A Novel Disturbance Observer Based Fixed-Time Sliding Mode Control for Robotic Manipulators With Global Fast Convergence

This paper proposes a new global fixed-time sliding mode control strategy for the trajectory tracking control of uncertain robotic manipulators.First,a fixed-time disturbance observer(FTDO) is designed to deal with the adverse effects of model uncertainties and external disturbances in the manipulator systems.Then an adaptive scheme is used and the adaptive FTDO(AFTDO) is developed,so that the priori knowledge of the lumped disturbance is not required.Further,a new non-singular fast terminal sliding mode(NFTSM) surface is designed by using an arctan function,which helps to overcome the singularity problem and enhance the robustness of the system.Based on the estimation of the lumped disturbance by the AFTDO,a fixed-time non-singular fast terminal sliding mode controller(FTNFTSMC)is developed to guarantee the trajectory tracking errors converge to zero within a fixed time.The settling time is independent of the initial state of the system.In addition,the stability of the AFTDO and FTNFTSMC is strictly proved by using Lyapunov method.Finally,the fixed-time NFESM(FTNFTSM) algorithm is validated on a 2-link manipulator and comparisons with other existing sliding mode controllers(SMCs) are performed.The comparative results confirm that the FTNFTSMC has superior control performance.

Manipulator tracking technology based on FSRUKF

Aiming at the shortcoming that the traditional industrial manipulator using off-line programming cannot change along with the change of external environment,the key technologies such as machine vision and manipulator control are studied,and a complete manipulator vision tracking system is designed.Firstly,Denavit-Hartenberg(D-H)parameters method is used to construct the model of the manipulator and analyze the forward and inverse kinematics equations of the manipulator.At the same time,a binocular camera is used to obtain the threedimensional position of the target.Secondly,in order to make the manipulator track the target more accurately,the fuzzy adaptive square root unscented Kalman filter(FSRUKF)is proposed to estimate the target state.Finally,the manipulator tracking system is built by using the position-based visual servo.The simulation experiments show that FSRUKF converges faster and with less error than the square root unscented Kalman filter(SRUKF),which meets the application requirements of the manipulator tracking system,and basically meets the application requirements of the manipulator tracking system in the practical experiments.

Optimal Joint Space Control of a Cable-Driven Aerial Manipulator

This article proposes a novel method for maintaining the trajectory of an aerial manipulator by utilizing a fast nonsingular terminal sliding mode(FNTSM)manifold and a linear extended state observer(LESO).The developed controlmethod applies an FNTSMto ensure the tracking performance’s control accuracy,and an LESO to estimate the system’s unmodeled dynamics and external disturbances.Additionally,an improved salp swarm algorithm(ISSA)is employed to parameter tune the suggested controller by integrating the salp swarmtechnique with a cloud model.This approach also uses a model-free scheme to reduce the complexity of controller design without relying on complex and precise dynamics models.The simulation results show that the proposed controller outperforms linear active rejection disturbance control and PID controllers in terms of transient performance and resilience against lumped disturbances,and the ISSA can help the proposed controller find optimal control parameters.

Observer-Based Control for a Cable-Driven Aerial Manipulator under Lumped Disturbances

With the increasing demand for interactive aerial operations,the application of aerial manipulators is becoming more promising.However,there are a few critical problems on how to improve the energetic efficiency and pose control of the aerialmanipulator forpractical application.In this paper,a novel cable-drivenaerialmanipulatorused for remote water sampling is proposed and then its rigid-flexible coupling dynamics model is constructed which takes joint flexibility into account.To achieve high precision joint position tracking under lumped disturbances,a newly controller,which consists of three parts:linear extended state observer,adaptive super-twisting strategy,and fractional-order nonsingular terminal sliding mode control,is proposed.The linear extended state observer is adopted to approximate unmeasured states and unknown lumped disturbances and achieve model-free control structure.The adaptive super-twisting strategy and fractional-order nonsingular terminal sliding mode control are combined together to achieve good control performance and counteract chattering problem.The Lyapunovmethod is utilized to prove the overall stability and convergence of the system.Lastly,various visualization simulations and ground experiments are conducted,verifying the effectiveness of our strategy,and all outcomes demonstrate its superiorities over the existing control strategies.

