Linearized dynamic models for trajectory control of robot manipulators

To make an appropriate choice of linear model for the development of real-time control of robot manipulators, four direct linearization schemes have been studied here on the basis of computation and accuracy. Accuracy is assured in the state linearization method, whereas the rate linearization method leads to a satisfactory trade-off between computational effort and accuracy. In the case of high-velocity motions, a combination of state and rate linearization is proposed.     

Direct adaptive control of manipulators in cartesian space

A new adaptive-control scheme for direct control of manipulator end effector to achieve trajectory tracking in Cartesian space is developed in this article. The control structure is obtained from linear multivariable theory and is composed of simple feedforward and feedback controllers and an auxiliary input. The direct adaptation laws are derived from model reference adaptive control theory and are not based on parameter estimation of the robot model. The utilization of adaptive feedforward control and the inclusion of auxiliary input are novel features of the present scheme and result in improved dynamic performance over existing adaptive control schemes. The adaptive controller does not require the complex mathematical model of the robot dynamics or any knowledge of the robot parameters or the payload, and is computationally fast for on-line implementation with high sampling rates. The control scheme is applied to a two-link manipulator for illustration.     

Adaptive control of robot manipulators in task space

Adaptive control of robotic manipulators in task space or Cartesian space is considered. A general Lyapunov-like concept is used to design an adaptive control law. It is shown that the global stability and convergence can be achieved for the adaptive control algorithm. The algorithm has the advantage that the inverse of Jacobian matrix is not required. The algorithm is further modified so that the requirement for the bounded inverse of estimated inertia matrix is also eliminated. In addition, two approaches are presented to achieve robustness to bounded disturbances     

基于PVR技术的机器人动态仿真控制

详细分析了基于投射式虚拟现实技术的机器人动态仿真控制,提出了仿真控制系统的总体框架,实现了仿真任务,并对投射式虚拟现实技术进行了改进,就投射式虚拟现实技术在一般工程仿真中的应用进行了有益的探讨.     

Real- time geometric modeling using models in an actuator space and cartesian space

In this article, three models in two different spaces are compared for use with an image processing system working in real time. A static geometric model of a robot work-cell is held in computer memory as several solid polyhedra. This model is updated as new objects enter or leave the workplace. Similar 2-D slices in joint space, spheres, and simple polyhedra are used to model these objects. The three models are compared for their ability to be updated with new information and for the efficiency of the whole system in accessing data concerning new objects. The system supplies data to a “Path Planner.” The path planner contains a geometric model of the static environment and a robot. The robot machinery structure is modeled as connected cylinders and spheres and the range of motion is quantized. © 1995 John Wiley & Sons, Inc.     

基于状态空间的机械臂轨迹规划

提出一种基于状态空间的机械臂轨迹规划方法,定义并构造了机械臂系统的状态空间,根据内在机构约束与外部环境约束描述出系统状态的可达范围,并给出了任务的可实现条件.对于可实现任务,在状态空间能搜索到任务完成的最优解.如果任务无法完成,则修改系统配置或约束,在新的状态空间确定任务实现的转化条件,并对任务的设计与规划给予指导.研究了障碍约束下两连杆机械臂的点到点任务,实验结果验证了该方法的有效性.     

Distribution of dynamic loads for multiple cooperating robot manipulators

For the situation of multiple cooperating manipulators handling a single object, a formulation is presented which allows load distribution of the combined system to be made while taking manipulator dynamics into account. First, object dynamics are used to transform the motion task. An integrated procedure for modeling arm dynamics is detailed. Then a method is introduced which transforms the object load to the joint level. At this level, various methods of load distribution that allow subtask performance are proposed. These methods allow desired object motion while selecting loads desirable to alleviate manipulator dynamic loads.     

A multi-purpose dynamic and tactile sensor for robot manipulators

In this article, a multi-purpose tactile/acceleration sensor system made of newly developed thick polymeric piezoelectric material and capable of sensing acceleration, contact force and pressure is developed and evaluated using analytical and experimental techniques, The sensor system is discretized and modeled by a single degree of freedom second order system from which equations of electromechanical dynamics are formulated to evaluate its performance. Analytical solutions are compared favorably with experimental results.     

