Application of Artificial Neural Network in Robotic Hybrid Position/Force Control

A hybrid position/force controller is designed for the joint 2 and the joint 3 of thePUMA 560 robot.The hybrid controller includes a multilayered neural network,which canidentify the dynamics of the contacted environment and can optimize the parameters of PIDcontroller.The experimental results show that after having been trained,the robot has sta-ble response to the training patterns and strong adaptive ability to the situation between thepatterns.

Hybrid H_2/H_∞ force/position control based on neural networks

A neural network control scheme with mixed H2/H∞performance was proposed for robot force/position control under parameter uncertainties and external disturbances. The mixed H2/H∞tracking performance ensures both robust stability under a prescribed attenuation level for external disturbance and H2optimal tracking. The neural network was introduced to adaptively estimate nonlinear uncertainties, improving the system’s performance under parameter uncertainties as well as obtaining the H2/H∞tracking performance. The simulation shows that the control method performs better even when the system is under large modeling uncertainties and external disturbances.

Adaptive hybrid force/position control for an industrial robot： theory and experiments

This paper proposes a feasible force/position control method for industrial robots utilized for such tasks as grinding, polishing, deburring, and so on. Specifically, an adaptive force/position control strategy is designed in this paper which regulates the contact force between a robot and a workpiece to reach any given set-point exponentially fast, and enables the robot to follow a chosen trajectory simultaneously without requiring prior knowledge of the system parameters. The stability of the closed-loop system is analyzed by Lyapunov techniques. To test the validity of the force/position control method, some simulation results are first collected for the closed-loop system. Furthermore, some experiments are implemented on a 5DOF （degree of freedom） industrial robot for the constructed adaptive force controller. Both simulation and experiment results demonstrate the superior performance of the designed adaptive force/position control strategy.

FUZZY COORDINATION AND FORCE/POSITION CONTROL OF ROBOTIC MANIPULATOR

It is crucial for implementing force/position control of robotic manipulator under the constraint of unknown environment to determine the force control and the position control directions. This paper presents an on line algorithm to real timely estimate the tangent and the normal vectors of the constraint surface based on the measured contact force under the consideration of frictional force. A fuzzy synthesis policy is proposed to coordinate the conflict between the compliant force control and the stiff position control. An experimental study on an AdeptThree, a SCARA type robotic manipulator, is conducted. The experimental results show that the policy presented in the paper is effective.

Impedance force control for position controlled robotic manipulators under the constraint of unknown environments

A force control strategy for position controlled robotic manipulators is presented. On line force feedback data are employed to estimate the local shape of the unknown constraint. The estimated vectors are used to generate the virtual reference trajectory for the target impedance model that is driven by the force error to produce command position. By following the command position trajectory the robotic manipulator can follow the unknown constraint surface while keeping an acceptable force error in a manner depicted by the target impedance model. Computer simulation on a 3 linked planar manipulator and experimental studies on an Adept 3, an SCARA type robotic manipulator, are conducted to verify the force tracking capability of the proposed control strategy.

Decentralized adaptive neural network sliding mode position/force control of constrained reconfigurable manipulators

A decentralized adaptive neural network sliding mode position/force control scheme is proposed for constrained reconfigurable manipulators. Different from the decentralized control strategy in multi-manipulator cooperation, the proposed decentralized position/force control scheme can be applied to series constrained reconfigurable manipulators. By multiplying each row of Jacobian matrix in the dynamics by contact force vector, the converted joint torque is obtained. Furthermore, using desired information of other joints instead of their actual values, the dynamics can be represented as a set of interconnected subsystems by model decomposition technique. An adaptive neural network controller is introduced to approximate the unknown dynamics of subsystem. The interconnection and the whole error term are removed by employing an adaptive sliding mode term. And then, the Lyapunov stability theory guarantees the stability of the closed-loop system. Finally, two reconfigurable manipulators with different configurations are employed to show the effectiveness of the proposed decentralized position/force control scheme.

