A Neuro-interface with fuzzy compensator for controlling nonholonomic mobile robots

This paper describes a control method for mobile robots represented by a nonlinear dynamical system, which is subjected to an output deviation caused by drastically changed disturbances. We here propose some controllers in the framework of neuro-interface. It is assumed that a neural network (NN)-based feedforward controller is construcetd by following the concept of virtual master-slave robot, in which a virtual master robot as a feedforward controller is used to control the slave (i.e., actual) robot. The whole system of the present neuro-interface consists of an NN-based feedforward controller, a feedback PD controller and an adaptive fuzzy feedback compensator. The NN-based feedforward controller is trained offline by using a gradient method, the gains of the PD controller are to be chosen constant, and the adaptive fuzzy compensator is constructed with a simplified fuzzy reasoning. Some simulations are presented to confirm the validity of the present approach, where a nonholonomic mobile robot with two independent driving wheels is assmued to have a disturbance due to the change of mass for the robot.     

含有摩擦补偿的全方位移动机器人自抗扰控制

本文针对全方位移动机器人轨迹追踪中的摩擦补偿问题,提出了一种改进的非线性自抗扰控制器.首先建立了含有经典静态摩擦模型的全方位移动机器人动力学模型.其次,基于该模型设计非线性控制器和线性扩张状态观测器并给出了系统的稳定性分析.通过将模型已知项加入线性扩张状态观测器中得到摩擦力的估计值,并将估计值用于非线性控制器中摩擦补偿部分.为减小摩擦力对机器人低速运动轨迹追踪控制的影响,非线性控制器采用变增益控制器进行轨迹追踪控制.最后通过仿真结果验证本文提出控制器的有效性.     

Mobile robot navigation in a partially structured static environment, using neural predictive control

This paper presents a way of implementing a model-based predictive controller (MBPC) for mobile robot navigation when unexpected static obstacles are present in the robot environment. The method uses a nonlinear model of mobile robot dynamics, and thus allows an accurate prediction of the future trajectories. An ultrasonic ranging system has been used for obstacle detection. A multilayer perceptron is used to implement the MBPC, allowing real-time implementation and also eliminating the need for high-level data sensor processing. The perceptron has been trained in a supervised manner to reproduce the MBPC behaviour. Experimental results obtained when applying the neural-network controller to a TRC Labmate mobile robot are given in the paper.     

Application of a hybrid controller to a mobile robot

This paper presents the application of a hybrid controller to the optimization of the movement of a mobile robot. Through hybrid controller processes, the optimal angle and velocity of a robot moving in a work space was determined. More effective movement resulted from these hybrid controller processes. The experimental scenarios involved a five-versus-five soccer game and a MATLAB simulation, where the proposed system dynamically assigned the robot to the target position. The hybrid controller was able to choose a better position according to the circumstances encountered. The hybrid controller that is proposed includes a support vector machine and a fuzzy logic controller. We used the method of generalized predictive control to predict the target position, and the support vector machine to determine the optimal angle and velocity required for the mobile robot to reach the goal. First, we used the generalized predictive control to predict the target position. Then, the support vector machine is used to classify the angle that must be followed by the mobile robot to reach the goal. Next, a fuzzy logic controller is designed to determine the velocity of the left and right wheels of the mobile robot. Thus generated, the velocity was optimized according to the measures obtained by the support vector machine. Finally, based on the optimal velocity of robot, the output membership function was modified. Consequently, the proposed hybrid controller allowed the robot to reach the goal quickly and effectively.     

A Smith-predictor-based generalised predictive controller for mobile robot path-tracking

This paper shows how to improve the robustness of mobile robot path tracking when predictive control algorithms are used. When uncertainties in a mobile robot are considered, it is shown how the classical generalised predictive controller (GPC) may lead to an unstable behaviour of the mobile robot. The equivalence between the GPC and a structure composed by an optimal predictor and a classical controller is given, and it is shown that use of a Smith predictor instead of an optimal predictor increases the robustness of the system. A new strategy, the Smith-predictor-based GPC, is proposed. Experimental tests carried out on a LABMATE mobile robot validate the performance of the proposed controller.     

