失重环境下可控柔性臂的模态特性

在非重力场中,考虑控制器动态反馈的影响,对存在控制器定位约束的柔性臂系统进行动力分析,研 究其在相对平衡位置的模态特性．以具有柔性关节和弹性臂杆的可控柔性臂为研究对象,分析了控制器作用下的反 馈约束特性,将控制器位置和速度增益引入力边界条件,得到了耦合控制器参量的模态特征方程,证明了反馈约束 的存在使得系统特征频率为复频率,且模态主振型是复变函数．通过数值仿真,明确了可控柔性臂的模态特性与控 制器增益之间的关系,得到了不同于经典振动理论的结论．设计了可控柔性臂的仿失重实验平台,试验模态结果证 明了理论分析的有效性．     

Soft computing methods applied to the control of a flexible robot manipulator

The paper describes use of soft computing methods (fuzzy logic and neural network techniques) in the development of a hybrid fuzzy neural control (HFNC) scheme for a multi-link flexible manipulator. A manipulator with multiple flexible links is a multivariable system of considerable complexity due to the inter-link coupling effects that are present in both rigid and flexible motions. Modelling and controlling the dynamics of such manipulators is therefore difficult. The proposed HFNC scheme generates control actions combining contributions form both a fuzzy controller and a neural controller. The primary loop of the proposed HFNC contains a fuzzy controller and a neural network controller in the secondary loop to compensate for the coupling effects due to the rigid and flexible motion along with the inter-link coupling. It has been ascertained from the present investigation that the proposed soft-computing-based controller works effectively in the tracking control of such a multi-link flexible manipulator. The results are extendable to other multivariable systems of similar complexity.     

受时变约束柔性臂鲁棒RBF神经网络力/位置控制

研究了受时变约束的柔性臂系统，建立了分布参数模型，通过奇异摄动方法将该模型划分为表征系统刚性运动的集中参数子系统和表征系统振动的分布参数子系统．设计了集中参数子系统的鲁棒RBF神经网络力／位置控制算法和分布参数子系统的鲁棒自适应振动抑制控制算法．理论分析及仿真结果验证了该方法的有效性．     

Dynamic hybrid position/force control of a two degree-of-freedom flexible manipulator

In this article, a method is proposed whereby both contact force exerted by a flexible manipulator and position of end-effector while in contact with a surface are controlled. We approximate elastic deformations by means of B-spline functions and derive dynamic equations of joint angles, vibration of the flexible link, and constraint force. A controller for the hybrid position/force control of the flexible manipulator is designed on the basis of the singular perturbation method. Simulation results confirm that the controller performs remarkably well. © 3994 John Wiley & Sons, Inc.     

Observer-Based Adaptive Controller Design of Flexible Manipulators Using Time-Delay Neuro-Fuzzy Networks

In this paper, a dynamical time-delay neuro-fuzzy controller is proposed for the adaptive control of a flexible manipulator. It is assumed that the robotic manipulator has only joint angle position measurements. A linear observer is used to estimate the robot joint angle velocity. For a perfect tracking control of the robot, the output redefinition approach is used in the adaptive controller design using time-delay neuro-fuzzy networks. The time-delay neuro-fuzzy networks with the rule representation of the TSK type fuzzy system have better learning ability for complex dynamics as compared with existing neural networks. The novel control structure and learning algorithm are given, and a simulation for the trajectory tracking of a flexible manipulator illustrates the control performance of the proposed control approach.     

A Review of: “Fuzzy Sets and Their Applications”. By C. V. NEGOIT[Abreve] and D. A. RALESCU. (In Rumanian) (Bucharest: Editura Technica, 1974.) [Pp.219.]

This paper presents a new controller for position and force control of robotic devices interacting with passive environments. In this approach, for the manipulator dynamics in joint space, suitable output equations are defined which represent the position control and force control subspaces. The dynamics of the manipulator are projected along these subspaces to obtain the dynamics in the respective subspaces. The resulting dynamics are linearized and decoupled using a nonlinear input-state linearizing controller. For the position control subspace dynamics, desirable stability features are achieved through pole placement design. Along the force control subspace, a soft base is introduced, the compliance effect of which is controlled by an appropriate compensation term. Based on the force feedback information, this compensation is modifed online using an extended dynamics. Assuming a model of the passive environment, aspects of local stability of the controller have been discussed. The theory has been presented for a two-link planar manipulator example, based on which, a numerical simulation is discussed.     

Continuous shape-grinding experiment based on model-independent force/position hybrid control method with on-line spline approximation

Based on the analysis of interaction between manipulator’s hand and working object, a model representing the constrained dynamics of robot is first discussed. The constraint forces are expressed by algebraic function of states, input generalized forces, and constraint condition, and then a decoupling control method of force and position of manipulator’s hand tip is proposed. In order to give the grinding system the ability to adapt to any object shape being changed by the grinding, estimating function of the constraint condition in real time for the adaptive force/position control was added, which is indispensable for the proposed method without using force sensor. This paper explores whether the performance of the proposed controller is independent of alloy work-piece models or not. The experimental result is shown to verify the feature of the decoupling control of force and position of the tip.     

