Force and Position Control of Robot Manipulators: Learning Control Approach

The learning control is applied to hybrid force and position control of robot manipulators. When the geometry and position of a constraint surface is known, the hybrid force and position controller and the feedforward compensator can be designed in the constraint coordinates. When the operation is periodic, the learning hybrid force and position control enhance the control performance as the feedforward compensator is updated in each cycle by the force and position error in the preceding trials. This scheme is proved to be asymptotically stable. A two degree of freedom SCARA-type direct-drive robot manipulator is used to test the learning hybrid force and position control. The deburring tool mounted on the upper link of the robot could follow a flat, tilted flat, and curved 1/4 " aluminum plate with a desired contact force of 10 N (within the root-mean-square force error of 1.95 N) and with a desired tangential velocity. The experiments confirmed the effectiveness of the learning hybrid force and position controller.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

This paper presents a unified control framework for both set-point and time-varying force control of robot manipulator by introducing an improved position-based impedance control (IPBIC). In order to essentially achieve accurate force control, especially time-varying force tracking, a new target impedance function compensated by a force controller is presented. The essence of the improved method in realizing time-varying force tracking, as well as the coupled stability of the manipulator–environment system is investigated. To further improve the force control performance, the Newton-type iterative learning control (ILC) is introduced upon the closed-loop system. A case study on a two-link robot model demonstrates the effectiveness of this method.

Fuzzy Predictive Algorithms Applied to Force Control of Robotic Manipulators

Some types of learning control were developed for a class of robot manipulators to realize a desired motion. In those control schemes, the actuator input to the robot is modified by the acceleration error signal or the velocity error signal so as to make the robot motion approach the desired one by repeating operation of the robot. However, since actual measured signals of those variables are apt to be contaminated by noise, it is desirable to correct the input by an actual position signal which is more accurately measured than the acceleration or velocity signal. Motivated by this observation, a new type of learning control is proposed, in which the input is modified by the position error signal. It is theoretically shown that by using this control method the robot motion converges to the desired one with repetition of maneuvering the robot. It is also shown that this learning method can be applied to not only position control but also force control of robot manipulators. Moreover, it is experimentally shown by using such a learning method that the real robot can be trained to polish a curved surface of an object and eventually carry the task to fulfillment.

A new classification scheme for robotic force control algorithms is presented. It is shown that algorithms can be categorized according to whether they are joint-based or cartesian-based. Further, they can be grouped according to whether the innermost servo loop is based on torque, velocity, or position. For useful force control, closing an inner loop on position can greatly increase disturbance rejection while also allowing for a split rate servo controller to minimize the computational burden. Experimental results from the implementation of a new force feedback method are presented. This cartesian-based method uses an inner loop closed on position and has been implemented on an AdeptOne robot. Bias force application, force searching, and selective compliance about a synthetic remote center compliance (RCC) have all been implemented. Excellent results in free space and in hard contact have been achieved. This technique has been successfully used to assemble parts having 0.001 inch clearances.

This paper presents an experimental evaluation and comparison of basic strategies that have been proposed for force control of robot manipulators. This experimental review of force control methodologies is unique in its breadth - never has such a complete spectrum of strategies been experimentally compared on the same system. The commonality amongst the experiments has permitted the ability to objectively compare and contrast these strategies, and draw conclusions about the efficacy of each. As will be seen, the results support our previous analysis and show the superiority of integral force control for force trajectory tracking.

_This paper presents an effective adaptive neural network feedback controller for force control of robot manipulators in an unknown environment by applying damping neurons which possess elastic-viscous properties. The unexpected overshooting and oscillation caused by the unknown and/or unmodeled dynamics of a robot manipulator and an environment can be decreased efficiently by the effect of the proposed damping neurons. Furthermore, a fuzzy controlled evaluation function is applied for the learning of the proposed neural network controller, so that the controller is able to adapt to the unknown environment more effectively. The effectiveness of the proposed neural network controller is evaluated by experiment with a 3 d.o.f. direct-drive planar robot manipulator.

