Adaptive operation-space control of redundant manipulators with joint limits avoidance
29 Mar 2018-pp 358-363
TL;DR: Neural networks are constructed under the proposed torque control design as to approximate the unknown dynamics and achieve small tracking errors in the operation-space to cope with the unknown and unstructured dynamic nonlinearities of the robot model.
Abstract: In this paper, adaptive operation-space control with joint limits avoidance is proposed for a redundant robot manipulator. Utilizing redundant properties of the robotic manipulator, joint limits avoidance is achieved without interfering the main-task objective in the operation-space. Two control objectives are unified under one common control framework. To cope with the unknown and unstructured dynamic nonlinearities of the robot model, neural networks (NNs) are constructed under the proposed torque control design as to approximate the unknown dynamics and achieve small tracking errors. Simulation studies are carried out to verify the effectiveness of the proposed framework.
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TL;DR: Through simulation and experimental studies, it is found that the robot can autonomously adjust its trajectory or interaction mode when the environment types or cost function parameters are tuned, so that a more humanized and intelligent interaction can be realized.
Abstract: In this paper, we propose an adaptive impedance control with reference trajectory learning for the robots interacting with unknown environment. A cost function considering its tracking errors and interaction force is introduced and a reference trajectory learning law based on iterative learning is presented to minimize it. Also, an adaptive impedance control is designed to follow the target impedance model with the adaptive reference trajectory to implement the convergence of tracking errors and interaction force. Through simulation and experimental studies, we find that the robot can autonomously adjust its trajectory or interaction mode when the environment types or cost function parameters are tuned, so that a more humanized and intelligent interaction can be realized.
7 citations
Cites background from "Adaptive operation-space control of..."
...Property 2 ( [27]): The matrix Cx(q(t), q̇(t))− 1 2Ṁx(q(t)) is skew-symmetric, i....
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...Property 1 ( [27]): The matrixMx(q(t)) is symmetric and positive definite....
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TL;DR: An acceleration-level tri-criteria optimization motion planning scheme is proposed, which combines the minimum acceleration norm, repetitive motion planning, and infinity-norm acceleration minimization solutions via weighting factor to resolve joint-angle drift problem of dual redundant manipulators.
Abstract: In order to solve joint-angle drift problem of dual redundant manipulators at acceleration-level, an acceleration-level tri-criteria optimization motion planning (ALTC-OMP) scheme is proposed, which combines the minimum acceleration norm, repetitive motion planning, and infinity-norm acceleration minimization solutions via weighting factor. This scheme can resolve the joint-angle drift problem of dual redundant manipulators which will arise in single criteria or bi-criteria scheme. In addition, the proposed scheme considers joint-velocity joint-acceleration physical limits. The proposed scheme can not only guarantee joint-velocity and joint-acceleration within their physical limits, but also ensure that final joint-velocity and joint-acceleration are near to zero. This scheme is realized by dual redundant manipulators which consist of left and right manipulators. In order to ensure the coordinated operation of manipulators, two motion planning problems are reformulated as two general quadratic program (QP) problems and further unified into one standard QP problem, which is solved by a simplified linear-variational-inequalities-based primal-dual neural network at the acceleration-level. Computer-simulation results based on dual PUMA560 redundant manipulators further demonstrate the effectiveness and feasibility of the proposed ALTC-OMP scheme to resolve joint-angle drift problem arising in the dual redundant manipulators.
3 citations
Cites background from "Adaptive operation-space control of..."
...Equation (12) is the forward kinematics equation of the left robot manipulator of dual manipulators....
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TL;DR: In this paper , a quadratic programming (QP) scheme is elaborated to achieve the primary tracking control task of redundant manipulators as well as joint limits avoidance task, and a gradient neurodynamics (GND) model is utilized to estimate the kinematics of redundant manipulation.
Abstract: Redundant manipulators could be efficient tools in industrial production as a result of their dexterity. However, existing kinematic control methods for redundant manipulators have two main disadvantages. On one hand, model uncertainties or unknown kinematic parameters may degrade the performance of existing model-based control methods subject to joint limits. On the other hand, existing model-free control methods ignore the existence of joint limits although they do not need to know kinematic models of redundant manipulators. In this paper, a quadratic programming (QP) scheme is elaborated to achieve the primary tracking control task of redundant manipulators as well as joint limits avoidance task. Besides, a gradient neurodynamics (GND) model is utilized to estimate the kinematics of redundant manipulators. Then, a primal dual neural network, which is employed to solve the QP problem, and the GND model are integrated towards developing a model-free control method constrained by joint angle and velocity limits for redundant manipulators. The visual sensory feedback is fed to the two neural networks. The efficacy of the proposed control method is demonstrated by extensive simulations and experiments, and the merits of the proposed method are also substantiated by comparisons.