Review and unification of reduced-order force control methods

In this article, we compare some of the recent methods developed for simultaneous position and force control of a single n-link constrained robot manipulator. Mathematical models of the constrained manipulator are introduced and the advantages and disadvantages of the associated control formulations are discussed. The similarities between each of the proposed formulations are also highlighted. Finally, a transformation is presented that generalizes the methods of decoupling force from the position dynamics.     

Position and force tracking of a two‐manipulator system manipulating a flexible beam

This article discusses the issue of hybrid position and force control of a two‐manipulator system manipulating a flexible beam in trajectory tracking. Unlike our previous approach of set‐point position control in the trajectory tracking, the system coordinates are hard to be regulated to the desired states with nonzero tracking velocities under continuous feedback control. In this study, we design a hybrid position and force tracking controller while using saturation control to compensate for the effect of beam vibration dynamics on the tracking performance. All parameters and states used in the controller are readily available so that the proposed method is feasible to implement. Under the proposed controller, the tracking error asymptotically converges to a predetermined boundary. Simulation results demonstrate the validity of the proposed approach. © 2001 John Wiley & Sons, Inc.     

Position/vibration control of two-degree-of-freedom arms having one flexible link with artificial pneumatic muscle actuators

A manipulator with a light and thus flexible link would be advantageous over a rigid link in the sense that it is physically safer when it comes into contact with its environment than a manipulator with a rigid and thus heavy link, even though it is harder for a flexible link manipulator to be robustly controlled. On the other hand, if an actuator can deliver enough force while maintaining proper compliance, it would be advantageous for the sake of safety. An artificial pneumatic muscle-type actuator is an adequate choice in this case. In this work, position control problem of a two-degree-of-freedom arm system having a flexible second link with artificial pneumatic muscle-type actuators is addressed. A composite controller design method is proposed in the framework of the singular perturbation method. Various robust control schemes are designed in order to meet with payload variation, parameter uncertainty, unmodeled vibration mode, actuator dynamics both in the slow and the fast subsystems.     

Sliding mode force,motion control,and stabilization of elastic manipulator in the presence of uncertainties

This article considers the question of position and force control of three-link elastic robotic systems on a constraint surface in the presence of robot parameter and environmental constraint geometry uncertainties. The approach of this article is applicable to any multi-link elastic robot. A sliding mode control law is derived for the position and force trajectory control of manipulator. Unlike the rigid robots, sliding mode control of an end point gives rise to unstable zero dynamics. Instability of the zero dynamics is avoided by Controlling a point that lies in the neighborhood of the actual end point position. The sliding mode controller accomplishes tracking of the end-effector and force trajectories on the constrained surface; however, the maneuver of the arm causes elastic mode excitation. For point-to-point control on the constraint surface, a stabilizer is designed for the final capture of the terminal state and vibration suppression. Numerical results are presented to show that in the closed-loop system position and force control is accomplished in spite of payload and constraint surface geometry uncertainty. © 1995 John Wiley & Sons, Inc.     

Neural Network Force Control for Industrial Robots

In this paper, we present a hierarchical force control framework consisting of a high level control system based on neural network and the existing motion control system of a manipulator in the low level. Inputs of the neural network are the contact force error and estimated stiffness of the contacted environment. The output of the neural network is the position command for the position controller of industrial robots. A MITSUBISHI MELFA RV-M1 industrial robot equipped with a BL Force/Torque sensor is utilized for implementing the hierarchical neural network force control system. Successful experiments for various contact motions are carried out. Additionally, the proposed neural network force controller together with the master/slave control method are used in dual-industrial robot systems. Successful experiments are carried out for the dual-robot system handling an object.     

Robust Control of a Two-Link Flexible Manipulator with Quasi-Static Deflection Compensation Using Neural Networks

A robust control method of a two-link flexible manipulator with neural networks based quasi-static distortion compensation is proposed and experimentally investigated. The dynamics equation of the flexible manipulator is divided into a slow subsystem and a fast subsystem based on the assumed mode method and singular perturbation theory. A decomposition based robust controller is proposed with respect to the slow subsystem, and H ^∞ control is applied to the fast subsystem. The overall closed-loop control is determined by the composite algorithm that combines the two control laws. Furthermore, a neural network compensation scheme is also integrated into the control system to compensate for quasi-static deflection. The proposed control method has been implemented on a two-link flexible manipulator for precise end-tip tracking control. Experimental results are presented in this paper along with concluding remarks.     

Adaptive Jacobian position/force tracking control of free-flying manipulators

This paper solves the problem of position/force tracking control of a free-flying space manipulator with uncertain kinematics and dynamics. A free-flying manipulator interacting with an uncertain compliant surface is considered. To cope with the uncertainties arising from free-flyer’s kinematics, dynamics and surface stiffness and position, an adaptive Jacobian controller is devised. The convergence of the force and position tracking errors is proved based on Lyapunov stability analysis. Numerical simulation is presented to show the performance of the controller.     

