Kristin Glass

Bio: Kristin Glass is an academic researcher from New Mexico State University. The author has contributed to research in topics: Adaptive control & Control theory. The author has an hindex of 17, co-authored 89 publications receiving 991 citations. Previous affiliations of Kristin Glass include United States Department of Energy.

Direct adaptive impedance control of robot manipulators

Abstract: This article presents an adaptive scheme for controlling the end-effector impedance of robot manipulators. The proposed control system consists of three subsystems: a simple “filter” that characterizes the desired dynamic relationship between the end-effector position error and the end-effector/environment contact force, an adaptive controller that produces the Cartesian-space control input required to provide this desired dynamic relationship, and an algorithm for mapping the Cartesian-space control input to a physically realizable joint-space control torque. The controller does not require knowledge of either the structure or the parameter values of the robot dynamics and is implemented without calculation of the robot inverse kinematic transformation. As a result, the scheme represents a general and computationally efficient approach to controlling the impedance of both nonredundant and redundant manipulators. Furthermore, the method can be applied directly to trajectory tracking in free-space motion by removing the impedance filter. Computer simulation results are given for a planar four degree-of-freedom redundant robot under adaptive impedance control. These results demonstrate that accurate end-effector impedance control and effective redundancy utilization can be achieved simultaneously by using the proposed controller.

Real-time collision avoidance for redundant manipulators

Abstract: This paper presents a simple and robust approach to achieving collision avoidance for kinematically redundant manipulators at the control-loop level. The proposed scheme represents the obstacle avoidance requirement as inequality constraints in the manipulator workspace, and ensures that these inequalities are satisfied while the end-effector tracks the desired trajectory. The control scheme is the damped-least-squares formulation of the configuration control approach implemented as a kinematic controller. Computer simulation and experimental results are given for a Robotics Research 7 DOF redundant arm and demonstrate the collision avoidance capability for reaching inside a truss structure. These results confirm that the proposed approach provides a simple and effective method for real-time collision avoidance. >

Adaptive compliant motion control for dexterous manipulators

Abstract: This article presents two adaptive schemes for compliant motion control of dexterous manipulators. The first scheme is developed using an adaptive impedance control approach for torque-controlled manipulators, whereas the second strategy is an adaptive admittance controller for position-controlled manipulators. The proposed controllers are very general and computationally efficient, as they do not require knowledge of the manipulator dynamic model or parameter values of the manipulator or the environment and are implemented without calculation of the inverse dynamics or inverse kinematic trans formation. It is shown that the control strategies are globally stable in the presence of bounded disturbances and that in the absence of disturbances the ultimate bound on the size of the system errors can be made arbitrarily small. The capabilities of the proposed control schemes are illustrated through both computer simulations and laboratory experiments with a dexterous Robotics Research Corporation seven-degrees-of-freedom (DOF) manipulator. 17 refs., 4 figs.

Global regulation of uncertain manipulators using bounded controls

Abstract: This paper considers the position regulation problem for uncertain robot manipulators in the presence of constraints on the available actuator torques, and proposes two new controllers as solutions to this problem. The first controller is derived under the assumption that the manipulator state is measurable, while the second strategy is developed for those applications in which only position measurements are available. Each scheme consists of a nonadaptive component for gross position control and an adaptive component to ensure convergence to the desired position. The controllers are computationally simple, require very little information regarding the manipulator model or the payload, and ensure that the position error is globally convergent. The capabilities of the proposed control strategies are illustrated though both computer simulations and laboratory experiments with an IMI Zebra Zero manipulator.

Adaptive control of nonholonomic mechanical systems

Abstract: This paper considers the problem of controlling nonholonomic mechanical systems in the presence of incomplete information concerning the system model and state, and presents a class of adaptive controllers as a solution to this problem. The proposed control strategies provide simple and robust solutions to a number of important nonholonomic system control problems, including stabilization to an equilibrium manifold, stabilization to an equilibrium point, and trajectory tracking control. All of the schemes are computationally efficient, are implementable without system dynamic model or rate information, and ensure uniform boundedness of all signals and accurate motion control.

Neural Network Control of Robot Manipulators and Nonlinear Systems

Abstract: From the Publisher: This graduate text provides an authoritative account of neural network (NN) controllers for robotics and nonlinear systems and gives the first textbook treatment of a general and streamlined design procedure for NN controllers. Stability proofs and performance guarantees are provided which illustrate the superior efficiency of the NN controllers over other design techniques when the system is unknown. New NN properties, such as robustness and passivity are introduced, and new weight tuning algorithms are presented. Neural Network Control of Robot Manipulators and Nonlinear Systems provides a welcome introduction to graduate students, and an invaluable reference to professional engineers and researchers in control systems.

Robot Manipulator Control: Theory and Practice

Abstract: Robot Manipulator Control offers a complete survey of control systems for serial-link robot arms and acknowledges how robotic device performance hinges upon a well-developed control system. Containing over 750 essential equations, this thoroughly up-to-date Second Edition, the book explicates theoretical and mathematical requisites for controls design and summarizes current techniques in computer simulation and implementation of controllers. It also addresses procedures and issues in computed-torque, robust, adaptive, neural network, and force control. New chapters relay practical information on commercial robot manipulators and devices and cutting-edge methods in neural network control.

Adaptive Neural Impedance Control of a Robotic Manipulator With Input Saturation

Abstract: In this paper, adaptive impedance control is developed for an ${n}$ -link robotic manipulator with input saturation by employing neural networks. Both uncertainties and input saturation are considered in the tracking control design. In order to approximate the system uncertainties, we introduce a radial basis function neural network controller, and the input saturation is handled by designing an auxiliary system. By using Lyapunov’s method, we design adaptive neural impedance controllers. Both state and output feedbacks are constructed. To verify the proposed control, extensive simulations are conducted.

Exponential stabilization of driftless nonlinear control systems using homogeneous feedback

Abstract: This paper focuses on the problem of exponential stabilization of controllable, driftless systems using time-varying, homogeneous feedback. The analysis is performed with respect to a homogeneous norm in a nonstandard dilation that is compatible with the algebraic structure of the control Lie algebra. It can be shown that any continuous, time-varying controller that achieves exponential stability relative to the Euclidean norm is necessarily non-Lipschitz. Despite these restrictions, we provide a set of constructive, sufficient conditions for extending smooth, asymptotic stabilizers to homogeneous, exponential stabilizers. The modified feedbacks are everywhere continuous, smooth away from the origin, and can be extended to a large class of systems with torque inputs. The feedback laws are applied to an experimental mobile robot and show significant improvement in convergence rate over smooth stabilizers.