Robot Position Control Using Force Information for Cooperative Work in Remote Robot Systems with Force Feedback

This paper proposes robot position control using force information for cooperative work between two remote robot systems with force feedback in each of which a user operates a remote robot by using a haptic interface device while observing work of the robot with a video camera. We also investigate the effect of the proposed control by experiment. As cooperative work, we deal with work in which two robots carry an object together. The robot position control using force information finely adjusts the position of the robot arm to reduce the force applied to the object. Thus, the purpose of the control is to avoid large force so that the object is not broken. In our experiment, we make a comparison among the following three cases in order to clarify how to carry out the control effectively. In the first case, the two robots are operated manually by a user with his/her both hands. In the second case, one robot is operated manually by a user, and the other robot is moved automatically under the proposed control. In the last case, the object is carried directly by a human instead of the robot which is operated by the user in the second case. As a result, experimental results demonstrate that the control can help each system operated manually by the user to carry the object smoothly.

Hybrid Dynamic Variables-Dependent Event-Triggered Fuzzy Model Predictive Control

This article focuses on dynamic event-triggered mechanism(DETM)-based model predictive control(MPC) for T-S fuzzy systems.A hybrid dynamic variables-dependent DETM is carefully devised,which includes a multiplicative dynamic variable and an additive dynamic variable.The addressed DETM-based fuzzy MPC issue is described as a “min-max” optimization problem(OP).To facilitate the co-design of the MPC controller and the weighting matrix of the DETM,an auxiliary OP is proposed based on a new Lyapunov function and a new robust positive invariant(RPI) set that contain the membership functions and the hybrid dynamic variables.A dynamic event-triggered fuzzy MPC algorithm is developed accordingly,whose recursive feasibility is analysed by employing the RPI set.With the designed controller,the involved fuzzy system is ensured to be asymptotically stable.Two examples show that the new DETM and DETM-based MPC algorithm have the advantages of reducing resource consumption while yielding the anticipated performance.

A Review of Robotic Force/Position Control

This paper reviews robotic position and force control techniques. The existing fundamental force control algorithms are compared and discussed, including explicit/implicit control, stiffness control, impedance/admit- tance control, and hybrid position/force control. This work is intended to give a basic guidance for understanding and utilization of the fundamental robotic control algorithm.

Compliant landing of a trotting quadruped robot based on hybrid motion/force robust control

A compliant landing strategy for a trotting quadruped robot on unknown rough terrains based on contact force control is presented. Firstly, in order to lower the disturbance caused by the landing impact force, a landing phase is added between the swing phase and the stance phase, where the desired contact force is set as a small positive constant. Secondly, the joint torque optimization of the stance legs is formulated as a quadratic programming(QP) problem subject to equality and inequality/bound constraints. And a primal-dual dynamical system solver based on linear variational inequalities(LVI) is applied to solve this QP problem. Furthermore, based on the optimization results, a hybrid motion/force robust controller is designed to realize the tracking of the contact force, while the constraints of the stance feet landing angles are fulfilled simultaneously. Finally, the experiments are performed to validate the proposed methods.

Robust scheme of global parallel force/position regulators for robot manipulators under environment uncertainty

A simple robust scheme of parallel force/position control is proposed in this paper to deal with two problems for non-planar constraint surface and nonlinear mechanical feature of environment： i） uncertainties in environment that are usually not available or difficult to be determined in most practical situations; ii） stability problem or/and integrator windup due to the integration of force error in the force dominance rule in parallel force/position control. It shows that this robust scheme is a good alternative for anti-windup. In the presence of environment uncertainties, global asymptotic stability of the resulting closed-loop system is guaranteed; it environment with complex characteristics. Finally, numerical robot manipulator. also shows robustness of the proposed controller to uncertain simulation verifies results via contact task of a two rigid-links

Hybrid force control of astronaut rehabilitative training robot under active loading mode

In order to mitigate the effects of space adaptation syndrome(SAS) and improve the training efficiency of the astronauts, a novel astronaut rehabilitative training robot(ART) was proposed. ART can help the astronauts to carry out the bench press training in the microgravity environment. Firstly, a dynamic model of cable driven unit(CDU) was established whose accuracy was verified through the model identification. Secondly, to improve the accuracy and the speed of the active loading, an active loading hybrid force controller was proposed on the basis of the dynamic model of the CDU. Finally, the actual effect of the hybrid force controller was tested by simulations and experiments. The results suggest that the hybrid force controller can significantly improve the precision and the dynamic performance of the active loading with the maximum phase lag of the active loading being 9° and the maximum amplitude error being 2% at the frequency range of 10 Hz. The controller can meet the design requirements.