基于卷积神经网络的机械臂抓取控制系统设计

为保证机械臂的抓取精度,保证物体抓取的稳定性,本文设计基于卷积神经网络的机械臂抓取控制系统。在系统硬件部分,加设图像、位置和压力传感器,改装机械臂抓取控制器和运动驱动器,利用图像传感器设备,获取满足质量要求的机械臂抓取目标图像,为机械臂抓取控制功能提供硬件支持。软件部分利用卷积神经网络算法提取图像特征,确定机械臂抓取目标位置。结合机械臂当前位置的检测结果,规划机械臂抓取路线,预估机械臂抓取角度与抓取力。最终通过机械臂抓取参数控制量的计算,在控制器的支持下实现系统的机械臂抓取控制功能。实验结果表明,所设计系统应用下位置控制误差和速度控制误差的平均值分别为0.192m和0.138m/s,同时物体抓取掉落概率明显降低。     

Stability and control aspects of flexible link robot manipulators during constrained motion tasks

The effect of robotic manipulator structural compliance on system stability and trajectory tracking performance and the compensation of this structural compliance has been the subject of a number of publications for the case of robotic manipulator noncontact task execution. The subject of this article is the examination of dynamics and stability issues of a robotic manipulator modeled with link structural flexibility during execution of a task that requires the robot tip to contact fixed rigid objects in the work environment. The dynamic behavior of a general n degree of freedom flexible link manipulator is investigated with a previously proposed nonlinear computed torque constrained motion control applied, computed based on the rigid link equations of motion. Through the use of techniques from the theory of singular perturbations, the analysis of the system stability is investigated by examining the stability of the “slow” and “fast” subsystem dynamics. The conditions under which the fast subsystem dynamics exhibit a stable response are examined. It is shown that if certain conditions are satisfied a control based on only the rigid link equations of motion will lead to asymptotic trajectory tracking of the desired generalized position and force trajectories during constrained motion. Experiments reported here have been carried out to investigate the performance of the nonlinear computed torque control law during constrained motion of the manipulator. While based only on the rigid link equations of motion, experimental results confirm that high-frequency structural link modes, exhibited in the response of the robot, are asymptotically stable and do not destabilize the slow subsystem dynamics, leading to asymptotic trajectory tracking of the overall system. © 1992 John Wiley & Sons, Inc.     

基于分布参数模型的柔性臂变结构力控制

提出一种基于受约束柔性臂分布参数模型的麦结构力控稍方法,避免了集中参数模型的缺陷,解决了系统存在模型不确定性和外部干扰影响下的力控制问题.通过Lyapunov函数设计了系统的变结构控制器,其中滑模面设计为系统转动角、转动角速度和柔性臂根部应变的线性组合.利用线性算子半群理论和LaSalle不变集原理,证明了闭环系统的渐近稳定性.仿真结果验证了该方法的有效性.     

Sliding Mode Control for Flexible-link Manipulators Based on Adaptive Neural Networks

This paper mainly focuses on designing a sliding mode boundary controller for a single flexible-link manipulator based on adaptive radial basis function (RBF) neural network. The flexible manipulator in this paper is considered to be an Euler-Bernoulli beam. We first obtain a partial differential equation (PDE) model of single-link flexible manipulator by using Hamiltons approach. To improve the control robustness, the system uncertainties including modeling uncertainties and external disturbances are compensated by an adaptive neural approximator. Then, a sliding mode control method is designed to drive the joint to a desired position and rapidly suppress vibration on the beam. The stability of the closed-loop system is validated by using Lyapunov’s method based on infinite dimensional model, avoiding problems such as control spillovers caused by traditional finite dimensional truncated models. This novel controller only requires measuring the boundary information, which facilitates implementation in engineering practice. Favorable performance of the closed-loop system is demonstrated by numerical simulations.     

Neural network-based position synchronised internal force control scheme for cooperative manipulator system

More complex problems of simultaneous position and internal force control occur with cooperative manipulator systems than that of a single one. In the presence of unwanted parametric and modelling uncertainties as well as external disturbances, a decentralised position synchronised force control scheme is proposed. With a feedforward neural network estimating engine, a precise model of the system dynamics is not required. Unlike conventional cooperative or synchronised controllers, virtual position and virtual synchronisation errors are introduced for internal force tracking control and task space position synchronisation. Meanwhile joint space synchronisation and force measurement are unnecessary. Together with simulation studies and analysis, the position and the internal force errors are shown to asymptotically converge to zero. Moreover, the controller exhibits different characteristics with selected synchronisation factors. Under certain settings, it can deal with temporary cooperation by an intelligent retreat mechanism, where less internal force would occur and rigid collision can be avoided. Using a Lyapunov stability approach, the controller is proven to be robust in face of the aforementioned uncertainties.