Trajectory tracking control of mobile robots without using longitudinal velocity measurements

We propose a new robust trajectory tracking control scheme for wheeled mobile robots without longitudinal velocity measurements. In the proposed controller, a velocity observer is used to estimate the longitudinal velocity of a wheeled mobile robot. A wheeled mobile robot model, including motor dynamics, is used to develop the controller. The developed controller has the following useful properties. (1) The developed controller does not require any accurate knowledge of the robot parameters or the motor parameters. Even if there are uncertainties in the robot dynamics, including the motor properties, it is certain that tracking errors ultimately become uniformly bounded in a closed-loop system using the developed controller. (2) It is shown theoretically that the ultimate norms of tracking errors can easily be reduced by setting only one design parameter.     

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.     

非完整移动机器人全局路径跟踪控制

根据制导路径跟踪理论,提出了一种非完整移动机器人全局路径跟踪控制方法.这一方法首先在路径坐标系上计算实际位置与期望位置的误差,利用制导的路径跟踪理论,导出消除该误差所需的姿态角和路径参数更新律,然后据此求解角速度及实际控制.文中还给出了初始路径参考点的计算方法,分析了路径跟踪方向和反转方法.稳定性分析证明该方法没有控制奇异点,受控闭环系统全局一致渐近稳定.最后通过移动机器人典型路径跟踪实验验证了所提出方法的可行性.     

一种基于总线的移动探测机器人控制系统设计与实现

针对机械臂定位相关的控制算法缺乏合适验证平台的问题,将移动机器人技术和机械臂控制技术相结合,在分布式控制系统结构的基础上设计并实现了移动探测机器人原型样机。该系统由一个中央主控单元和多个从控制单元构成,单元之间通过总线相连,以实现探测机械臂各部分的模块化。该控制系统已经成功应用于实验室的探测机器人中。该设计能够完成对机械臂运动控制任务,可以用于移动探测机器人平台的建立以及相关算法的实现。     

非完整移动机器人的反步跟踪控制方法

移动机器人是典型的非完整系统,对其进行运动控制是一个热点且是一项具有挑战性的工作.目前的大部分研究都是基于运动学模型的,而在实际应用中,动力学特性不可忽视.针对轨迹跟踪这一典型控制任务,文中对一种轮式移动机器人进行了动力学建模.利用反步技术,提出了一种控制结构,综合了速度控制器和力矩控制器.该控制律不但能够得到稳定跟踪所需要的速度,而且同时能够计算出驱动移动机器人行进的电机力矩.仿真结果表明,该控制律可以良好地工作.控制结构的通用性保证了其他控制方法拓展的可能性.     

未知环境中移动机器人实时导航与避障的分层模糊控制

为了解决单模糊控制器的“规则库爆炸”问题,设计了一种分层的模糊控制器,用于指导移动机器人通过未知环境到达指定的目标点．控制器根据8个超声传感器的信息和目标相对于机器人的方位确定机器人的运动．首先,每个超声传感器的信息被输入到危险度模糊控制器（DFC）中,产生关于周围环境中障碍物危险度的模糊向量．这些模糊向量经过融合与归一化处理后分别输入到上层的速度模糊控制器（VFC）和角速度模糊控制器（RFC）的推理机中．VFC根据目标的距离和障碍物的危险度控制机器人的前进速度．RFC根据目标的方向和障碍物的危险度控制机器人的转向,并采用最大隶属度法的反模糊化策略解决“对称不确定”问题．仿真与实验结果证明了所设计的模糊控制器简单而有效．     

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。     

An embedded fuzzy controller for a behavior-based mobile robot with guaranteed performance

In this paper, an embedded fuzzy controller for a nonholonomic mobile robot is developed. The mobile robot was built based on the behavior-based artificial intelligence, where several levels of competences and behaviors are implemented. A class of fuzzy control laws is formulated using the Lyapunov's direct method, which can guarantee the convergence of the steering errors. Theoretical analysis of the fuzzy control algorithms for steering control of the mobile robot is performed. The requirements for a suitable rule base selection in the proposed fuzzy controller are provided, which can guarantee the asymptotical stability of the system. Simulation and experimental studies are conducted to investigate the performance of the proposed fuzzy controller. It can achieve the desired turn angle and make the mobile robot follow the target trajectory satisfactorily.     