A Custom Manipulator for Dental Implantation Through Model-Based Design

This paper presents a Model-Based Design(MBD)approach for the design and control of a customized manipulator intended for drilling and position-ing of dental implants accurately with minimal human intervention.While performing an intra-oral surgery for a prolonged duration within a limited oral cavity,the tremor of dentist's hand is inevitable.As a result,wielding the drilling tool and inserting the dental implants safely in accurate position and orientation is highly challenging even for experienced dentists.Therefore,we introduce a customized manipulator that is designed ergonomically by taking in to account the dental chair specifications and anthropomorphic data such that it can be readily mounted onto the existing dental chair.The manipulator can be used to drill holes for dental inserts and position them with improved accuracy and safety.Further-more,a thorough multi-body motion analysis of the manipulator was carried out by creating a virtual prototype of the manipulator and simulating its controlled movements in various scenarios.The overall design was prepared and validated in simulation using Solid works,MATLAB and Simulink through Model Based Design(MBD)approach.The motion simulation results indicate that the manipulator could be built as a prototype readily.

Gravity-Based Kinetostatic Modeling of Parallel Manipulators Using Screw Theory

The pose accuracy of parallel manipulators(PMs)is a key index to measure their performance.Establishing the grav-ity-based kinetostatic model of a parallel robot provides an important basis for its error composition and accuracy improvement.In this paper,a kinetostatic modeling approach that takes real gravity distribution into consideration is proposed to analyze the influence of gravity on the infinitesimal twist and actuator forces of PMs.First,the duality of the twist screw and constraint wrenches are used to derive the gravity-attached constraint wrenches independent of the external load and the limb stiffness matrix corresponding to the kinematics-based constraint wrenches.Sec-ond,the gravity model of the mechanism is established based on the screw theory and the principle of virtual work.Finally,the analytical formulas of the infinitesimal twist and the actuator force of PMs are obtained,and the influences of the external load,platform gravity,and rod gravity on the stiffness of the mechanism are decoupled.The non-overconstrained 3RPS and overconstrained 2PRU-UPR PMs are taken as examples to verify the proposed method.This research proposes a methodology to analyze the infinitesimal deformation of the mechanism under the influence of gravity.

A Comparative Study on Kinematic Calibration for a 3-DOF Parallel Manipulator Using the Complete-Minimal,Inverse-Kinematic and Geometric-Constraint Error Models

Kinematic calibration is a reliable way to improve the accuracy of parallel manipulators, while the error model dramatically afects the accuracy, reliability, and stability of identifcation results. In this paper, a comparison study on kinematic calibration for a 3-DOF parallel manipulator with three error models is presented to investigate the relative merits of diferent error modeling methods. The study takes into consideration the inverse-kinematic error model, which ignores all passive joint errors, the geometric-constraint error model, which is derived by special geometric constraints of the studied RPR-equivalent parallel manipulator, and the complete-minimal error model, which meets the complete, minimal, and continuous criteria. This comparison focuses on aspects such as modeling complexity, identifcation accuracy, the impact of noise uncertainty, and parameter identifability. To facilitate a more intuitive comparison, simulations are conducted to draw conclusions in certain aspects, including accuracy, the infuence of the S joint, identifcation with noises, and sensitivity indices. The simulations indicate that the complete-minimal error model exhibits the lowest residual values, and all error models demonstrate stability considering noises. Hereafter, an experiment is conducted on a prototype using a laser tracker, providing further insights into the diferences among the three error models. The results show that the residual errors of this machine tool are signifcantly improved according to the identifed parameters, and the complete-minimal error model can approach the measurements by nearly 90% compared to the inverse-kinematic error model. The fndings pertaining to the model process, complexity, and limitations are also instructive for other parallel manipulators.