Adaptive control for robot manipulators using multiple parameter models

In this paper, we propose multiple parameter models based adaptive switching control system for robot manipulators. We first uniformly distribute the parameter set into a finite number of smaller compact subsets. Then, distributed candidate controllers are designed for each of these smaller compact subsets. Using Lyapunov inequality, a candidate controller is identified from the finite set of distributed candidate controllers that best estimates the plant at each instant of time. The design reduced the observer-controller gains by reducing modeling errors and uncertainties via identifying an appropriate control/model via choosing largest guaranteed decrease in the value of the Lyapunov function energy function. Compared with CE based CAC design, the proposed design requires smaller observer-controller gains to ensure stability and tracking performance in the presence of large-scale modeling errors and disturbance uncertainties. In contrast with existing adaptive method, multiple model based distributed hybrid design can be used to reduce the energy consumption of the industrial robotic manipulator for large scale industrial automation by reducing actuator input energy. Finally, the proposed hybrid adaptive control design is experimentally tested on a 3-DOF Phantom^TM robot manipulator to demonstrate the theoretical development for real-time applications.

     

ANFIS-based an adaptive continuous sliding-mode controller for robot manipulators in operational space

Multibody System Dynamics - This paper addresses the task-space robust trajectory tracking control problem for robot manipulators in the presence of uncertainties and external disturbances. First,...     

A finite element formulation for dynamic parameter identification of robot manipulators

In this paper a finite element based approach is described for the automatic generation of models suitable for dynamic parameter identification. The method involves a nonlinear finite element formulation in which both links and joints are considered as specific finite elements [6, 7]. Since the identification procedure considers rigid-link robot models, the inertial properties of the link elements are described using a lumped mass formulation. The parameters to be identified are masses, first-order moments and inertial tensor components of the links. The equations of motion are written in a form which is linear in the dynamic parameters. This formulation is obtained by employing Jourdain’s principle of virtual power. The parameters are estimated using a linear least squares technique. Singular value decomposition of the regression matrix is used to find the minimum parameter set. Simulation results obtained from the 6 DOF PUMA 560 robot based on the estimated parameters show that the method yields accurate responses.     

Robust neuro-fuzzy sensor-based motion control among dynamic obstacles for robot manipulators

A new robust neuro-fuzzy controller for autonomous and intelligent robot manipulators in dynamic and partially known environments containing moving obstacles is presented. The navigation is based on a fuzzy technique for the idea of artificial potential fields (APFs) using analytic harmonic functions. Unlike the fuzzy technique, the development of APFs is computationally intensive. A computationally efficient processing scheme for fuzzy navigation to reasoning about obstacle avoidance using APF is described, namely, the intelligent dynamic motion planning. An integration of a robust controller and a modified Elman neural networks (MENNs) approximation-based computed-torque controller is proposed to deal with unmodeled bounded disturbances and/or unstructured unmodeled dynamics of the robot arm. The MENN weights are tuned online, with no off-line learning phase required. The stability of the overall closed-loop system, composed by the nonlinear robot dynamics and the robust neuro-fuzzy controller, is guaranteed by the Lyapunov theory. The purpose of the robust neuro-fuzzy controller is to generate the commands for the servo-systems of the robot so it may choose its way to its goal autonomously, while reacting in real-time to unexpected events. The proposed scheme has been successfully tested. The controller also demonstrates remarkable performance in adaptation to changes in manipulator dynamics. Sensor-based motion control is an essential feature for dealing with model uncertainties and unexpected obstacles in real-time world systems.     