基于自适应和神经动力学的轮式移动机器人路径跟踪控制(英文)

本文提出一种自适应和神经动力学相结合的轮式移动机器人路径跟踪控制方法.首先,设计运动学控制器用来获得机器人期望速度;其次,考虑机器人动力学模型参数的不确定性,利用模型参考自适应方法来设计动力学控制规律,使得机器人实际速度渐近逼近期望值;再次,为克服速度和力矩的跳变,加入神经动力学模型对控制器进行优化,并且通过Lypunov理论来证明整个控制系统的稳定性;最后仿真结果表明该控制方法的有效性.     

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.􀁱     

轮式机器人滑模轨迹跟踪控制器设计

轮式机器人是一个典型的非完整性系统。由于非线性和非完整特性,很难为移动机器人系统的轨迹跟踪建立一个合适的模型。介绍了一种轮式机器人滑模轨迹跟踪控制方法。滑模控制是一个鲁棒的控制方法,能渐近的按一条所期望的轨迹稳定移动机器人。以之为基础,描述了轮式机器人的动力学模型并在二维坐标下建立了运动学方程,根据运动学方程设计滑模控制器,该控制器使得机器人的位置误差收敛到零。     

Design of a sliding mode controller for an automatic guided vehicle and its implementation

In this paper, a control scheme that combines a kinematic controller and a sliding mode dynamic controller with external disturbances is proposed for an automatic guided vehicle to track a desired trajectory with a specified constant velocity. It provides a method of taking into account specific mobile robot dynamics to convert desired velocity control inputs into torques for the actual mobile robot. First, velocity control inputs are designed for the kinematic controller to make the tracking error vector asymptotically stable. Then, a sliding mode dynamic controller is designed such that the mobile robot’s velocities converge to the velocity control inputs. The control law is obtained based on the backstepping technique. System stability is proved using the Lyapunov stability theory. In addition, a scheme for measuring the errors using a USB camera is described. The simulation and experimental results are presented to illustrate the effectiveness of the proposed controller.     

A higher level path tracking controller for a four-wheel differentially steered mobile robot

A variety of approaches for path tracking control of wheeled mobile robots have been implemented. While most of these are based on controlling the robot dynamics, they are not applicable if the robot dynamics are inaccessible. In this paper, a fuzzy logic controller (FLC) for the path tracking of a wheeled mobile robot based on controlling the robot at a higher level is presented. The controller is highly robust and flexible and automatically follows a sequence of discrete waypoints, and no interpolation of the waypoints is needed to generate a continuous reference trajectory. The speeds are varied depending on the variations in the path and on the posture of the robot. The heuristic rules of the FLC are based on an analogy with a human driving a car and the optimization of the controller is based on experimentation. The implementation on a P3-AT mobile robot shows the effectiveness of the proposed approach.     

移动机器人网络控制系统的输出反馈控制

以线性时不变系统为被控对象,建立了四轮移动机器人网络控制系统的离散数学模型。诱导时延是影响系统性能的关键因素,通过在节点中设置缓冲区的方法可以将网络控制系统中的随机诱导时延转化为确定性时延,从而将网络控制系统由随机系统转化为确定性系统。通过被控对象移动机器人控制实验系统,设计了一个能处理网络诱导时延的输出反馈控制器,分析了采样周期和网络诱导时延对网络控制系统稳定性的影响。仿真结果表明了该控制器和控制策略的正确性及有效性。     

Hybrid stabilizing control on a real mobile robot

To establish empirical verification of a stabilizing controller for nonholonomic systems, the authors implement a hybrid control concept on a 2-DOF mobile robot. Practical issues of velocity control are also addressed through a velocity controller which transforms the mobile robot to a new system with linear and angular velocity inputs. Experiments in the physical meaning of different controller components provide insights which result in significant improvements in controller performance