Collision avoidance for a mobile robot based on radial basis function hybrid force control technique

Collision avoidance is always difficult in the planning path for a mobile robot. In this paper, the virtual force field between a mobile robot and an obstacle is formed and regulated to maintain a desired distance by hybrid force control algorithm. Since uncertainties from robot dynamics and obstacle degrade the performance of a collision avoidance task, intelligent control is used to compensate for the uncertainties. A radial basis function （RBF） neural network is used to regulate the force field of an accurate distance between a robot and an obstacle in this paper and then simulation studies are conducted to confirm that the proposed algorithm is effective.

Position/Force Hybrid Control System for High Precision Aligning of Small Gripper to Ring Object

A position/force hybrid control system based on impedance control scheme is designed to align a small gripper to a special ring object. The vision information provided by microscope vision system is used as the feedback to indicate the position relationship between the gripper and the ring object. Multiple image features of the gripper and the ring object are extracted to estimate the relative positions between them. The end-effector of the gripper is tracked using the extracted features to keep the gripper moving in the field of view. The force information from the force sensor serves as the feedback to ensure that the contact force between the gripper and the ring object is limited in a small safe range. Experimental results verify the effectiveness of the proposed control strategy.

NOVEL 6-DOF WEARABLE EXOSKELETON ARM WITH PNEUMATIC FORCE-FEEDBACK FOR BILATERAL TELEOPERATION

A particular emphasis is put on a novel wearable exoskeleton arm, ZJUESA, with 6 degrees of freedom, which is used for the robot teleoperation with the force-feedback in the unknown environment. In this external structure mechanism, the 3-revolution-prismatic-spherical （3RPS） parallel mechanism is devised from the concept of the human upper-limb anatomy and applied for the shoulder 3-DOF joint. Meanwhile, the orthogonal experiment design method is introduced for its optimal design. Aiming at enhancing the performance of teleoperation, the force feedback is employed by the pneumatic system on ZJUESA to produce the vivid feeling in addition to the soft control interface. Due to the compressibility and nonlinearity of the pneumatic force feedback system, a novel hybrid fuzzy controller for the precise force control is proposed and realized based on the Mega8 microcontroller units as the units of the distributed control system on ZJUESA. With the results of several experiments for master-slave control with force feedback, the feasibility of ZJUESA system and the effect of its hybrid fuzzy controller are verified.

双悬臂截割机器人相对动力学建模与力位混合控制研究

双悬臂截割机器人可解决传统单臂掘进机在截割大尺寸断面时效率低下的难题,但其与煤岩的动态交互影响控制性能。现有研究以双臂接触同一对象形成运动闭链为前提,无法满足双悬臂截割机器人双臂运动及末端截割头输出力的控制要求。针对该问题,设计了一种基于机器人相对动力学模型的力位混合控制系统。建立双悬臂截割机器人运动学和动力学模型,基于机器人的相对雅可比矩阵及虚位移与虚功原理推导出机器人的相对动力学模型,通过单一变量同时描述机器人双臂的运动状态,将机器人双臂独立的动力学模型整合为一个整体。基于机器人的相对动力学模型,设计了机器人双臂力位混合控制系统,通过李雅普诺夫函数验证了系统的稳定性和可行性。仿真结果表明:双悬臂截割工艺较单悬臂截割拥有更大的工作空间,具有一次性实现大断面截割的能力;双悬臂截割机器人力位混合控制系统能够完成对期望相对位置和期望相对力的同步跟踪,对截割头期望位置跟踪的绝对误差在0.3132 m以内,均方根误差为0.1447 m。

两相式混合步进电机速度和位移控制案例设计

步进电机在电脉冲信号的控制下能实现一定的角位移或线位移,掌握步进电机的控制技术非常重要。以两相混合式步进电机为控制对象,以单片机AT89C52和可编程计数器/定时器8253为主对电机进行速度和位移控制设计,对某一具体案例采用Proteus软件进行设计与仿真,包括控制电路和程序的设计。仿真和实验结果表明:该系统结构简单、容易实现、成本低,可用于其他步进电机的控制,并为教学或工程应用提供了参考。

