Vision-based Stabilization of Nonholonomic Mobile Robots by Integrating Sliding-mode Control and Adaptive Approach

Vision-based pose stabilization of nonholonomic mobile robots has received extensive attention. At present, most of the solutions of the problem do not take the robot dynamics into account in the controller design, so that these controllers are difficult to realize satisfactory control in practical application. Besides, many of the approaches suffer from the initial speed and torque jump which are not practical in the real world. Considering the kinematics and dynamics, a two-stage visual controller for solving the stabilization problem of a mobile robot is presented, applying the integration of adaptive control, sliding-mode control, and neural dynamics. In the first stage, an adaptive kinematic stabilization controller utilized to generate the command of velocity is developed based on Lyapunov theory. In the second stage, adopting the sliding-mode control approach, a dynamic controller with a variable speed function used to reduce the chattering is designed, which is utilized to generate the command of torque to make the actual velocity of the mobile robot asymptotically reach the desired velocity. Furthermore, to handle the speed and torque jump problems, the neural dynamics model is integrated into the above mentioned controllers. The stability of the proposed control system is analyzed by using Lyapunov theory. Finally, the simulation of the control law is implemented in perturbed case, and the results show that the control scheme can solve the stabilization problem effectively. The proposed control law can solve the speed and torque jump problems, overcome external disturbances, and provide a new solution for the vision-based stabilization of the mobile robot.

A New Noise-Tolerant Dual-Neural-Network Scheme for Robust Kinematic Control of Robotic Arms With Unknown Models

Taking advantage of their inherent dexterity,robotic arms are competent in completing many tasks efficiently.As a result of the modeling complexity and kinematic uncertainty of robotic arms,model-free control paradigm has been proposed and investigated extensively.However,robust model-free control of robotic arms in the presence of noise interference remains a problem worth studying.In this paper,we first propose a new kind of zeroing neural network(ZNN),i.e.,integration-enhanced noise-tolerant ZNN(IENT-ZNN)with integration-enhanced noisetolerant capability.Then,a unified dual IENT-ZNN scheme based on the proposed IENT-ZNN is presented for the kinematic control problem of both rigid-link and continuum robotic arms,which improves the performance of robotic arms with the disturbance of noise,without knowing the structural parameters of the robotic arms.The finite-time convergence and robustness of the proposed control scheme are proven by theoretical analysis.Finally,simulation studies and experimental demonstrations verify that the proposed control scheme is feasible in the kinematic control of different robotic arms and can achieve better results in terms of accuracy and robustness.

基于磁驱动正交悬臂探针的三维原子力显微镜技术开发

本文介绍了一种基于磁驱动正交悬臂探针(magnetically driven-orthogonal cantilever probes,MDOCP)的三维原子力显微镜(three-dimensional atomic force microscopy,3D-AFM)表征方法,该方法采用两个独立的三自由度纳米扫描器,能够实现探针沿可控矢量角度跟踪扫描样品表面。该3D-AFM系统还配备了高精度旋转台,可实现360°全向成像。定制的MD-OCP包含水平悬臂、垂直悬臂和磁球三部分,其中磁球可在磁场中机械驱动OCP实现激振。垂直悬臂具有一个突出的尖端,可检测深槽和具有悬垂/凹边特征的结构。首先,对MD-OCP的设计、模拟、制造和性能分析进行了描述;其次,详细介绍了探针振幅补偿和360°旋转原点定位的方法。通过使用标准AFM阶梯光栅进行对比实验,验证了所提出方法对于陡峭侧壁和拐角处细节的表征能力,其中采用了三维地形重建方法将图像整合。通过对具有微梳结构的微机电系统(MEMS)器件进行3D表征,进一步证实了所提出基于MD-OCP的3D-AFM技术的有效性。最后,该技术被用于确定微阵列芯片的关键尺寸(critical dimensions,CD)。实验结果表明,所提出的方法可以高精度地获取三维结构的CD信息,相比于难以获得侧壁信息的二维技术,在三维微纳制造检测领域具有更好的潜力。

A Braille Reading System Based on Electrotactile Display With Flexible Electrode Array

Dear editor,Due to visual impairment and lack of social attention,the reading problem of the blind has not been well resolved.Many existing Braille reading devices still have many problems and are not accepted by the public.This paper developed a portable Braille reading system based on electrotactile display technology.The proposed system is composed of a six-channel electrotactile stimulator,a flexible electrode array for Braille display and a graphical user interface(GUI)for monitoring and control.Based on the flexibility of the system,two Braille reading strategies have been designed:spatial mode and sequential mode.The system was preliminary tested by six sighted subjects.The results showed that subjects were able to recognize Braille characters with more than 80%accuracy within less than 10 seconds for the initial use.Compared to existing Braille reading systems,this system is portable,wearable and flexible in control.

Research on point stabilization of a wheeled mobile robot using fuzzy control optimized by GA

A point stabilization scheme of a wheeled mobile robot （WMR） which moves on uneven surface is presented by using tuzzy control. Taking the kinematics and dynamics of the vehicle into account, the fuzzy controller is employed to regulate the robot based on a kinematic nonlinear state feedback control law. Herein, the fuzzy strategy is composed of two velocity control laws which are used to adjust the speed and angular velocity, respectively. Subsequently, genetic algorithm （GA） is applied to optimize the controller parameters. Through the self-optimization, a group of optimum parameters is gotten. Simulation results are presented to show the effectiveness of the control strategy.