Recursive recurrent neural network:A novel model for manipulator control with different levels of physical constraints

Manipulators actuate joints to let end effectors to perform precise path tracking tasks.Recurrent neural network which is described by dynamic models with parallel processing capability,is a powerful tool for kinematic control of manipulators.Due to physical limitations and actuation saturation of manipulator joints,the involvement of joint constraints for kinematic control of manipulators is essential and critical.However,current existing manipulator control methods based on recurrent neural networks mainly handle with limited levels of joint angular constraints,and to the best of our knowledge,methods for kinematic control of manipulators with higher order joint constraints based on recurrent neural networks are not yet reported.In this study,for the first time,a novel recursive recurrent network model is proposed to solve the kinematic control issue for manipulators with different levels of physical constraints,and the proposed recursive recurrent neural network can be formulated as a new manifold system to ensure control solution within all of the joint constraints in different orders.The theoretical analysis shows the stability and the purposed recursive recurrent neural network and its convergence to solution.Simulation results further demonstrate the effectiveness of the proposed method in end‐effector path tracking control under different levels of joint constraints based on the Kuka manipulator system.Comparisons with other methods such as the pseudoinverse‐based method and conventional recurrent neural network method substantiate the superiority of the proposed method.

Kinematic Control of Serial Manipulators Under False Data Injection Attack

With advanced communication technologies,cyberphysical systems such as networked industrial control systems can be monitored and controlled by a remote control center via communication networks.While lots of benefits can be achieved with such a configuration,it also brings the concern of cyber attacks to the industrial control systems,such as networked manipulators that are widely adopted in industrial automation.For such systems,a false data injection attack on a control-center-to-manipulator(CC-M)communication channel is undesirable,and has negative effects on the manufacture quality.In this paper,we propose a resilient remote kinematic control method for serial manipulators undergoing a false data injection attack by leveraging the kinematic model.Theoretical analysis shows that the proposed method can guarantee asymptotic convergence of the regulation error to zero in the presence of a type of false data injection attack.The efficacy of the proposed method is validated via simulations.

Adaptive Leader-Follower Consensus Control of Multiple Flexible Manipulators With Actuator Failures and Parameter Uncertainties

In this paper,the leader-follower consensus problem for a multiple flexible manipulator network with actuator failures,parameter uncertainties,and unknown time-varying boundary disturbances is addressed.The purpose of this study is to develop distributed controllers utilizing local interactive protocols that not only suppress the vibration of each flexible manipulator but also achieve consensus on joint angle position between actual followers and the virtual leader.Following the accomplishment of the reconstruction of the fault terms and parameter uncertainties,the adaptive neural network method and parameter estimation technique are employed to compensate for unknown items and bounded disturbances.Furthermore,the Lyapunov stability theory is used to demonstrate that followers’angle consensus errors and vibration deflections in closed-loop systems are uniformly ultimately bounded.Finally,the numerical simulation results confirm the efficacy of the proposed controllers.

Non-Negative Adaptive Mechanism-Based Sliding Mode Control for Parallel Manipulators with Uncertainties

In this paper,a non-negative adaptive mechanism based on an adaptive nonsingular fast terminal sliding mode control strategy is proposed to have finite time and high-speed trajectory tracking for parallel manipulators with the existence of unknown bounded complex uncertainties and external disturbances.The proposed approach is a hybrid scheme of the online non-negative adaptive mechanism,tracking differentiator,and nonsingular fast terminal sliding mode control(NFTSMC).Based on the online non-negative adaptive mechanism,the proposed control can remove the assumption that the uncertainties and disturbances must be bounded for the NFTSMC controllers.The proposed controller has several advantages such as simple structure,easy implementation,rapid response,chattering-free,high precision,robustness,singularity avoidance,and finite-time convergence.Since all control parameters are online updated via tracking differentiator and non-negative adaptive law,the tracking control performance at high-speed motions can be better in real-time requirement and disturbance rejection ability.Finally,simulation results validate the effectiveness of the proposed method.

Fault Diagnosis in Robot Manipulators Using SVM and KNN

In this paper,Support Vector Machine(SVM)and K-Nearest Neighbor(KNN)based methods are to be applied on fault diagnosis in a robot manipulator.A comparative study between the two classifiers in terms of successfully detecting and isolating the seven classes of sensor faults is considered in this work.For both classifiers,the torque,the position and the speed of the manipulator have been employed as the input vector.However,it is to mention that a large database is needed and used for the training and testing phases.The SVM method used in this paper is based on the Gaussian kernel with the parametersγand the penalty margin parameter“C”,which were adjusted via the PSO algorithm to achieve a maximum accuracy diagnosis.Simulations were carried out on the model of a Selective Compliance Assembly Robot Arm(SCARA)robot manipulator,and the results showed that the Particle Swarm Optimization(PSO)increased the per-formance of the SVM algorithm with the 96.95%accuracy while the KNN algo-rithm achieved a correlation up to 94.62%.These results showed that the SVM algorithm with PSO was more precise than the KNN algorithm when was used in fault diagnosis on a robot manipulator.

