin regardless of large initial tracking errors and unknown skidding and slipping. Simulation results are presented to validate the good tracking performance and robustness of the proposed control system against unknown skidding and slipping.     

Adaptive motion control of wheeled mobile robot with unknown slippage

As a major representative nonholonomic system, wheeled mobile robot (WMR) is often used to travel across off-road environments that could be unstructured environments. Slippage often occurs when WMR moves in slopes or uneven terrain, and the slippage generates large accumulated position errors in the vehicle, compared with conventional wheeled mobile robots. An estimation of the wheel slip ratio is essential to improve the accuracy of locomotion control. In this paper, we propose an improved adaptive controller to allow WMR to track the desired trajectory under unknown longitudinal slip, where the stabilisation of the closed-loop tracking system is guaranteed by the Lyapunov theory. All system states use neural network online weight tuning algorithms, which ensure small tracking errors and no loss of stability in robot motion with bounded input signals. We demonstrate superior tracking results using the proposed control method in various Matlab simulations.     

移动机器人的线性自抗扰控制设计与实验验证

为了实现移动机器人的高精度轨迹跟踪控制, 设计了一种基于扩张状态观测器的扰动抑制方法和相应的 实验验证平台. 首先, 考虑到不确定扰动如车轮纵向和侧向滑动对移动机器人系统控制性能的影响, 建立了受扰下 的运动学模型; 然后, 基于扩张后的运动学模型设计了扩张状态观测器来估计系统扰动; 接着, 利用扰动估计构建 了线性自抗扰控制器, 并利用Lyapunov函数证明了闭环系统的稳定性; 同时, 基于MATLAB/Simulink软件和微控制 器搭建了所推荐控制算法的实验验证平台. 最后, 仿真和实验结果都验证了所提出控制方法的有效性.     

一种新的高动态GPS载波跟踪算法

提出了基于无迹粒子滤波（UPF）算法的高动态GPS载波跟踪环路,仿真分析了该方案在高斯噪声和非高斯噪声环境下对高动态GPS信号的跟踪性能,并与分别基于扩展卡尔曼滤波（EKF）、无迹卡尔曼滤波（UKF）、粒子滤波（PF）及扩展卡尔曼粒子滤波（EPF）这四种算法的载波跟踪环路进行了性能对比。仿真结果表明,基于UPF估计器的载波跟踪环路在高动态、弱信号以及非高斯噪声环境下具有优越的跟踪性能,既可以提高跟踪精度,又解决了非高斯噪声干扰问题。通过模拟实验验证了该方案的有效性。     

不确定轮式移动机器人的任意轨迹跟踪

本文研究参数不确定轮式移动机器人的任意轨迹跟踪统一控制问题.通过引入坐标变换、输入变换和辅助动态,将机器人模型转换为合适的形式;进而运用Lyapunov方法和自适应技术设计了一种自适应统一控制器,该控制器可以保证跟踪误差全局一致最终有界,且最终界大小可以通过调整控制器参数而任意调节.仿真结果验证了控制律的有效性.     

非平衡负载下轮式移动机器人的抗扰PID控制

在非平衡负载条件下,轮式移动机器人(WMR)的前进、转向速度耦合,影响着轨迹跟踪和避障等运动控制性能.为此,本文提出了一种基于抗扰PID(DR–PID)控制器的WMR速度调节主动抗扰(ADR)控制策略.首先,建立WMR的速度耦合模型,引入解耦矩阵减小静态耦合作用;然后,基于一类改进干扰观测器(DOB)控制方法,设计一种具有ADR能力的PID控制器,即DR–PID,用于WMR的速度分散调节.进一步,考虑高频增益不匹配/不确定性,分析闭环系统稳定性条件.所得结论揭示了PID控制器的抗扰机理;最后,在不平衡负载条件下开展WMR运动控制实验研究,实验结果验证了所提方法的有效性.     

轮式移动机器人的数据驱动轨迹跟踪滑模约束控制

针对有输入饱和约束的轮式移动机器人(WMR)的轨迹跟踪问题,提出一种抗饱和无模型自适应积分终端滑模控制方案.该方案基于紧格式动态线性化技术,构建WMR系统的在线数据驱动模型.在积分终端滑模控制器设计过程中,引入动态抗饱和补偿器,以解决WMR系统轨迹跟踪过程中执行器饱和问题.控制器设计仅利用控制系统的输入输出数据,与WMR系统模型信息无关.因此,针对不同类型的WMR系统,该方案均可实现.最后,通过仿真实验将所提出的方法与PID方法的控制效果进行对比,仿真结果表明,所提出的控制算法的跟踪误差更小且响应速度更快.     

