Two-dimensional learning-control theory for robotic manipulators (original) (raw)
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Adaptive iterative learning control for robot manipulators
Automatica, 2004
In this paper, we propose some adaptive iterative learning control (ILC) schemes for trajectory tracking of rigid robot manipulators, with unknown parameters, performing repetitive tasks. The proposed control schemes are based upon the use of a proportional-derivative (PD) feedback structure, for which an iterative term is added to cope with the unknown parameters and disturbances. The control design is very simple in the sense that the only requirement on the PD and learning gains is the positive deÿniteness condition and the bounds of the robot parameters are not needed. In contrast to classical ILC schemes where the number of iterative variables is generally equal to the number of control inputs, the second controller proposed in this paper uses just two iterative variables, which is an interesting fact from a practical point of view since it contributes considerably to memory space saving in real-time implementations. We also show that it is possible to use a single iterative variable in the control scheme if some bounds of the system parameters are known. Furthermore, the resetting condition is relaxed to a certain extent for a certain class of reference trajectories. Finally, simulation results are provided to illustrate the e ectiveness of the proposed controllers. ?
An adaptive switching learning control method for trajectory tracking of robot manipulators
Mechatronics, 2006
In this paper, a new adaptive switching learning control approach, called adaptive switching learning PD control (ASL-PD), is proposed for trajectory tracking of robot manipulators in an iterative operation mode. The ASL-PD control method is a combination of the feedback PD control law with a gain switching technique and the feedforward learning control law with the input torque profile. The torque profile is updated by the previous torque profile (which makes sense for learning). Furthermore, in this new control method, the switching control scheme is integrated into the iterative learning procedure; as such, the trajectory tracking converges very fast. The ASL-PD method achieves the asymptotical convergence based on the LyapunovÕs method. The ASL-PD method possesses both adaptive and learning capabilities with a simple control structure. The simulation study validates this new method. In particular, both position and velocity tracking errors monotonically decrease with the increase of the number of iterations. The convergence rate with the ASL-PD method is faster than that of the adaptive iterative learning control method proposed by others in literature.
Transactions of the Institute of Measurement and Control, 2018
This paper deals with Iterative Learning Control (ILC) design to solve the trajectory tracking problem for rigid robot manipulators subject to external disturbances, and performing repetitive tasks. A high order ILC scheme is synthetized; this controller contains the information (errors) of several iterations and not only of one iteration. It has been shown that the closed loop system (robot plus controller) is asymptotically stable, over the whole finite time interval, when the iteration number tends to infinity. This proof is based upon the use of a Lyapunov-like positive definite sequence, which is shown to be monotonically decreasing under the proposed controller scheme. Finally, simulation results on two-link manipulator are provided to illustrate the effectiveness of the proposed controller.
Learning Control of Robot Manipulators
Journal of Dynamic Systems, Measurement, and Control, 1993
Learning control encompasses a class of control algorithms for programmable machines such as robots which attain, through an iterative process, the motor dexterity that enables the machine to execute complex tasks. In this paper we discuss the use of function identification and adaptive control algorithms in learning controllers for robot manipulators. In particular, we discuss the similarities and differences between betterment learning schemes, repetitive controllers and adaptive learning schemes based on integral transforms. The stability and convergence properties of adaptive learning algorithms based on integral transforms are highlighted and experimental results illustrating some of these properties are presented.
On the use of learning control for improved performance in robot control systems
1997 European Control Conference (ECC), 1997
Iterative learning control applied to a simpli ed model of a robot arm is studied. The iterative learning control input signal is used in combination with conventional feedback and feed-forward control, and the aim is to let the learning control signal handle the e ects of unmodeled dynamics and friction. Convergence and robustness aspects of the choice of lters in the updating scheme of the iterative learning control signal are studied.
On the iterative learning control theory for robotic manipulators
IEEE Journal on Robotics and Automation
A "high-gain feedback" point of view is considered within the iterative learning control theory for robotic manipulators. Basic results concerning the uniform boundedness of the trajectory errors are established, and a proof of convergence of the algorithm is given.
Further Results on Adaptive Iterative Learning Control of Robot Manipulators
Based on a combination of a PD controller and a switching type two-parameter compensation force, an iterative learning controller with a projection-free adaptive algorithm is presented in this paper for repetitive control of uncertain robot manipulators. The adaptive iterative learning controller is designed without any a priori knowledge of robot parameters under certain properties on the dynamics of robot manipulators with revolute joints only. This new adaptive algorithm uses a combined time-domain and iteration-domain adaptation law allowing to guarantee the boundedness of the tracking error and the control input, in the sense of the infinity norm, as well as the convergence of the tracking error to zero, without any a priori knowledge of robot parameters. Simulation results are provided to illustrate the effectiveness of the learning controller. ᭧
ANTICIPATIVE ITERATIVE LEARNING CONTROL OF ROBOT MANIPULATORS
An iterative learning scheme for the tracking control of robot manipulators without velocity measurement is presented. The proposed learning algorithm is anticipative (noncausal) in the sense that it utilizes "future" values of the tracking error obtained during the previous iteration. Also, the standard resetting assumption is relaxed to the form of δ q -resetting assumption. The proposed algorithm ensures convergence of the tracking error to a prescribed small domain in finite number of iterations, uniformly in time. Some experimental results on a six-degrees-of-freedom (6-DOF) robot manipulator are presented to show the effectiveness of the proposed algorithm.
Synthesis and computer simulation of learning control of horizontal robot arm
This paper presents the synthesis and the computer simulation of an Iterative learning control (ILC) of trajectory-tracking robot-motion, based on an explicit dynamic model of TT-3000 SCARA-type robot of SEIKO Instruments Inc. Two learning operators are proposed. The simplified one is a scalar function identical to the minimal eigenvalue of the inertial matrix of the dynamic equations of robot motion. The complicated one is the inertial matrix itself. Two learning-control update laws with a feedback controller attached are derived by utilization of the scalar and the matrix learning operators respectively. The convergence of both learning procedures, based on the proposed update laws, is examined by the computer simulation. As a result a rapid convergence of the learning procedure with the matrix learning operator is achieved despite of the significant initial trajectory-tracking errors.
Iterative Learning Control Based on Relaxed 2-D Systems Stability Criteria
IEEE Transactions on Control Systems Technology, 2000
This brief develops a new algorithm for the design of iterative learning control law algorithms in a 2-D systems setting. This algorithm enables control law design for error convergence and performance, and is actuated by process output information only. Results are also given from the experimental application to a gantry robot.