2 citations
References
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01 Sep 1998
TL;DR: 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.
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.
1,337 citations
"Adaptive operation-space control of..." refers background in this paper
...Property 2: [10] Matrix 2C(q(t), q̇(t)) − Ṁ(q(t)) is a skew-symmetric matrix if C(q(t), q̇(t)) is in the Christoffel form, i....
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TL;DR: Adaptive neural network control for the robotic system with full-state constraints is designed, and the adaptive NNs are adopted to handle system uncertainties and disturbances.
Abstract: This paper studies the tracking control problem for an uncertain ${n}$ -link robot with full-state constraints The rigid robotic manipulator is described as a multiinput and multioutput system Adaptive neural network (NN) control for the robotic system with full-state constraints is designed In the control design, the adaptive NNs are adopted to handle system uncertainties and disturbances The Moore–Penrose inverse term is employed in order to prevent the violation of the full-state constraints A barrier Lyapunov function is used to guarantee the uniform ultimate boundedness of the closed-loop system The control performance of the closed-loop system is guaranteed by appropriately choosing the design parameters Simulation studies are performed to illustrate the effectiveness of the proposed control
1,021 citations
TL;DR: Adapt neural control schemes are proposed for two classes of uncertain multi-input/multi-output (MIMO) nonlinear systems in block-triangular forms that avoid the controller singularity problem completely without using projection algorithms.
Abstract: In this paper, adaptive neural control schemes are proposed for two classes of uncertain multi-input/multi-output (MIMO) nonlinear systems in block-triangular forms. The MIMO systems consist of interconnected subsystems, with couplings in the forms of unknown nonlinearities and/or parametric uncertainties in the input matrices, as well as in the system interconnections without any bounding restrictions. Using the block-triangular structure properties, the stability analyses of the closed-loop MIMO systems are shown in a nested iterative manner for all the states. By exploiting the special properties of the affine terms of the two classes of MIMO systems, the developed neural control schemes avoid the controller singularity problem completely without using projection algorithms. Semiglobal uniform ultimate boundedness (SGUUB) of all the signals in the closed-loop of MIMO nonlinear systems is achieved. The outputs of the systems are proven to converge to a small neighborhood of the desired trajectories. The control performance of the closed-loop system is guaranteed by suitably choosing the design parameters. The proposed schemes offer systematic design procedures for the control of the two classes of uncertain MIMO nonlinear systems. Simulation results are presented to show the effectiveness of the approach.
771 citations
"Adaptive operation-space control of..." refers background in this paper
...2 in [12], (45) indicates that all closedloop signals η1, η2 and {W̃} are uniformly bounded....
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Book•
09 Dec 1998
TL;DR: The text has been tailored to give a comprehensive study of robot dynamics, present structured network models for robots, and provide systematic approaches for neural network based adaptive controller design for rigid robots, flexible joint Robots, and robots in constraint motion.
Abstract: There has been considerable research interest in neural network control of robots, and satisfactory results have been obtained in solving some of the special issues associated with the problems of robot control in an "on-and-off" fasion. This text is dedicated to issues on adaptive control of robots based on neural networks. The text has been tailored to give a comprehensive study of robot dynamics, present structured network models for robots, and provide systematic approaches for neural network based adaptive controller design for rigid robots, flexible joint robots, and robots in constraint motion. Rigorous proof of the stability properties of adaptive neural network controllers is provided. Simulation examples are also presented to verify the effectiveness of the controllers, and practical implementation issues associated with the controllers are also discussed.
516 citations
"Adaptive operation-space control of..." refers background or methods in this paper
...Nevertheless, as the control input designed in (32) incorporates unknown components M(q(t)), C(q(t), q̇(t)) and G(q(t)), RBFNNs are constructed and the GL operator [9] is utilized to express P (q(t), q̇(t), vr, v̇r(t)) as below...
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...Property 1: [9] Matrix M(q(t)) is symmetric and positive definite....
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