Results 1  10
of
29
A Hybrid Architecture for Adaptive Robot Control
, 2000
"... The autonomous operation of robot systems in an uncertain environment poses many challenges to their control architecture. Such systems must be reactive with respect to local disturbances and uncertainties and have to adapt to more persistent changes in environmental conditions and task requirements ..."
Abstract

Cited by 38 (0 self)
 Add to MetaCart
The autonomous operation of robot systems in an uncertain environment poses many challenges to their control architecture. Such systems must be reactive with respect to local disturbances and uncertainties and have to adapt to more persistent changes in environmental conditions and task requirements. In autonomous systems, this adaptation must often occur without outside intervention and within a single trial while avoiding catastrophic failure. This dissertation
Nonlinear Control of Hydraulic Robots
 IEEE Transactions on Robotics and Automation
, 2001
"... This paper addresses the control problem of hydraulic robot manipulators. The backstepping design methodology is adopted to develop a novel nonlinear position tracking controller. The tracking errors are shown to be exponentially stable under the proposed control law. The controller is further augme ..."
Abstract

Cited by 20 (0 self)
 Add to MetaCart
(Show Context)
This paper addresses the control problem of hydraulic robot manipulators. The backstepping design methodology is adopted to develop a novel nonlinear position tracking controller. The tracking errors are shown to be exponentially stable under the proposed control law. The controller is further augmented with adaptation laws to compensate for parametric uncertainties in the system dynamics. Acceleration feedbackisavoided by using two new adaptive and robust sliding type observers. The adaptive controllers are proven to be asymptotically stable via Lyapunov analysis. Simulation and experimental results performed with a hydraulic Stewart platform demonstrate the e#ectiveness of the approach.
Stability Guaranteed Control: Time Domain Passivity Approach
 IEEE Trans. on Control Systems Technology
, 2004
"... Abstract—A general framework for expanding the timedomain passivity control approach [12], [24] to large classes of control systems is proposed. We show that large classes of control systems can be described from a network point of view. Based on the network presentation, the large classes of contr ..."
Abstract

Cited by 13 (6 self)
 Add to MetaCart
(Show Context)
Abstract—A general framework for expanding the timedomain passivity control approach [12], [24] to large classes of control systems is proposed. We show that large classes of control systems can be described from a network point of view. Based on the network presentation, the large classes of control systems are analyzed in a unified framework. In this unified network model, we define “virtual input energy, ” which is a virtual source of energy for control, and “real output energy ” that is physically transferred to a plant to allow the concept of passivity to be used to study the stability of large classes of control systems. For guaranteeing the stability condition, the timedomain passivity controller for twoport [24] is applied. Design procedure is demonstrated for a motion control system. The developed method is tested with numerical simulation in the regulation of a single link flexible manipulator. Totally stable control is achieved under wide variety of operating condition and uncertainties without any model information. Index Terms—Passivity controller, passivity observer, stability guaranteed control, timedomain passivity. I.
An optimal control approach to robust control of robot manipulators
, 1998
"... We present a new optimal control approach to robust control of robot manipulators in the framework of Lin et al [7]. Because of the unknown load placed on a manipulator and the other uncertainties in the manipulator dynamics, it is important to design a robust control law that will guarantee the per ..."
Abstract

Cited by 13 (1 self)
 Add to MetaCart
We present a new optimal control approach to robust control of robot manipulators in the framework of Lin et al [7]. Because of the unknown load placed on a manipulator and the other uncertainties in the manipulator dynamics, it is important to design a robust control law that will guarantee the performance of the manipulator under these uncertainties. To solve this robust control problem, we first translate the robust control problem into an optimal control problem, where the uncertainties are reflected in the performance index. We then use the optimal control approach to solve the robust control problem. We show that the solution to the optimal control problem is indeed a solution to the robust control problem. We illustrate this approach using a twojoint SCARA type robot, whose robust control is obtained by solving an algebraic Riccati equation.
Learning to Exploit Dynamics for Robot Motor Coordination
, 2003
"... Humans exploit dynamicsgravity, inertia, joint coupling, elasticity, and so onas a regular part of skillful, coordinated movements. Such movements comprise everyday activities, like reaching and walking, as well as highly practiced maneuvers as used in athletics and the performing arts. Robo ..."
Abstract

Cited by 11 (2 self)
 Add to MetaCart
Humans exploit dynamicsgravity, inertia, joint coupling, elasticity, and so onas a regular part of skillful, coordinated movements. Such movements comprise everyday activities, like reaching and walking, as well as highly practiced maneuvers as used in athletics and the performing arts. Robots, especially industrial manipulators, instead use control schemes that ordinarily cancel the complex, nonlinear dynamics that humans use to their advantage. Alternative schemes from the machine learning and intelligent control communities offer a number of potential benefits, such as improved efficiency, online skill acquisition, and tracking of nonstationary environments. However, the success of such methods depends a great deal on structure in the form of simplifying assumptions, prior knowledge, solution constraints and other heuristics that bias learning. My premise
Modeling and PD Control of ClosedChain Mechanical Systems
, 1995
"... In this paper, we review the structure of a reduced model for closedchain mechanical systems originally proposed in [2], and highlight the fact that the model has two special characteristics which make it di#erent from models of openchain mechanical systems. First, it is defined locally, and sec ..."
Abstract

