农业轮式移动机器人反演自适应滑模轨迹跟踪控制

为提高农业轮式移动机器人路径跟踪控制的鲁棒性,提出一种基于农业轮式移动机器人反演自适应滑模控制策略。运用反演控制设计其运动学控制律,保证位置跟踪误差渐进收敛到零;根据动力学模型,设计自适应滑模动力学控制律,实现农业轮式移动机器人左右轮平稳的运行;运用李雅普诺夫定理保证闭环系统的最终一致稳定性。仿真实验验证了该方法的有效性和优越性,能够实现正弦型曲线路径跟踪。     

基于观测器的轮式移动机器人路径跟踪控制

研究基于状态观测器的轮式移动机器人的路径跟踪控制问题.首先简要回顾了基于状态反馈的移动机器人的路径跟踪控制问题;进而通过适当的状态变换将移动机器人模型转换为合适的形式,并在移动机器人的位置可以测量的情况下设计了一种可保证状态观测误差指数收敛的状态观测器;最后结合状态反馈路径跟踪控制器和所设计的观测器得到了一种基于观测器的路径跟踪控制器,该控制器可以保证移动机器人的运动轨迹指数收敛到期望路径上.仿真结果证实了所提出的基于观测器的路径跟踪控制器的有效性.     

基于神经动力学的非完整移动机器人跟踪控制

主要研究了非完整移动机器人轨迹跟踪问题。基于后退控制和神经动力学生物激励模型,采用自适应参数调节的方法提出了一种新的跟踪控制器。该控制器能够生成平稳合理的速度,解决了以往大部分跟踪控制器所产生的速度突变问题,并且具有很好的鲁棒性。运用李雅普诺夫稳定性理论证明控制系统的稳定性。对连续、离散轨迹的仿真及与传统后退方法的比较分析验证了该方法的有效性。     

基于滚动时域优化的轮式移动机器人路径跟踪问题研究

轮式移动机器人是典型的非完整约束系统. 本文基于滚动时域控制策略研究轮式移动机器人的路径跟踪问题. 为了既能够保证移动机器人渐近收敛到期望轨迹, 又能够保证在线求解的优化问题的滚动可行性, 参考轨迹 被选为优化问题中的终端等式约束. 仿真结果验证了所提出的控制策略的有效性.     

基于自适应反步法的轮式移动机器人跟踪控制

轮式移动机器人是一种典型的非完整约束系统.基于反步法提出一种自适应扩展控制器,对含有未知参数的非完整轮式移动机器人动力学系统进行轨迹跟踪控制并且Lyapunov稳定性理论保证跟踪误差渐近收敛到零.为了克服速度跳变产生滑动,加入了神经动力学模型对控制器进行改进.以两驱动轮移动机器人为例,利用运动学自适应控制器设计出转矩控制器,有效解决了不确定非完整轮式移动机器人动力学系统的轨迹跟踪问题.仿真结果证明该方法的正确性和有效性.     

非时间参考的移动机器人路径跟踪控制

基于非时间参考的思想提出了一种移动机器人路径跟踪控制方法.首先选择移动机器人实际路径在某参考系下的x轴投影作为非时间参考量,并针对一类几何路径的跟踪建立移动机器人非时间参考的运动学模 型,据此设计以恒定速度跟踪期望路径的控制律,然后在此基础上给出跟踪任意几何路径的分段控制策略.此跟踪 控制系统所采用的参考量为非时间量􀁯,摆脱了时间因素的影响,因此能提高移动机器人在不确定环境中的跟踪能 力.仿真和物理实验表明了控制方法的有效性.􀁱     

基于全局视觉的轮式移动机器人轨迹跟踪控制

将视觉伺服控制思想引入到全局视觉条件下的轮式移动机器人轨迹跟踪控制中,提出一种基于消除图像特征误差的控制方法.讨论并推导了包含电机模型的非完整移动机器人动力学方程,设计了鲁棒速度跟踪控制器.实验结果证明了文中方法的有效性.􀁱     

具有参数不确定性的轮式移动机器人自适应backstepping控制

针对参数不确定的轮式移动机器人的轨迹跟踪问题,设计自适应跟踪控制器.基于移动机器人的动力学模型,采用backstepping积分方法,通过逐步递推选择适当的Lyapunov函数,设计基于状态反馈的自适应控制器,并进行了相应的稳定性分析.与传统PID控制进行仿真对比,结果表明提出的自适应控制策略能较好地补偿系统参数摄动的影响,提高了移动机器人的轨迹跟踪性能和鲁棒性.     

