Journal ArticleDOI
Robust control of dynamically interacting systems
J.E. Colgate,Neville Hogan +1 more
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This paper describes an approach to the design of ‘interaction controllers’ and contrasts this with an Approach to the Design of Approaches toDynamic interaction with the environment is fundamental to the process of manipulation.Abstract:
Dynamic interaction with the environment is fundamental to the process of manipulation. This paper describes an approach to the design of ‘interaction controllers’ and contrasts this with an approa...read more
Citations
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Book ChapterDOI
Comparing Series Elasticity and Admittance Control for Haptic Rendering
TL;DR: In this paper, the tradeoffs between admittance control and series elastic actuators are explored with the use of analytical comparisons in the frequency domain backed up by experiments and complemented with a passivity analysis that accounts for an excess of passivity contributed by human biomechanics.
Proceedings ArticleDOI
Variable impedance control method for robot contact force based on TD3 algorithm
TL;DR: In this article , a variable impedance control method for contact forces based on the TD3 (Twin Delayed Deep Deterministic Policy Gradient) algorithm is presented, which builds on the original impedance model to obtain the optimal impedance parameters.
Journal ArticleDOI
Manipulation at optimum locations for maximum force transmission with mobile robots under environmental disturbances
TL;DR: In this article , a forward dynamic controller is implemented to eliminate undesired excessive motions near singular joint configurations and a reset control algorithm along with an admittance type controller are developed for stable interaction with an unknown object under environmental disturbances.
Proceedings ArticleDOI
Reinforcement Learning-Based Impedance Learning for Robot Admittance Control in Industrial Assembly
TL;DR: In this paper , an impedance learning method using reinforcement learning (RL) for robot admittance control in industrial assembly is presented, where a model-free RL method termed twin delayed deep deterministic policy gradient is utilized to tune stiffness parameters, guaranteeing the safety of robots and environments during the completion of assembly tasks.
Journal ArticleDOI
Converse negative imaginary theorems
TL;DR: In this article , the authors derived necessary and sufficient conditions for a feedback system to be robustly stable against various types of negative imaginary (NI) uncertainty, and established a non-existence result that no stable system can robustly stabilise all marginally stable NI uncertainty.
References
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Book
Linear Optimal Control Systems
Huibert Kwakernaak,Raphael Sivan +1 more
TL;DR: In this article, the authors provide an excellent introduction to feedback control system design, including a theoretical approach that captures the essential issues and can be applied to a wide range of practical problems.
Book
Introduction to physical system dynamics
R. Robert Rosenberg,Dean Karnopp +1 more
TL;DR: The SYSKIT is a linear system software toolkit that contains a highly integrated set of programs with applications to system dynamics, controls and vibrations.
Dissertation
Practical robustness measures in multivariable control system analysis
TL;DR: A very general robustness theorem is presented from which all of the robustness tests dealing with specific model errors may be derived and are able to guarantee feedback system stability in the face of model errors of larger magnitude.
Dissertation
Stability robustness of impedance controlled manipulators coupled to passive environments
TL;DR: Thesis. as discussed by the authors, Mass. Institute of Technology, Dept. of Mechanical Engineering, Boston, Massachusetts, U.S.A. (M.S., 1987).
Practical robustness measures in multivariable control system analysis. Ph.D. Thesis
TL;DR: In this paper, the robustness of linear time invariant feedback control systems with respect to model uncertainty is considered using frequency domain criteria, and robustness tests are unified under a common framework based on the nature and structure of model errors.