This article presents a comparative study between the hybrid impedance control approach and the impedance force control approach with neural networks compensation of manipulator modeling errors. The proposed algorithm consists of an outer hybrid impedance control loop that generates the reference (target) acceleration to an inner inverse dynamics control loop. In order to improve the controller robustness, a compensation action of the manipulator modeling errors is introduced, acting on the target acceleration. This compensation action is based on a neural network model achieved by minimizing the modeling errors along the manipulator trajectory. The neural network algorithm uses an error training signal to model errors, that is minimized along the trajectory. The performance of the hybrid impedance control system with neural network compensation, is compared with the impedance force controller with neural network compensation, and is illustrated by computer simulations with a two degree-of-freedom PUMA 560 robot, whose end-effector is forced to move along a frictionless surface located perpendicular to the horizontal plane. The results obtained reveal even better performance when the desired force profile is not constant along the trajectory. This situation is very important in fine motion tasks where the desired force must have a varying profile along the trajectory, namely at the beginning and the end of the task. The results have also shown accurate force tracking and position control, even when significant uncertainties in the robot dynamic model are assumed.

This book is the second edition of a textbook published in 1996 by McGraw Hill and originates from a graduate level course given by the authors at the University of Naples. The topics include kinematics, statics and dynamics of robot manipulators together with trajectory planning and active control. There are only minor additions the first edition, which are mainly the use of quaternion to describe the orientation of the end effector and a short description of a closed chain architecture for a manipulator (parallelogram arm). The book is largely devoted to serial manipulators, with special developments about active control including adaptative control, robust controls and stability analysis. Another strength of this book is the great number of problems proposed at the end of each chapter, together with a list of references related to it. The fundamental features covered by the text are illustrated on simple examples of serial manipulators (two-link planar arm, parallelogram arm) including analytical results and numerical tests.The book has nine chapters followed by three appendices. The first appendix is devoted to linear algebra, the second recalls some fundamental aspects of rigid body mechanics and the third gives some basic principles of feedback control of linear systems.Chapter one is an introduction to the study of robot manipulators, giving an interesting classification of their architectures, the corresponding workspace and describes the tasks for which theyare used. After some standard examples of industrial manipulators, bibliographical reference texts are proposed, including textbooks on modelling and control of robots, general books on robotics, specialized texts, scientific robotic reviews and some international conferences on robotics.Chapters two, three and four are devoted to mechanical modelling of robot manipulators. The fundamental basics of kinematics are given in chapter two, including the representation of finite rotations by Euler angles or unit quaternions, homogeneous transformations, Denavit-Hartenberg parameters and workspace. The direct and inverse kinematical problems are solved in analytical form for some typical manipulator structures. The differential kinematics of robots are presented in chapter three, with an introduction to the geometric and analytic Jacobian matrices, kinematic singularities and redundancy. The inverse kinematic problem is presented, with special attention to the case of redundant robots where the solution is obtained by a linear optimization problem leading to the introduction of the pseudo-inverse Jacobian matrix and to the solution of several objectives such as avoidance of collision with an obstacle or moving away from singularities. Several inverse kinematics algorithms are given wih an interesting application to a three-link planar arm. Finally, a property of kineto-statics duality is deduced from the principle of virtual work applied to an equilibrium configuration of the robot. Chapter four is a standard presentation of the derivation of the dynamical model by Lagrange formulation and then by the Newton-Euler method. In the Lagrange formulation method, the linearity with respect to inertial parameters is shown and a detailed formulation of the dynamical model is obtained for a two-link Cartesian arm, a two-link planar arm and a parallelogram arm. The problem of dynamic parameter identification is also briefly presented from a numerical point of view. The recursive algorithm constructed from the Newton-Euler formulation is presented and illustrated by considering a two-link planar arm. Finally, the operational space dynamic model is introduced.In chapter five, paths and trajectory planning in joints and in operational spaces are presented; several classical methods of interpolation are described.Chapter six is an extensive study of active control of manipulators. Several methods are presented, involving classical independent joint control, non-linear centralized control, robust control and adaptative control. Both joint-space control and operational-space control are studied together with stability analysis by using Liapounoff functions. An interesting application to the two-link planar arm already used shows the comparison between various control schemes.Chapter seven deals with interaction control of serial manipulators with the working environment. Several strategies involving compliance control, impedance control, force control and hybrid control are presented.Chapter eight describes the actuators and the sensors used in robotics. Several types of servomotors (electric and hydraulic) are presented, together with the model giving their input/output relationship. Several kinds of sensors are also described including encoders, tachometers, force and vision sensors.The last chapter gives a short presentation of the functional architecture of a robot's control system, including characteristics of the programing environment and the hardware architecture.In conclusion, the book provides a good insight about simulation and control of robot manipulators, with a detailed study of the various control strategies and several interesting and pedagogical applications. This book is an excellent review of the standard knowledge needed not only for graduate students but also for researchers interested in robot manipulators.M Pascal