轮式移动机器人轨迹跟踪控制研究

针对轮式移动机器人参数摄动和内外部扰动等问题,提出一种新型的基于自适应扩张状态观测器的滑模控制算法。采用自适应虚拟速度控制器估计系统未知参数,滑模控制器抑制参数摄动和内外部扰动,非线性扩张状态观测器观测系统扰动并减小控制输入的抖振,实现了轨迹跟踪误差的快速收敛。利用Lyapunov稳定性理论证明了控制算法的稳定收敛性。将所提算法与传统自适应反演滑模算法进行对比,对比结果表明了所提算法的有效性和鲁棒性。     

A new adaptive tracking control scheme of wheeled mobile robot without longitudinal velocity measurement

This paper proposes a new adaptive trajectory tracking control scheme of the wheeled mobile robot without longitudinal velocity measurement. First, based on a kinematic controller, we obtain a new tracking error equation, which is suitable to develop an adaptive controller. Then, we develop a new adaptive trajectory tracking controller, which does not need any accurate values of the wheeled mobile robot parameters, including the driving motor parameters. Moreover, as the longitudinal velocity measurement is still difficult, this controller is developed without longitudinal velocity measurement. In addition, this new adaptive controller introduces a method to improve the control performance. The stability of the closed‐loop system is presented using the direct Lyapunov method. Finally, numerous simulations verify the effectiveness of the new controller.     

含有驱动器模型的移动机器人自适应跟踪控制

本文针对包含驱动器模型的移动机器人, 考虑到其在粗糙表面上运动过程中所受的摩擦力以及不可建模的动态的影响, 使用反步设计法(Backstepping)给出了一种自适应跟踪控制策略.其中对于不可建模的动态, 本文使用一种非线性函数对其影响进行抵消,使得机器人的路径跟踪对不确定具有鲁棒性; 对于摩擦力项, 使用径向基神经网络(RBFNN)对其进行逼近, 在控制器中能够根据逼近值给予相应的摩擦力补偿量, 从而使移动机器人比较适合在粗糙度大的路面(如沙地)上进行路径跟踪. 仿真结果验证了该控制方法的有效性.     

Robust neural network–based tracking control and stabilization of a wheeled mobile robot with input saturation

This paper presents a robust neural network–based control scheme to deal with the problem of tracking and stabilization simultaneously for a wheeled mobile robot subject to parametric uncertainties, external disturbances, and input saturation. At first, a new error‐state transformation scheme is designed by introducing some auxiliary variables as an additional virtual control signals to reduce the adverse effect caused by the underactuation. These variables can change their structures for different desired trajectories to be tracked. Then, a robust control law is proposed combining with a kinematic controller and a dynamic controller, while a three‐layer neural network system is applied to approximate model uncertainties. Stability analysis via the Lyapunov theory shows that the proposed controller can make tracking errors converge to bounded neighborhoods of the origin. Finally, some simulation results are illustrated to verify the effectiveness of the proposed control strategy.     

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.     

Robust adaptive neural network control for environmental boundary tracking by mobile robots

The paper addresses the problem of environmental boundary tracking for the nonholonomic mobile robot with uncertain dynamics and external disturbances. To do environmental boundary tracking, a reference velocity is designed for the nonholonomic mobile robot. In this paper, a radial basis function neural network (NN) is used to approximate a nonlinear function containing the uncertain model terms and the elements of the Hessian matrix of the environmental concentration function. Then, the NN approximator is combined with a robust control to construct a robust adaptive NN control for the mobile robot to track the desired environment boundary. It is proved that the tracking error can be guaranteed to converge to zero in the ultimate. Simulation results are presented to illustrate the stability of the robust adaptive control. Copyright © 2011 John Wiley & Sons, Ltd.     