高压阀口液动力补偿控制策略仿真分析

直动式比例伺服阀是一种新型的单级伺服阀,采用比例电磁铁或线性力马达直接驱动滑阀运动,具有成本低、频响高、抗污染能力强等优点,广泛应用于注塑机进给控制、轧机板厚控制系统等高速高精度液压控制系统中。传统的直动式比例伺服阀采用PID控制策略实现阀芯的精确位置控制,在高压大流量工况下(供油压力达到20 MPa、流量大于100 L/min),阀芯受到液动力扰动变得更加剧烈,易出现响应速度慢、阀芯抖动的现象,影响了比例伺服阀的控制精度。为了解决大液动力扰动的问题,提出了一种基于指数收敛干扰观测器的滑模控制器。通过建立阀芯动力学模型来设计滑模控制器,保证阀芯位置的高动态跟踪性能;使用基于指数收敛的干扰观测器,估计阀芯动力学模型中液动力的不确定性扰动,解决高压大流量工况下的阀芯液动力扰动补偿问题。搭建了比例伺服阀系统的联合仿真模型进行仿真验证,结果表明,提出的控制器可以解决高压大流量工况下阀芯液动力扰动对阀芯位置控制的干扰问题。

铆接机器人位置和压铆力自适应模糊滑模阻抗控制

针对机器人铆接小尺寸、低刚度铆钉过程中对准精度要求高、作业行程小和冲击力大等问题,提出了一种结合自适应模糊滑模控制和模糊阻抗控制的机器人铆接控制方法。为此,将机器人铆接控制过程分为接近对准与压铆两个阶段。其中,为了实现快速接近铆钉顶部以及铆接头与铆钉轴线的精确对准,设计了以双曲正切函数为切换函数的模糊自适应滑模控制器,使得切换过程更加光滑、滑模增益自适应调节,从而降低了系统的抖振,实现了运动的快速性和准确性;针对压铆阶段作业行程小、冲击力大的特点,设计了以接触力误差和力增量为输入的阻尼调整模糊控制器,减少了阻尼参数在阻抗控制过程中产生的振荡,避免了过度力加载导致的铆钉损坏或铆接失败。仿真及试验结果表明,所给出的控制方法位置精度在0.3 mm以内,力接触具有更好的稳定性、实时性和快速性,能够满足铆接工艺规范要求,对于铆接机器人的高精度作业具有借鉴意义。

单绳缠绕式矿井提升机制动瞬态冲击抑制策略

在矿井提升机的制动控制中,制动瞬态冲击是影响矿井提升机安全可靠运行的关键问题。由于受到技术和经济成本的制约,目前包含贴闸/压紧的制动过程全程采用力闭环控制,这不可避免地导致闸瓦与闸盘刚性接触时的制动力瞬态冲击问题。针对矿井提升机制动瞬态冲击问题,文中提出了一种基于滞回切换原理的混合贴闸/压紧制动控制策略。首先,分别利用非奇异快速终端滑模控制和反步控制设计了贴闸和压紧控制器;其次,为达到快速贴闸的目的,设计了一种基于离散积分器的在线贴闸轨迹再规划方法,有效地减小了制动器贴闸时间;然后,为实现由贴闸控制到压紧控制的安全切换,利用滞回切换原理,制定了混合贴闸/压紧控制稳态切换策略,大大减小了制动力瞬态冲击;最后,为验证提出方法的有效性,选择传统全程力闭环控制策略C1与混合贴闸/压紧直接切换策略C2作为对比方法,在单绳缠绕式提升试验台进行了对比实验,从贴闸时间、制动力最大跟踪误差和提升钢丝绳最大张力3个方面对实验结果进行了分析。实验结果表明:相较于C1控制策略,提出制动控制策略的贴闸时间缩短了64.5%,钢丝绳张力峰值减小了41 N;相较于C2控制策略,提出制动控制策略的制动力冲击减小了90.3%,钢丝绳张力峰值减小了88 N。上述结果表明本文提出的方法能有效改善制动瞬态冲击,减小制动空动时间,提高制动系统安全性,同时本研究也为一类需要混合力/位置控制的电液伺服系统提供了一种有效的解决方法。