Cited by 7 (0 self)
 Add to MetaCart
(Show Context)
In this paper, we review the structure of a reduced model for closedchain mechanical systems originally proposed in [2], and highlight the fact that the model has two special characteristics which make it di#erent from models of openchain mechanical systems. First, it is defined locally, and second it is an implicit model. We show that closedchain systems satisfy the property of skew symmetry. We therefore propose PDbased control strategies with full as well as simple gravity compensation and discuss and compare the computational issues involved in the implementation of both controllers. 1 Introduction A closedchain mechanical system, referred to here as constrained system #, can be thought of as consisting of a free system # # to which constraints C are applied as shown in Figure 1 for a planar mechanism. The free system # is an n Figure 1: Free System, Constraints, and Constrained System degreeoffreedom (d.o.f) holonomic system which consists of a collection of rigid...
On the Uniform Boundedness of the Inertia Matrix of Serial Robot Manipulators
 J. Robotic Systems
, 1998
"... In the control of robot manipulators, it is customary to assume that the eigenvalues of the inertia matrix are uniformly bounded from below and above. However, in this paper it is shown that not all manipulators possess this property. The class of serial robot manipulators with bounded inertia matri ..."
Abstract

Cited by 5 (0 self)
 Add to MetaCart
(Show Context)
In the control of robot manipulators, it is customary to assume that the eigenvalues of the inertia matrix are uniformly bounded from below and above. However, in this paper it is shown that not all manipulators possess this property. The class of serial robot manipulators with bounded inertia matrix, referred to as class BD manipulators, is completely characterized and it is shown that it includes manipulators with nontrivial joint configurations. For manipulators of this class, easily computable uniform bounds for the minimum and maximum eigenvalues of the inertia matrix are provided. 1 Introduction The standard model for the dynamics of an nlink rigid robot manipulator is given by [9] D(q) q + C(q, q) q + g(q)=u, (1) where the nvectors q, q, and q represent the link angles, velocities and accelerations respectively, D(q)isthenninertia matrix, C(q, q) q represents the Coriolis and centrifugal terms, g(q) represents the gravitational terms, and u is the input vector. Al...
A nonlinear adaptive H∞ tracking control design in robotic systems via neural networks
 IEEE Trans. Control Syst. Technol. 1997
"... Abstract—An adaptive neuralnetwork tracking control with a guaranteedH1 performance is proposed for robotic systems with plant uncertainties and external disturbances. A neuralnetwork system is introduced to learn these unknown (or uncertain) dynamics by an adaptive algorithm. Moreover, the effect ..."
Abstract

Cited by 3 (0 self)
 Add to MetaCart
(Show Context)
Abstract—An adaptive neuralnetwork tracking control with a guaranteedH1 performance is proposed for robotic systems with plant uncertainties and external disturbances. A neuralnetwork system is introduced to learn these unknown (or uncertain) dynamics by an adaptive algorithm. Moreover, the effects on the tracking error due to the approximation error via the adaptive neural network must be attenuated to a prescribed level, i.e., an H 1 tracking performance is achieved. Hence, in this study, both the H1 tracking theory and adaptive neuralnetwork control scheme are combined together to achieve the nonlinear adaptive H 1 tracking control design for uncertain or unknown robotic systems. The developed control scheme is smooth and semiglobal as well as very simple and computationally efficient, since it does not require a knowledge of either the mathematical model or the parameterization of the robotic dynamics. Finally, extensive simulations are given to illustrate the tracking performance of a twolink robotic manipulator with the proposed adaptive neural H 1 control design. Index Terms—Nonlinear adaptive control, H1 tracking control, neural network, robotic control. I.
Sliding Mode Robust Control of Robot Manipulator in the Task Space by Support of Feedback Linearization and BackStepping Control
 World Applied Sciences Journal
, 2009
"... Abstract: A robust control approach is developed to control robot in the task space using sliding mode by support of feedback linearization control and backstepping method, in this study. The bounds of uncertainties applied in the sliding mode control are reduced by applying feedback linearization. ..."
Abstract

Cited by 2 (0 self)
 Add to MetaCart
(Show Context)
Abstract: A robust control approach is developed to control robot in the task space using sliding mode by support of feedback linearization control and backstepping method, in this study. The bounds of uncertainties applied in the sliding mode control are reduced by applying feedback linearization. This provides a robust control system with a less error. The backstepping method is used to define a linear slip surface and providing uniform ultimate boundedness stability purpose. A case of study is carried out on a two link elbow robot driven by electrical motors.
Robot Position Sensor Fault Tolerance
, 1997
"... Contents 1. INTRODUCTION AND LITERATURE SEARCH ........................................11 1.1 JUSTIFICATION FOR FAULT TOLERANCE IN ROBOTICS.................................................................11 1.2 GENERAL PURPOSE FAULT TOLERANCE...................................................... ..."
Abstract

Cited by 2 (2 self)
 Add to MetaCart
Contents 1. INTRODUCTION AND LITERATURE SEARCH ........................................11 1.1 JUSTIFICATION FOR FAULT TOLERANCE IN ROBOTICS.................................................................11 1.2 GENERAL PURPOSE FAULT TOLERANCE...................................................................................13 1.2.1 Failure detection and identification (FDI)..........................................................................13 1.2.2 Fault recovery.............................................................................................................13 1.3 ROBOT FAULT TOLERANCE ..................................................................................................14 1.3.1 Robot joint failure detection and identification ..................................................................14 1.3.2 Robot joint failure recovery...................