三轮驱动移动机器人轨迹跟踪控制

针对三轮驱动移动机器人在轨迹跟踪控制过程中运动不平滑的问题,建立了移动机器人在一定运动约束条件下的运动学模型。根据移动机器人位姿误差微分方程的描述,设计了基于后退时变状态反馈方法的移动机器人轨迹跟踪控制器。基于李雅普诺夫方法,对轨迹跟踪控制器的稳定性进行了分析,证明了该控制器能够保证闭环系统全局一致渐进稳定。仿真结果验证了运动学模型的正确性,以及轨迹跟踪控制器的有效性。     

基于预测控制的非连续路段下移动机器人的轨迹跟踪

针对非连续路段下的轨迹跟踪问题,设计了基于观测型的预测控制器。首先建立了移动机器人的运动学模型,根据机器人的运动学模型得出了其位姿误差微分方程;然后在轨迹跟踪问题的基础上,设计了系统的观测模型,通过将预测控制器与系统的观测模型结合,设计了观测型预测控制器;最后再MATLAB环境下,利用本文所设计的控制器对移动机器人在非连续路段下的轨迹跟踪问题进行仿真,并将仿真结果与PID控制器控制的仿真结果进行对比,由仿真结果可以看出,本文所设计的控制器具有很好的鲁棒性、快速性及稳定性,可适用于移动机器人的轨迹跟踪的研究。     

一种改进的移动机器人轨迹跟踪迭代学习方法

通过对轮式移动机器人轨迹跟踪优化问题的研究,提出了一种适应性强、收敛速度快且跟踪误差小的迭代滤波学习控制方法,充分发挥了迭代学习控制和Kalman滤波算法的优势,通过引入状态补偿项和设计新的迭代学习增益矩阵对迭代学习律进行了改进。改进的迭代学习控制能够更快速、更精确、更有效地跟踪期望的圆轨迹。采用离散的Kalman滤波器对干扰和噪声进行滤波,抑制了干扰和噪声对轨迹跟踪的影响,使该控制算法更适合于工程应用。计算机实验和仿真表明该方法具有较好的轨迹跟踪能力。     

基于Lyapunov方法和快速终端滑模的轨迹跟踪控制

针对移动机器人的运动学模型,提出一种具有全局渐近稳定性的跟踪控制器。该跟踪控制器的设计分为两部分：第一部分是采用全局快速终端滑动模态的思想设计了角速度的控制律,用来渐近镇定移动机器人跟踪的前向角误差;第二部分是采用Lyapunov方法设计了线速度的控制律,用来渐近镇定移动机器人跟踪的平面坐标误差。采用Lyapunov稳定性定理,证明了移动机器人在满足这些控制律条件下,实现了对参考轨迹的全局渐近跟踪。实验结果表明移动机器人能够有效地跟踪期望轨迹,有利于在实际应用中推广。     

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.     

基于惯性测量单元的轮式机器人轨迹跟踪仿真

针对轮式机器人轨迹跟踪质量下降问题,提出基于惯性测量单元(IMU)的轮式移动机器人轨迹跟踪控制方法.通过两个惯性测量单元(IMU)来建立轮式机器人的速度、角速度计算模型,针对轮式移动机器人执行中的差动命令对轮式机器人实施闭环控制;设计轮式机器人工作轨迹参考量,结合动力学模型提出轮式机器人轨迹跟踪控制器,使轮式机器人可以在指定的轨迹上进行跟踪.实验结果表明,提出的方法能够使轮式移动机器人的轨迹跟踪误差更小,跟踪结果更精准.     

基于预测控制的全向移动机器人轨迹跟踪

针对全向移动机器人的轨迹跟踪控制问题,在分析与建立全向移动机器人运动学与动力学模型的基础上,研究了基于模型预测控制的移动机器人轨迹跟踪方法;通过对误差模型的线性化描述,并将目标函数以二次项部分与线性部分构成,在满足控制约束的条件下,将采样周期内最小化目标甬数的优化问题转换为二次规划问题的求解;当取预测时域N=5时,机器人在x和y方向的误差范围分别为±5.4%和±4.7%,并在轨迹曲线中曲率半径较小处出现较为明显的抖动,最后对误差产生原因进行了分析与总结,结果表明该方法对全向移动机器人的轨迹跟踪控制是有效可行的.     

基于模糊CMAC的移动机器人轨迹跟踪控制

针对受非完整约束的移动机器人的轨迹跟踪问题,提出了一种基于模糊CMAC的轨迹跟踪控制策略。该策略利用模糊CMAC神经网络逼近移动机器人动力学模型的非线性和不确定,同时与速度误差结合起来构成力矩控制器,并用滑模项来补偿不确定性扰动对系统的影响。李亚普诺夫稳定性定理保证了系统的稳定性和跟踪误差的渐近收敛,仿真结果进一步验证了所提方法的有效性。     

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.     