Task decoupling in robot manipulators

Task decoupling in robotic manipulators implies that there is a subset of joints primarily responsible for the completion of a subset of the manipulator task. In this paper, we take a novel and general view of task decoupling in which we identify link subsystems primarily responsible for completion of a subset of the manipulator task components, which is not necessarily position or orientation. Our analysis leads to the discovery of other decoupled manipulator geometries never identified before, wherein the decoupled system is responsible for a subset of degress-of-freedom involving a hybrid combination of both position and orientation. Closed-form inverse kinematic solutions for these manipulator geometries are therefore guaranteed. Task decoupling also implied singularity decoupling wherein singularities of decoupled subsystems are equivalent to the manipulator singularities. The analysis leads to a novel and efficient method for identifying the singularities and solving the inverse kinematics problem of six-axes manipulators with decoupled geometries. The practicality of the concepts introduced is demonstrated through an industrial robot example involving a hybrid position and orientation decoupling.     

A systematic formulation of dynamic equations for robot manipulators with elastic links

Although a variety of formulation schemes for the dynamic equations of robot manipulators with rigid links can be found in the literature, an efficient method of the formulation for robot manipulators with elastic links is not well known. Accordingly, this work presents the derivation of the equations of motion for application to mechanical manipulators with elastic links. The formulation is conducted analytically using Hamilton's principle. The resultant equations consist of the terms of inertial, Coriolis, centrifugal, gravitational, and exerted forces. They are expressed in terms of a set of independent generalized coordinates. In contrast to conventional variational approaches, the present method provides an efficient and systematic way for obtaining the compact symbolic equations of flexible manipulator systems. Two examples are presented to illustrate the proposed methodology. Firstly, a three-link flexible manipulator with three revolute joints is studied. A flexible manipulator consisting of a prismatic joint and a discrete mass is the second model.     

Combined direct and indirect adaptive control of robot manipulators using multiple models

A novel methodology is proposed for the adaptive control of rigid robotic manipulators. The proposed method utilizes multiple adaptive models for the identification and control of the manipulator. The present study is an extension of our previous work which utilized an indirect adaptive control approach with multiple models for better transient performance. The proposed scheme uses a composite approach where both prediction and tracking errors are used in a combined direct and indirect adaptive control framework. Simulation results are given to demonstrate the efficient use of the methodology.     

Flexible control for robot manipulators

We designed, implemented, and tested a real-time flexible controller for manipulating different types of robots and control algorithms. The robot-independent, IBM PC-based multiprocessor system contains a DSP56001 master controller, six independent HCTL-1100 joint processors for accurate robotic joint control, and an interface computer board for processor communication. The joint processors operate in four user-defined modes and can be connected directly to an incremental optical encoder, which accommodates specialized applications and eliminates extra hardware     

Finite-time control for robot manipulators

Finite-time control of the robot system is studied through both state feedback and dynamic output feedback control. The effectiveness of the proposed approach is illustrated by both theoretical analysis and computer simulation. In addition to offering an alternative approach for improving the design of the robot regulator, this research also extends the study of the finite-time control problem from second-order systems to a large class of higher order nonlinear systems.     

Fault identification for robot manipulators

Several factors must be considered for robotic task execution in the presence of a fault, including: detection, identification, and accommodation for the fault. In this paper, a nonlinear observer is used to identify a class of actuator faults once the fault has been detected by some other method. Advantages of the proposed fault-identification method are that it is based on the nonlinear dynamic model of a robot manipulator (and hence, can be extended to a number of general Euler Lagrange systems), it does not require acceleration measurements, and it is independent from the controller. A Lyapunov-based analysis is provided to prove that the developed fault observer converges to the actual fault. Experimental results are provided to illustrate the performance of the identification method.     

Task-based optimization of reconfigurable robot manipulators

Reconfigurable Manipulators are structurally redundant robots that utilize a subset of their joints to perform a specific task optimally. This paper presents a method of finding a task-based optimal configuration for a new type of reconfigurable robot manipulator, called the modular autonomously reconfigurable serial (MARS) manipulator. The reconfiguration optimization treats the joint space of the MARS manipulator as a 12-dimensional smooth configuration manifold. The manifold is discretized and ranked based on a variety of criteria, and then clustered into attractive and repellent regions. The user then specifies which regions are desired in the target configuration, and the manifold is reduced in dimension in order to maximize the number of attractive regions and minimize the number of repellent regions. Six manipulator configurations are synthesized using this approach, and their effectiveness is compared.