Adaptive iterative learning control for robot manipulators

Robotica, 2011

SUMMARYThis 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 D-type ILC is presented with an initial condition algorithm, which gives the initial state value in each iteration automatically. Thus, the resetting condition (the initial state error is equal to zero) is not required. The λ-norm is adopted as the topological measure in our proof of the asymptotic stability of this control scheme, over the whole finite time-interval, when the iteration number tends to infinity. Simulation results are presented to illustrate the effectiveness of the proposed control scheme.

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.

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.

IFAC Proceedings Volumes, 1997

Some aspects of the use of learning control for improved performance in robot control systems are studied. The learning control signal is used in combination with conventional feedback and feed-forward control. The e ects of disturbances, unmodeled dynamics and friction are studied theoretically and in simulations of a simpli ed model of a robot arm. Convergence and robustness aspects of the choice of lters in the updating scheme of the learning control signal are studied.

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.

A new control method, called adaptive nonlinear PD learning control (NPD-LC), is proposed for robot manipulator applications in this paper. The proposed control structure is a combination of a nonlinear PD control structure and a directly learning structure. Consequently, this new control method possesses both adaptive and on-line learning properties. One of the unique features of the NPD-LC algorithm is that the learning is based on the previous torque profile of the repetitive task. It is proved that the NPD-LC enjoys the asymptotic convergence for both tracking positions and tracking velocities. Simulation studies were conducted by comparing the proposed method with many other existing methods. As a result, it was demonstrated that the NPD-LC method can achieve a faster convergence speed. The proposed NPD-LC is robust and can be implemented for the control of robot manipulators.

2002

Iterative learning controllers are found to be effective for trajectory tracking tasks in the robotic systems especially when the system model is not known. One of the drawback of iterative learning control is its slow convergence and high tracking errors in the initial iterations because of zero knowledge about the system for each new desired trajectory. In this paper, importance of the initial control input in the convergence of error is highlighted. Experience of iterative learning controller for different desired trajectories is modelled using neural network. For a new desired trajectory, this neural network generates the initial control input which is used by the learning controller. This approach is proved to be very effective in improving the convergence of the tracking error. The proposed method is very general and applicable to most of the iterative learning controller without modifying their simple learning structures.

Asian Journal of Control, 2017

Real-life work operations of industrial robotic manipulators are performed within a constrained state space. Such operations most often require accurate planning and tracking a desired trajectory, where all the characteristics of the dynamic model are taken into consideration. This paper presents a general method and an efficient computational procedure for path planning with respect to state space constraints. Given a dynamic model of a robotic manipulator, the proposed solution takes into consideration the influence of all imprecisely measured model parameters, making use of iterative learning control (ILC). A major advantage of this solution is that it resolves the well-known problem of interrupting the learning procedure due to a high transient tracking error or when the desired trajectory is planned closely to the state space boundaries. The numerical procedure elaborated here computes the robot arm motion to accurately track a desired trajectory in a constrained state space taking into consideration all the dynamic characteristics that influence the motion. Simulation results with a typical industrial robot arm demonstrate the robustness of the numerical procedure. In particular, the results extend the applicability of ILC in robot motion control and provide a means for improving the overall trajectory tracking performance of most robotic systems.

A synthesis algorithm for the filters in a first order ILC is presented and applied on an industrial robot. The proposed ILC synthesis method is evaluated using two experiments on the robot. The first is a one-axis experiment where the system can be seen as a single servo. A modeling experiment is done to give input to the synthesis algorithm and then ILC is applied to the single axis showing a dramatic improvement in trajectory following. In the second experiment ILC is applied to a more complex multi axes motion where the robot draws a circle in a plane. The evaluation of the result is done using a pen mounted on the robot and it is evident that also on the arm-side an improved motion can be achieved. In both experiments the error converges to a stable level in about 5 iterations. Since a model is desired for the synthesis, an extra iteration has to be done for the modeling experiment. In this particular case a good path following can therefore be achieved after 6 iterations.

2021 IEEE 17th International Conference on Automation Science and Engineering (CASE), 2021

This work presents an iterative learning control (ILC) algorithm to enhance the feedforward control (FFC) for robotic manipulators. The proposed ILC algorithm enables the cooperation between the ILC, inverse dynamics, and a PD feedback control (FBC) module. The entire control scheme is elaborated to guarantee the control accuracy of the first implementation; to improve the control performance of the manipulator progressively with successive iterations; and to compensate both repetitive and non-repetitive disturbances, as well as various uncertainties. The convergence of the proposed ILC algorithm is analysed using a well established Lyapunovlike composite energy function (CEF). A trajectory tracking test is carried out by a seven-degree-of-freedom (7-DoF) robotic manipulator to demonstrate the effectiveness and efficiency of the proposed control scheme. By implementing the ILC algorithm, the maximum tracking error and its percentage respect to the motion range are improved from 5.78 • to 1.09 • , and 21.09% to 3.99%, respectively, within three iterations.