基于视觉图像的全向移动机器人轨迹跟踪控制研究

机器人轨迹节点跟踪比较难,导致机器人实际轨迹偏离期望轨迹,所以设计基于视觉图像的全向移动机器人轨迹跟踪控制方法。以滑模变结构控制闭环为基础,求解运动学模型、动力学模型表达式,实现对机器人移动轨迹数学模型的构建。按照视觉图像划分标准,完成对全向移动机器人运动图像的分割,通过分离目标节点的方式提取运动学特征参量,完成机器人轨迹节点跟踪处理。设置前馈控制器与扰动观测器,根据运动学不等式条件计算误差向量指标的取值范围,根据该值对主机元件的控制作用能力进行调节,实现全向移动机器人轨迹跟踪控制。对比实验结果表明,所设计的方法应用后,全向移动机器人角速度曲线、线速度曲线与期望运动轨迹曲线之间的贴合程度均超过90%,满足全向移动机器人轨迹跟踪控制要求。     

模糊PID控制下移动机器人定位方法研究

机器人定位研究一直是机器人学研究的重点,但目前机器人定位方法都存在缺点,抗干扰能力差,不能做到准确定位,主要是由于环境等多方面因素的干扰,定位误差会逐渐加大;由于上述原因,提出了一种基于设定值加权模糊PID控制的移动机器人自定位方法;给出了定位过程的参数,为机器人移动建立模型,设计一种模糊 PID 控制器,根据误差及变化率大小,选择模糊定位或PID定位,实现移动机器人的智能定位,提高机器人定位准确的准确性;通过仿真实验结果证明:模糊PID控制的机器人自定位方法对移动机器人的定位过程有较好的改善作用,实用效果较好。     

基于Fuzzy-PID的移动机器人运动控制

移动机器人涉及到许多研究方向，运动控制是其中的基础。通过对移动机器人运动学模型进行分析，以足球机器人系统为实验平台，论证了Fuzzy-PID技术应用于移动机器人运动控制的可行性。将传统的PID控制与模糊控制相结合，通过PID控制实现控制的准确性，利用模糊控制提高控制的快速性。针对移动机器人运动控制中的实际问题，着重提出了基于误差分区的PID控制器和模糊控制器的设计方法。实验证明该方法不仅增强了控制器的调节能力，还在一定程度上简化了控制器的设计。     

Hybrid fuzzy control of robotics systems

This paper presents a new approach towards optimal design of a hybrid fuzzy controller for robotics systems. The salient feature of the proposed approach is that it combines the fuzzy gain scheduling method and a fuzzy proportional-integral-derivative (PID) controller to solve the nonlinear control problem. The resultant fuzzy rule base of the proposed controller can be decomposed into two layers. In the upper layer, the gain scheduling method is incorporated with a Takagi-Sugeno (TS) fuzzy logic controller to linearize the robotics system for a given reference trajectory. In the lower layer, a fuzzy PID controller is derived for all the locally linearized systems by replacing the conventional PI controller by a linear fuzzy logic controller, which has different gains for different linearization conditions. Within the guaranteed stability region, the controller gains can be optimally tuned by genetic algorithms. Simulation studies on a pole balancing robot and a multilink robot manipulator demonstrate the effectiveness and robustness of the proposed approach.     

非线性系统的模糊免疫PSD控制与仿真

针对模糊免疫PID控制算法中微分与积分增益不能根据系统特性自动调整的问题,提出了一种模糊免疫PSD(Proportional Summation Derivative)控制算法。该方法将自适应PSD算法与模糊免疫PID算法相结合,利用自适应PSD控制算法根据过程误差的几何特性建立的PSD控制规律,使得模糊免疫PID控制算法中的微分和积分增益可以随比例增益的变化而自适应调整,从而进一步提高控制算法的自适应性能。仿真实验表明,采用该算法可以提高非线性、时变系统的控制性能,并能减少参数调整的工作量。     

机器人全局PID模糊滑模跟踪控制与仿真研究

为解决机器人跟踪控制过程中采用PID控制算法会出现抖动和误差的问题,提出了一种机器人全局PID模糊滑模跟踪控制算法.通过将PID滑模控制和模糊控制相结合,设计了全局PID模糊滑模控制;基于模糊规则,对滑模控制增益进行自适应调整,从而消除建模误差和干扰,削弱了控制时产生的抖振,在线调整控制器参数和估计误差,并通过积分来消除外界干扰,因此提高了控制精度.仿真结果表明,与常规的PID算法相比,该方法在处理控制抖动和消除误差以及干扰方面具有极高的鲁棒性.     

