Abstract
This paper aims at the trajectory tracking problem of robot manipulators performing repetitive tasks in task space. Two control schemes are presented to conduct trajectory tracking tasks under uncertain conditions including unmodeled dynamics of robot and additional disturbances. The first controller, pure adaptive iterative learning control (AILC), is based upon the use of a proportional-derivative-like (PD-like) feedback structure, and its design seems very simple in the sense that the only requirement on the learning gain and control parameters is the positive definiteness condition. The second controller is designed with a combination of AILC and neural networks (NNs) where the AILC is adopted to learn the periodic uncertainties that attribute to the repetitive motion of robot manipulators while the add-on NNs are used to approximate and compensate all nonperiodic ones. Moreover, a combined error factor (CEF), which is composed of the weighted sum of tracking error and its derivative, is designed for network updating law to improve the learning speed as well as tracking accuracy of the system. Stabilities of the controllers and convergence are proved rigorously by a Lyapunov-like composite energy function. The simulations performed on two-link manipulator are provided to verify the effectiveness of the proposed controllers. The results of compared simulations illustrate that our proposed control schemes can significantly conduct trajectory tracking tasks.
Highlights
Iterative learning control (ILC) is a well-established control scheme for systems with repetitive tasks during a finite time interval. e fundamental idea of this method is to iteratively construct a control input based on errors from previous control information so that the performance of the system such as trajectory tracking and disturbance attenuation can be optimized
It is worth noticing that a combined error factor (CEF), which consists of the weighted sum of tracking error and its derivative, is designed for neural networks (NNs) updating law to improve the learning speed as well as tracking accuracy of the system
Two control schemes will be given to address the control problem of the robot manipulator system. e first controller, pure adaptive iterative learning control (AILC), is based upon the use a PD-like feedback structure, for which an iterative term is added to deal with the unknown parameters and additional disturbances. e second controller is designed with a combination of AILC and NNs where the AILC is adopted to iteratively learn the periodic uncertainties while the NNs are used to approximate and compensate all of the system nonperiodic uncertainties
Summary
Iterative learning control (ILC) is a well-established control scheme for systems with repetitive tasks during a finite time interval. e fundamental idea of this method is to iteratively construct a control input based on errors from previous control information so that the performance of the system such as trajectory tracking and disturbance attenuation can be optimized. It is very difficult to obtain the real-time position, velocity, and acceleration measurements of robot manipulators in practical situation for various disturbances, unmodeled dynamics, and unknown uncertainties Motivated by this situation, the authors provide a new controller for the pose trajectory tracking control problem which is based on a primary joint loop of joint velocity and a secondary loop of operational space position control [33]. (2) e stability and convergence of proposed control scheme are provided rigorously by a Lyapunov-like composite energy function, where the CEF, which is composed of the weighted sum of tracking error and its derivative, is employed to be designed for network updating law to improve the learning speed as well as tracking accuracy of the system. E conclusions and future recommendations are illustrated in the last section
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