Topic

# Motion control

About: Motion control is a(n) research topic. Over the lifetime, 22236 publication(s) have been published within this topic receiving 359372 citation(s).

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TL;DR: It is shown that components of the manipulator impedance may be combined by superposition even when they are nonlinear, and a generalization of a Norton equivalent network is defined for a broad class of nonlinear manipulators which separates the control of motion from theControl of impedance while preserving the superposition properties of the Norton network.

Abstract: Manipulation fundamentally requires the manipulator to be mechanically coupled to the object being manipulated; the manipulator may not be treated as an isolated system. This three-part paper presents an approach to the control of dynamic interaction between a manipulator and its environment. In Part I this approach is developed by considering the mechanics of interaction between physical systems. Control of position or force alone is inadequate; control of dynamic behavior is also required. It is shown that as manipulation is a fundamentally nonlinear problem, the distinction between impedance and admittance is essential, and given the environment contains inertial objects, the manipulator must be an impedance. A generalization of a Norton equivalent network is defined for a broad class of nonlinear manipulators which separates the control of motion from the control of impedance while preserving the superposition properties of the Norton network. It is shown that components of the manipulator impedance may be combined by superposition even when they are nonlinear.

3,091 citations

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06 Jun 1984TL;DR: In this paper, a unified approach to kinematically constrained motion, dynamic interaction, target acquisition and obstacle avoidance is presented, which results in a unified control of manipulator behaviour.

Abstract: Manipulation fundamentally requires a manipulator to be mechanically coupled to the object being manipulated. A consideration of the physical constraints imposed by dynamic interaction shows that control of a vector quantity such as position or force is inadequate and that control of the manipulator impedance is also necessary. Techniques for control of manipulator behaviour are presented which result in a unified approach to kinematically constrained motion, dynamic interaction, target acquisition and obstacle avoidance.

3,079 citations

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01 Feb 1987TL;DR: A framework for the analysis and control of manipulator systems with respect to the dynamic behavior of their end-effectors is developed, and the unified approach for motion and force control is developed.

Abstract: A framework for the analysis and control of manipulator systems with respect to the dynamic behavior of their end-effectors is developed. First, issues related to the description of end-effector tasks that involve constrained motion and active force control are discussed. The fundamentals of the operational space formulation are then presented, and the unified approach for motion and force control is developed. The extension of this formulation to redundant manipulator systems is also presented, constructing the end-effector equations of motion and describing their behavior with respect to joint forces. These results are used in the development of a new and systematic approach for dealing with the problems arising at kinematic singularities. At a singular configuration, the manipulator is treated as a mechanism that is redundant with respect to the motion of the end-effector in the subspace of operational space orthogonal to the singular direction.

2,628 citations

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20 Nov 2008

TL;DR: Robotics provides the basic know-how on the foundations of robotics: modelling, planning and control, suitable for use in senior undergraduate and graduate courses in automation and computer, electrical, electronic and mechanical engineering courses with strong robotics content.

Abstract: The classic text on robot manipulators now covers visual control, motion planning and mobile robots too! Robotics provides the basic know-how on the foundations of robotics: modelling, planning and control. The text develops around a core of consistent and rigorous formalism with fundamental and technological material giving rise naturally and with gradually increasing difficulty to more advanced considerations. The theory of manipulator structures presented in the early part of the book encompasses: the fundamentals: kinematics, statics and trajectory planning; and the technology of actuators, sensors and control units. Subsequently, more advanced instruction is given in: dynamics and motion control of robot manipulators; mobile robots; motion planning; and interaction with the environment using exteroceptive sensory data (force and vision). Appendices ensure that students will have access to a consistent level of background in basic areas such as rigid-body mechanics, feedback control, and others. Problems are raised and the proper tools established to find engineering-oriented solutions rather than to focus on abstruse theoretical methodology. To impart practical skill, more than 60 examples and case studies are carefully worked out and interwoven through the text, with frequent resort to simulation. In addition, nearly 150 end-of-chapter problems are proposed, and the book is accompanied by a solutions manual (downloadable from www.springer.com/978-1-84628-641-4) containing the MATLAB code for computer problems; this is available free of charge to those adopting Robotics as a textbook for courses. This text is suitable for use in senior undergraduate and graduate courses in automation and computer, electrical, electronic and mechanical engineering courses with strong robotics content.

2,140 citations

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TL;DR: A modular approach to motor learning and control based on multiple pairs of inverse (controller) and forward (predictor) models that can simultaneously learn the multiple inverse models necessary for control as well as how to select the inverse models appropriate for a given environment is proposed.

Abstract: Humans demonstrate a remarkable ability to generate accurate and appropriate motor behavior under many different and often uncertain environmental conditions. In this paper, we propose a modular approach to such motor learning and control. We review the behavioral evidence and benefits of modularity, and propose a new architecture based on multiple pairs of inverse (controller) and forward (predictor) models. Within each pair, the inverse and forward models are tightly coupled both during their acquisition, through motor learning, and use, during which the forward models determine the contribution of each inverse model's output to the final motor command. This architecture can simultaneously learn the multiple inverse models necessary for control as well as how to select the inverse models appropriate for a given environment. Finally, we describe specific predictions of the model, which can be tested experimentally.

1,996 citations