基于RRT^(*)-DR算法的机械臂避障路径规划

为使机械臂在障碍物环境下快速规划较优路径,提出了一种基于动态区域采样的改进RRT^(*)-DR路径规划算法,将整个规划过程分为快速探索路径和优化初始路径两个步骤。首先利用半目标导向扩展快速探索,找到连接起始点和目标点的路径。随后利用动态区域采样方法,始终在当前最优路径的周边范围内采样,优先密化当前最优路径附近的节点树,节省计算资源,使初始路径经过迭代快速向渐进最优路径收敛。同时,提出一种近障碍节点变步长机制,有选择性地缩短靠近障碍节点的扩展步长,可有效减少碰撞检测失败次数,提高算法效率。最后,在MATLAB和ROS系统下进行路径规划算法仿真,结果表明RRT^(*)-DR算法可在更短时间内实现路径规划,同时有效缩小路径代价。进一步通过实体机器人路径规划避障实验,验证了该算法的实用性和有效性。

采摘机器人机械手避障方法研究——基于遗传优化和模糊PID控制器

为了提高采摘机器人机械手移动的自动化程度,使其在移动过程中有效避开障碍物,将基于PID控制器的避障系统引入到了机械手的设计上,并采用遗传算法和模糊算法对控制器进行了优化,提高了机械手移动的准确性和平稳性。模拟采摘机器人机械手的作业环境,对机械手的避障功能进行了测试,结果表明:机械手可以成功躲避障碍物,且采用遗传和模糊算法优化的机械手具有更高的移动平稳性,对于提高果实采摘效率和质量具有重要的作用。

改进RRT算法的采摘机械臂路径规划

针对采用传统的快速随机扩展树(RRT)算法的采摘机械臂在果园工作环境中搜索路径时间长,最终路径不平滑、拐点多等问题,提出了一种改进的RRT避障算法。改进的算法采用高斯采样策略,减少了采样的随机性,避免产生更多不必要的随机树,增加规划的导向性;再添加A*代价函数去除路径的冗余点,最后使用贪婪算法简化路径,减少拐点,让机械臂可以快速、准确、平稳地沿着最佳路径运动到目标点。仿真表明,改进后的算法有效地减少了路径规划的时间,缩短了路径长度,具有良好的可行性和有效性。

基于卷积神经网络的采摘机械臂无碰撞运动规划研究

介绍了卷积神经网络的基本结构及其工作原理,基于DH参数法建立了采摘机械臂运动模型,并设计了一套采摘机械臂无碰撞运动规划算法,旨在实现对采摘机械臂的精确控制。MatLab仿真试验表明:采摘机械臂在系统的驱动控制下,能够准确从起点移动到目标点,轨迹比较圆滑,且能以最优的圆弧路径避开障碍物,优化效果明显,能够满足采摘机器人作业需求,证实了该算法的稳定性和可靠性。

中和学术思想指导下小儿推拿特色实践与应用

小儿推拿疗法有确切的疗效、丰富的特色和悠久的历史,是中医儿科的一种重要治疗方法。中和学术思想系国医大师孙光荣创立,反复实践于临床,效果可靠,继而形成了“中和医派”。中和学术思想主张恪守中医临床思维,遵循辨证治疗六步程式,强调“中和辨证、中和组方、中和用药”,提出“调气血、平升降、衡出入”的治疗总则,这些思想对小儿推拿的作用、治则、选穴、辨证、手法、治疗套路等都有全新的指导,验之临床,颇有疗效。经多年实践形成了“严守中和理论,谨遵儿童特点;恪守中医思维,遵循经络穴位;注重调和气血,燮理阴阳平衡;兼顾升降平衡,手法轻快柔和;主张辨证施推,注重三因制宜;主症主穴主推,随症加减施推;重视穴位功效,简化手法套路;博采众家之长,融会贯通创新”的八大特色的中和小儿推拿疗法。

