Abstract
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 Lyapunov-like 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.
Highlights
The involvement of internet of things (IoT) and internet of services (IoS) in the fourth industrial revolution (Industry 4.0) facilitates the development of the smart manufacturing, which has contributed to the widespread use of the robotic manipulators
In the proposed control approach, the iterative learning control (ILC) algorithm is used to enhance feedforward control (FFC) performance achieved by the inverse dynamics through compensating the inaccuracies in the system model, while the feedback control (FBC) is adopted to nullify the effect of non-repeatable external disturbances
An ILC algorithm is proposed to enhance the FFC performance for the robotic manipulators by working coordinately with the inverse dynamics module and the FBC module. Both repetitive and non-repetitive disturbances, as well as various uncertainties can be suppressed. Both the FBC and ILC algorithm contribute to the system convergence as presented in the Lyapunov-like analysis
Summary
The involvement of internet of things (IoT) and internet of services (IoS) in the fourth industrial revolution (Industry 4.0) facilitates the development of the smart manufacturing, which has contributed to the widespread use of the robotic manipulators. It is worth to note that in most of the above mentioned works, the ILC schemes are designed directly to replace the role of the pure inverse dynamics in the traditional controllers, which did not fully take the advantage of the available system dynamics Motivated by this observation, it is of practical importance to develop new controllers which fully utilise the available system knowledge as well as the learning ability. A novel learning-based controller is developed for the tracking control of robotic manipulators It consists of three parts including a FBC module, an inverse dynamics module, as well as an ILC strategy. In the proposed control approach, the ILC algorithm is used to enhance FFC performance achieved by the inverse dynamics through compensating the inaccuracies in the system model, while the FBC is adopted to nullify the effect of non-repeatable external disturbances. The CoppeliaSim® is selected for both simulation and real robot control purpose, due to its supporting of simultaneous incorporation with both programming environment (i.e. MATLAB® in this work) and hardware control
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