Locomotion Control of a Hydraulically Actuated Hexapod Robot by Robust Adaptive Fuzzy Control with Self-Tuned Adaptation Gain and Dead Zone Fuzzy Pre-compensation

Hydraulically actuated robotic mechanisms are becoming popular for field robotic applications for their compact design and large output power. However, they exhibit nonlinearity, parameter variation and flattery delay in the response. This flattery delay, which often causes poor trajectory tracking performance of the robot, is possibly caused by the dead zone of the proportional electromagnetic control valves and the delay associated with oil flow. In this investigation, we have proposed a trajectory tracking control system for hydraulically actuated robotic mechanism that diminishes the flattery delay in the output response. The proposed controller consists of a robust adaptive fuzzy controller with self-tuned adaptation gain in the feedback loop to cope with the parameter variation and disturbances and a one-step-ahead fuzzy controller in the feed-forward loop for hydraulic dead zone pre-compensation. The adaptation law of the feedback controller has been designed by Lyapunov synthesis method and its adaptation rate is varied by fuzzy self-tuning. The variable adaptation rate helps to improve the tracking performance without sacrificing the stability. The proposed control technique has been applied for locomotion control of a hydraulically actuated hexapod robot under independent joint control framework. For tracking performance of the proposed controller has also been compared with classical PID controller, LQG state feedback controller and static fuzzy controller. The experimental results exhibit a very accurate foot trajectory tracking with very small tracking error with the proposed controller.     

Wavenet fuzzy PID controller for nonlinear MIMO systems: Experimental validation on a high-end haptic robotic interface

A novel global PID control scheme for nonlinear MIMO systems is proposed and implemented for a robot as study case, this scheme is called AWFPID from its adaptive wavelet fuzzy PID control structure. Basically, it identifies inverse error dynamics using a radial basis neural network with daughter RASP1 wavelets activation function; its output is in cascaded with an infinite impulse response (IIR) filter to prune irrelevant signals and nodes as well as to recover a canonical form. Then, online adaptive fuzzy tuning of a discrete PID regulator is proposed, whose closed-loop guarantees global regulation for nonlinear dynamical plants. The wavelet network includes a fuzzy inference system for online tuning of learning rates. A real-time experimental study on a three degrees of freedom haptic interface, the PHANToM Premium 1.0A, highlights the regulation with smooth control effort without using the mathematical model of the robot.     

Design of a PID optimized neural networks and PD fuzzy logic controllers for a two‐wheeled mobile robot

In this paper, two intelligent techniques for a two‐wheeled differential mobile robot are designed and presented: A smart PID optimized neural networks based controller (SNNPIDC) and a PD fuzzy logic controller (PDFLC). Basically, mobile robots are required to work and navigate under exigent circumstances where the environment is hostile, full of disturbances such as holes and stones. The robot navigation leads to an autonomous decision making to overcome an obstacle and/or to stop the engine to protect it. In fact, the actuators that drive the robot should in no way be damaged and should stop to change direction in case of insurmountable disturbances. In this context, two controllers are implemented and a comparative study is carried out to demonstrate the effectiveness of the proposed approaches. For the first one, neural networks are used to optimize the parameters of a PID controller and for the second a fuzzy inference system type Mamdani based controller is adopted. The goal is to implement control algorithms for safe robot navigation while avoiding damage to the motors. In these two control cases, the smart robot has to quickly perform tasks and adapt to changing environment conditions while ensuring stability and accuracy and must be autonomous with regards to decision making. Simulations results aren't done in real environments, but are obtained with the Matlab/Simulink environment in which holes and stones are modeled by different load torques and are applied as disturbances on the mobile robot environment. These simulation results and the robot performances are satisfactory and are compared to a PID controller in which parameters are tuned by the Ziegler–Nichols tuning method. The applied methods have proven to be highly robust.     

Fuzzy PID Control of a Five DOF Robot Arm

This paper studies the application of fuzzy logic control on a five degrees of freedom (DOF) robot arm, the Maker 100 of U.S. Robots. The elaboration of the fuzzy control laws is based on two structures of coupled rules fuzzy PID controllers. The fuzzy PID controllers are numerically simulated and the simulation results confirm the success of the fuzzy PID control in trajectory tracking problems. Seeking a performance optimization, a systematic study of the choice of tuning parameters of the controllers is done. The success of the proposed fuzzy control law is again affirmed by a comparative evaluation with respect to the computed torque control method and the direct adaptive control method, the last two controls being also numerically implemented using the same dynamic model of the robot arm.     

自主移动机器人巡线控制研究

针对自主移动机器人传统巡线控制中存在的不足,在使用灰度传感器采集地面轨迹信息的同时,引入角度传感器对行进方向的角度信息进行采集,由此克服了传统巡线控制中单一传感器采集信息不全的缺点;设计了PID控制加模糊控制的复合控制器,并给出复合控制器算法.在此基础上建立实验系统,通过对其进行仿真,结果证明该控制策略不仅适于自主移动...