轮式移动机器人固定时间轨迹跟踪控制

针对存在外部干扰的轮式移动机器人轨迹跟踪控制问题,提出一种固定时间轨迹跟踪控制方案.首先,对于轮式移动机器人的运动学误差模型,基于一种新颖的积分滑模面设计固定时间运动学速度控制器,使跟踪误差在固定时间收敛到原点所在的邻域内;其次,对于轮式移动机器人的动力学模型,设计固定时间干扰观测器对外部干扰信息进行估计,提出一种固定时间轨迹跟踪控制器,以确保动力学系统的固定时间稳定性,实现轮式移动机器人的高精度轨迹跟踪控制;最后,通过仿真结果验证所设计的轨迹跟踪控制方案的有效性.     

基于速度空间的移动机器人同时避障和轨迹跟踪方法

针对有障碍物环境下非完整轮式 移动机器人的轨迹跟踪问题,提出一种基于速度空间的同时避障和轨迹跟踪方法(VSTTM).首先,根据机器人 的动力学特性构建速度空间,得到由速度元组构成的控制集;然后,构造目标函数并对各控制量进行 评价,其中跟踪误差评价函数评估跟踪效果,碰撞检测函数检测是否发生碰撞,终端状态惩罚项保证 算法的稳定性;最后,通过优化过程找到最优的无碰控制量.仿真结果表明了所提出方法的有效性.     

A Gaussian wavelet network-based robust adaptive tracking controller for a wheeled mobile robot with unknown wheel slips

In this paper, a robust adaptive tracking controller is proposed for a nonholonomic wheeled mobile robot (WMR) in the presence of unknown wheel slips. The role of the Gaussian wavelet network in this proposed controller is to approximate unknown smooth nonlinear dynamic functions due to no prior knowledge of the dynamic parameters of the WMR. In addition, one robust law is employed at the kinematic level so as to compensate the harmful effects of the unknown wheel slips, and another robust law is used at the dynamic level to overcome total uncertainties caused by dynamic parameter variations, external disturbances, etc. The stability of the whole closed-loop control system is proved in accordance with Lyapunov theory and Barbalat's lemma. Ultimately, the simulation results are shown in comparison with those of another control method under the same condition to confirm the validity and efficiency of this proposed control method.     

Design and Implementation of an Inverse Dynamics Controller for Uncertain Nonholonomic Robotic Systems

This paper addresses the trajectory tracking control problem of nonholonomic robotic systems in the presence of modeling uncertainties. A tracking controller is proposed such that it combines the inverse dynamics control technique and an adaptive robust PID control strategy to preserve robustness to both parametric and nonparametric uncertainties. A SPR-Lypunov stability analysis demonstrates that tracking errors are uniformly ultimately bounded (UUB) and exponentially converge to a small ball containing the origin. The proposed inverse dynamics tracking controller is successfully applied to a nonholonomic wheeled mobile robot (WMR) and experimental results are presented to validate the effectiveness of the proposed controller.     

A Scalable Adaptive Approach to Multi-Vehicle Formation Control with Obstacle Avoidance

This paper deals with the problem of distributed formation tracking control and obstacle avoidance of multi-vehicle systems (MVSs) in complex obstacle-laden environments. The MVS under consideration consists of a leader vehicle with an unknown control input and a group of follower vehicles, connected via a directed interaction topology, subject to simultaneous unknown heterogeneous nonlinearities and external disturbances. The central aim is to achieve effective and collision-free formation tracking control for the nonlinear and uncertain MVS with obstacles encountered in formation maneuvering, while not demanding global information of the interaction topology. Toward this goal, a radial basis function neural network is used to model the unknown nonlinearity of vehicle dynamics in each vehicle and repulsive potentials are employed for obstacle avoidance. Furthermore, a scalable distributed adaptive formation tracking control protocol with a built-in obstacle avoidance mechanism is developed. It is proved that, with the proposed protocol, the resulting formation tracking errors are uniformly ultimately bounded and obstacle collision avoidance is guaranteed. Comprehensive simulation results are elaborated to substantiate the effectiveness and the promising collision avoidance performance of the proposed scalable adaptive formation control approach.      