Force Control of Robot Manipulators

Machines at low velocity exhibit stick-slip motion. This phenomena is of particular importance in force control of robot manipulators because much of the force correcting action can occur in the low velocity regime of stick-slip. Experimental work has mapped out the structure of Stribeck friction, a non-linear low-velocity friction effect that contributes to and perhaps dominates stick-slip. In this paper I explore the implications of Stribeck friction for feedback control, in particular, for a system with Stribeck friction the following are examined: • The minimum velocity below which stick-slip will occur; • Accuracy of sensing required to eliminate stick-slip by feedback control; • The jerk size during stick-slip motion; • Mechanical attributes that will affect Stribeck friction.

External force at the end-effector is often required for the force control of robotic manipulator. In some cases, the force information measured by wrist force sensor consists of the external force and the undesired inertial force arising from the acceleration of the end-effector. Up to now, il: is still difficult to extract the external force exactly for the insufficiency of acceleration information. A novel six-axis accelerometer in. the type of dual annular membranes structure is presented in this paper. It can simultaneously measure all three linear acceleration components a <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</sub> , a <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">y</sub> , a <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">z</sub> , and three angular acceleration components a <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</sub> , a <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">y</sub> , a <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">z</sub> which can be used to extract the external forces. The Finite Element Method (FEM) simulations were carried out to optimize the configuration of strain gauges. The analysis of the rated strains and interference strains indicates that the accelerometer has a low level cross-sensitivity. The characteristic experiment shows that the linearity of the accelerometer is good with minor interference errors. It also reveals that the rated strains and interference strains are in good correspondence with the results from the FEM simulations.

Design of a digital adaptive algorithm for torque-controlled robotic joints

In this paper, we propose an adaptive nonlinear strategy for the motion and force control of manipulators with flexible joints. Our approach provides force control when in contact and robust motion control in its absence, all without the need for a control switch. This self-tuning behaviour for mixed contact/non-contact scenarios results from a unified formulation of force and motion control, with an integral transpose-based inverse kinematics core and adaptive update laws to cope with the manipulator flexibility and the contact stiffnesses. The global boundedness of all signals and the asymptotic stability of this controller are guaranteed via Lyapunov analysis. Finally, we validate its applicability experimentally by using low-cost hardware in a realistic mixed-contact scenario, demonstrating low computational demand.

A new Cartesian position control called resolved motion force control (RMFC) has been developed for robot manipulator. It adapts force convergence concept to control the Cartesian force and position instead of classical joint control. In this way, the changing configurations, gravity loading, and internal friction of the manipulator can be automatically compensated. Also, the method only uses the Jacobian transpose matrix such that it is applicable to more than six degrees-of-freedom manipulator without increasing the computational burden.