我国城市水源水库水质风险成因及对策

我国城市水源中约40%为湖泊或水库,且大部分为水库,因此水源水库的水质状况对我国城市安全极其关键。本文综述了我国水源水库水质问题研究进展,分析了22个代表性水源水库的周年水质状况。结果发现,当前我国城市水源水库水质风险类型主要是异味问题、藻类水华、铁锰超标、有机质偏高、营养盐超标等。引发水源水库水质问题的主要原因包括流域开发强度过大、库底淤积及内源释放、生态系统结构失衡、气候与水文异常变化等。针对上述主要问题,提出了构建在线水质监测预警体系、控制流域土地开发强度、构建面源拦截及流域净化系统、疏浚底泥、优化生态系统结构、实施应急曝气与控藻工程、完善流域生态保护法律法规等多种水源水库水质安全保障技术措施。鉴于水源水库水质及水生态对暴雨、高温热浪等极端气象响应敏感而复杂,在当前极端天气事件频发、强度不断增加的气候背景下,还应加强水库生态学基础研究,以深入理解水源水库水质对气候变化的响应机制,提高水库水安全保障科技支撑能力,满足我国城市高质量发展的水资源需求。

手法复位加针刺经筋结点治疗寰枢关节错缝失眠疗效及对睡眠质量的影响

目的观察手法复位加针刺经筋结点治疗寰枢关节错缝失眠的疗效及对睡眠质量的影响。方法将60例寰枢关节错缝失眠患者按照随机数字表法分为2组,对照组30例予单纯手法按摩复位治疗,治疗组30例予手法复位加针刺经筋结点治疗。2组均治疗10天。比较2组治疗前后中医证候评分、匹兹堡睡眠质量指数量表(PSQI)、世界卫生组织生活质量测定量表(WHOQOL-100)、疼痛视觉模拟评分(VAS)、舒适状况量表(GCQ)评分变化及血流动力学指标[双侧颈内动脉平均血流速度(Vm)、搏动指数(PI)和收缩期最大血流速度(PSV)]。并统计2组疗效。结果治疗组总有效率96.67%(29/30),对照组总有效率80.00%(24/30),治疗组临床疗效优于对照组(P<0.05)。2组治疗后中医证候各项评分均较本组治疗前降低(P<0.05),且治疗组治疗后均低于对照组(P<0.05)。2组治疗后PSQI、疼痛VAS均较本组治疗前降低(P<0.05),且治疗组治疗后均低于对照组(P<0.05),2组治疗后WHOQOL-100、GCQ评分均升高(P<0.05),且治疗组治疗后均高于对照组(P<0.05)。2组治疗后Vm、PI均较本组治疗前升高(P<0.05),且治疗组治疗后均高于对照组(P<0.05),2组治疗后PSV均降低(P<0.05),且治疗组治疗后低于对照组(P<0.05)。结论手法复位加针刺经筋结点治疗寰枢关节错缝失眠的疗效确切,不仅有助于改善患者临床症状和血流动力学,还有利于提高睡眠质量。

微创手术机器人主操作手设计与优化

主操作手作为机器人辅助微创外科手术中人机交互的唯一接口,提高其灵活性和紧凑性,对手术效果具有重要意义。设计了一款8自由度(Degree Of Freedom,DOF)串联型主操作手,其能够实现位置、姿态和开合力矩输出。采用改进的D-H参数法和解析法,分别建立了主操作手正运动学和逆运动学模型,并通过蒙特卡洛法分析了主操作手工作空间,验证了其设计要求;求解了主操作手雅可比矩阵,基于全局灵巧度指标和工作空间利用率指标,定义了主操作手的综合性能指标;利用遗传算法对主操作手的关键连杆尺度参数进行了优化。结果表明,优化后主操作手的综合性能指标相对于优化前提高了18.41%;主操作手在满足工作空间设计要求的基础上,机构更加紧凑且具备了良好的运动性能。