An Adaptive Model Following Control for Robotic Manipulators

Balestrino, Alessandro; Maria, G. De; Sciavicco, L.

doi:10.1115/1.3140646

Cited by 170 publications

(36 citation statements)

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“…The stability of regulation law that ensures the convergence of trajectory following was first attacked by Takegaki and Arimoto [21] under a system of linearized dynamics. The convergence under a non-linear system of robot dynamics has been proved by Balestrino et al [22] using Popov's hyperstability. All the proposed adaptive algorithms based on the MRAC scheme have the same burden of computational complexity.…”

Section: Continuous-path Control and Inverse Dynamicsmentioning

confidence: 96%

Design of robot control systems

Arimoto

1989

Advanced Robotics

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Design of robot control systems SUGURU ARIMOTOAbstract-An expository discussion on practical aspects of the design problems of robot control systems is presented. The first section discusses the present status of robot control methodology based on the so-called 'teaching and playback' control scheme. It is pointed out that PTP (point to point) control is still central in practice because only a sort of pulse-incremental servo controller is implemented for each joint actuator in actual industrial robots. The second section points out that servo controllers of this kind perform approximately as a PD or PID controller, and demonstrates that such PD and PID control schemes can work well even if the robot dynamics are non-linear and have strong couplings between the joint variables.The third section deals with path-tracking and trajectory-tracking control problems when teaching by human operators is not possible. This is then followed by a final but substantial section on recent results on learning control and adaptive control. An example of learning control for robot motions is given and its potential applicability in robotic systems is discussed.

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Section: Continuous-path Control and Inverse Dynamicsmentioning

confidence: 96%

Design of robot control systems

Arimoto

1989

Advanced Robotics

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“…Several' VS control algorithms have been proposed and studied in the literature (see for example Utkin 1978, Balestrino et al 1983, Ambrosino et al 1984, Slotine 1984, Burton and Zinober 1986, Buhler 1986. The main idea of these algorithms is to use a feedforward action to compensate the known part of the system, and then introduce a switching term in the control input in order to eliminate the undesired unknown disturbances.…”

Section: Introductionmentioning

confidence: 99%

Sliding mode using discontinuous control algorithms of integral type

Zanasi

1993

International Journal of Control

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“…The feedforward control law is therefore given by (13) in the frequency-domain; or by f 2 ( t ) = Cr( t ) + Bt( t ) + Ai;( t ) (14) in the time-domain. It must be noted that since the desired velocity f(t) and acceleration i;(t) are directly available, it is not necessary to perform differentiation in implementing the feedforward control law; and hence Q ( s ) is realizable.…”

Section: I58mentioning

confidence: 99%

“…This adaptation ensures that the robot control system is tuned online such that a good dynamic performance is achieved despite coefficient variations due to changes of the robot geometric configuration, speed of motion or the payload. Consider the nonlinear model of the end-effector dynamics written as [1,2,13] (22) where A*, B*, and C* are nxn matrices whose elements are (23) is a generalization of the "total" control law (21) developed in Section 2 based on the incremental analysis. As in equation (21), this control law is composed of three components; namely the "auxiliary input" E(t), the…”

Section: Direct Adaptive Control Of the End-effector Motionmentioning

confidence: 99%

An adaptive cartesian control scheme for manipulators

Seraji

Proceedings. 1987 IEEE International Conference on Robotics and Automation

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A new adaptive control scheme for direct control of manipulator end-effector to achieve trajectory tracking in Cartesian space is developed in this paper. 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 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. I n t r o d u c t i o nAlthough end-effector control is the ultimate goal of any robot control system, direct control of the end-effector motion in Cartesian space has not attracted much attention. Conventionally, task description is expressed in terms of a sequence of end-effector coordinates in the Cartesian space. This information is transformed through inverse kinematics to a series of angular positions in the joint space. End-effector control is then accomplished indirectly by controlling the joint angles which are related to the end-effector coordinates through forward kinematics. This indirect approach to end-effector control is both computationally inefficient and unduly complicated due to degeneracy of joint angles corresponding to a given endeffector condition. Furthermore, negligible errors in the joint angles may result in noticeable errors in the end-effector coordinates, depending on the manipulator geometry.Most existing methods for robot control in the joint space fall into the two major categories of "global linearization" and "adaptive control." In recent papers [1,2], Khatib has extended the global linearization method to the direct control of end-effector motion in the Cartesian space. This method is based on cancelling out the nonlinearities of the robot model by means of a nonlinear feedback controller and requires exact knowledge of the complex dynamic model of the robot. The method also needs accurate values of the robot parameters and the payload. When perfect cancellations of the robot nonlinearities is not achieved due to imperfect modeling or inaccurate parameter values, dynamic performance of the robot may be degraded and a complicated stability analysis is necessitated and this situation may ever1 lead to instability of the closedloop system [3 -41.Adaptive control offers an appealing solution to the robot control problem. In adaptive robot control methods, neither the complex mathematical model of the robot dynamics nor any knowledge of the robot's dynamic parameters or ,the payload are required in generating the control action. Adaptive control methods in general fall into two distinct categories; namely, indire...

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An Adaptive Model Following Control for Robotic Manipulators

Cited by 170 publications

References 0 publications

Design of robot control systems

Design of robot control systems

Sliding mode using discontinuous control algorithms of integral type

An adaptive cartesian control scheme for manipulators

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