采用惯性测量单元的移动机器人轨迹跟踪方法研究

对于非完整移动机器人的轨迹跟踪控制已有很多方法提出,但是这些方法或者是基于动力学模型或者是采用复杂的运动学模型,对于缺少强大计算设备且需要实时控制的工程应用是不适合的.本文针对非完整移动机器人提出了一种基于比例微分(proportional-differential,PD)控制器的实时轨迹跟踪控制方法.该方法运行在40 MHz的嵌入式控制器上的控制周期只有1～2 ms.通过将一个用于直流电机控制的非线性PID速度控制器与提出的轨迹控制器进行集成,实现了一个轮式移动机器人的运动控制.机器人轨迹跟踪实验系统中采用微机电系统(micro electro-mechanical system,MEMS)惯性测量单元检测轮式移动机器人的偏航角,实验结果验证了提出方法的有效性.     

基于T-S模型的轮式移动机器人轨迹跟踪控制

针对带有执行机构饱和约束与外部干扰的轮式移动机器人,提出了一种基于T-S模糊模型的轨迹跟踪方法.利用机器人运动特性和参考轨迹建立轨迹跟踪的误差系统并将其作T-S模型描述.通过求解具有LMI约束的半定规划问题,对每个线性子系统单独设计满足控制约束与H∞性能约束的状态反馈控制器,并在PDC(动态平行分配补偿)设计框架下构建全局控制器,最后证明闭环系统的李雅普诺夫稳定性.仿真结果验证了该方法的有效性和可行性.     

机械臂轨迹跟踪控制--基于EC-RBF神经网络的机械臂模型参考自适应控制

针对机械臂运动轨迹控制中存在的跟踪精度不高的问题,采用了一种基于EC-RBF神经网络的模型参考自适应控制方案对机械臂进行模型辨识与轨迹跟踪控制。该方案采用了两个RBF神经网络,运用EC-RBF学习算法,采用离线与在线相结合的方法来训练神经网络,一个用来实现对机械臂进行模型辨识,一个用来实现对机械臂轨迹跟踪控制。对二自由度机械臂进行仿真,结果表明,使用该控制方案对机械臂进行轨迹跟踪控制具有较高的控制精度,且因采用EC-RBF学习算法使网络具有更快的训练速度,从而使得控制过程较迅速。     

A robust adaptive fuzzy variable structure tracking control for the wheeled mobile robot: Simulation and experimental results

In this paper an adaptive fuzzy variable structure control (kinematic control) integrated with a proportional plus derivative control (dynamic control) is proposed as a robust solution to the trajectory tracking control problem for a differential wheeled mobile robot. The variable structure controller, based on the sliding mode theory, is a well known, proven control method, fit to deal with uncertainties and disturbances (e.g., structural and parameter uncertainties, external disturbances and operating limitations). To minimize the problems found in practical implementations of the classical variable structure controllers, an adaptive fuzzy logic controller replaces the discontinuous portion of the control signals (avoiding the chattering), causing the loss of invariance, but still ensuring the robustness to uncertainties and disturbances without having any a priori knowledge of their boundaries. Moreover, the adaptive fuzzy logic controller is a feasible tool to approximate any real continuous nonlinear system to arbitrary accuracy, and has a simple structure by using triangular membership functions, a low number of rules that must be evaluated, resulting in a lower computational load for execution, making it feasible for real time implementation. Stability analysis and the convergence of tracking errors as well as the adaptation laws are guaranteed with basis on the Lyapunov theory. Simulation and experimental results are explored to show the verification and validation of the proposed control strategy.     

提高轮式移动机器人性能的AKF 和滑模相结合控制方法

针对轮式移动机器人在实际工作中不可避免地受到环境因素影响的问题,采用Sage-Husa自适应卡尔曼滤波对带有白噪声的参考轨迹进行估计,以提高测量信息的真实性;同时在速度控制的基础上,考虑机器人动力学模型及其外界干扰,利用滑模控制思想设计出具有渐近收敛性的力矩反馈控制规律来跟踪滤波后的估计值.仿真结果表明,该控制方法能有效抑制测量噪声和外界干扰的影响,快速跟踪任意参考轨迹.