基于改进RBPF算法的轮式机器人SLAM导航系统设计

传统的机器人导航系统在复杂的地形环境中常常无法引导机器人躲避突然出现的障碍物,无法精准采集数据。为此提出一种改进RBPF算法的轮式机器人SLAM导航系统,对系统硬件和软件进行设计。系统硬件主要由导航功能模块、底盘驱动模块、控制模块组成,利用RPLIDAR A1型激光测距雷达设计导航功能模块,并设计底盘驱动模块和控制模块。软件设计中,以改进RBPF算法为基础,设计了轮式机器人SLAM导航系统的实现程序,应用算法代入的方式加强了普通轮式机器人导航算法对粒子计算与卡尔曼滤波的敏感程度。实验结果表明,改进RBPF算法在避障和计算误差方面的优势,证明了该系统相比传统避障后的路径选择更便捷,导航错误出现率降低了30%左右。     

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.     

一种改进扩展卡尔曼滤波新方法

针对现有的迭代扩展卡尔曼滤波（EIEKF）跟踪时估计精度较低这一不足,提出了一种改进扩展卡尔曼滤波（NIEKF）新方法。本文将迭代滤波理论引入到扩展卡尔曼滤波方法中,重复利用观测信息,采用经典的非线性非高斯模型进行仿真实验,给出了该方法与扩展卡尔曼滤波（EKF）、Unscented 卡尔曼滤波（UKF）、现有的迭代扩展卡尔曼滤波（EIEKF）的仿真结果,并分析了其跟踪性能和均方根误差。实验结果表明,改进扩展卡尔曼滤波（NIEKF）新方法具有更高的估计精度。     

基于UKF的移动机器人主动建模及模型自适应控制方法

利用基于无色卡尔曼滤波（Unscented Kalman Filter, UKF）的状态和参数联合估计方法对移动机器人进行在线主动建模,基于该主动模型的逆动力学控制方法,实现了移动机器人对其自身不确定因素的自主性. 在针对全方位移动机器人的仿真实验中,验证了UKF对时变的状态和参数的收敛性和跟踪能力,并给出了不确定界. 基于主动建模的逆动力学控制方法与常值PID控制方法的比较结果,验证了该方法的有效性.     

Adaptive feedback linearizing control of nonholonomic wheeled mobile robots in presence of parametric and nonparametric uncertainties

In this paper, the integrated kinematic and dynamic trajectory tracking control problem of wheeled mobile robots (WMRs) is addressed. An adaptive robust tracking controller for WMRs is proposed to cope with both parametric and nonparametric uncertainties in the robot model. At first, an adaptive nonlinear control law is designed based on input–output feedback linearization technique to get asymptotically exact cancellation of the parametric uncertainty in the WMR parameters. The designed adaptive feedback linearizing controller is modified by two methods to increase the robustness of the controller: (1) a leakage modification is applied to modify the integral action of the adaptation law and (2) the second modification is an adaptive robust controller, which is included to the linear control law in the outer loop of the adaptive feedback linearizing controller. The adaptive robust controller is designed such that it estimates the unknown constants of an upper bounding function of the uncertainty due to friction, disturbances and unmodeled dynamics. Finally, the proposed controller is developed for a type (2, 0) WMR and simulations are carried out to illustrate the robustness and tracking performance of the controller.     

基于均方根容积卡尔曼的δ-GLMB多目标跟踪算法

在非线性高杂波密度场景下,高斯混合(Gaussian Mixture,GM)实现的δ-广义标签多伯努利滤波器(δ-Generalized Labeled Multi-Bernoulli Filter,δ-GLMB)难以准确地估计目标数目及运动状态。针对这一问题,提出基于均方根容积卡尔曼滤波(Square-rooted Cubature Kalman Filter,SCKF)的δ-GLMB高斯混合实现算法。基于三阶球面-径向容积准则选取一组等权的容积点集,对GM-δ-GLMB滤波器的伯努利分量传递过程中的高斯参量进行预测及更新,实现非线性模型系统下的目标跟踪。仿真结果表明,与现有的δ-GLMB滤波器的扩展卡尔曼滤波(Extended Kalman Filter,EKF)高斯混合实现及无迹卡尔曼滤波(Unscented Kalman Filter,UKF)高斯混合实现相比,该算法可提高非线性高杂波密度环境下的目标跟踪精度。