An adaptive neural network compensator for decoupling of dynamic effects of a macro-mini manipulator
In this paper, an adaptive neural network compensator is proposed to improve the control performance of a macro-mini robotic manipulator, whereby an end effector called the mini manipulator is mounted at the end of an industrial robot called the macro manipulator in robotic literature. With the macro-mini architecture, the manipulator has the advantages of a large workspace due to the industrial robot, as well as fast dynamic response and diverse functions due to the end effector with optimized mechanical design. However, the coupling dynamics between two separated systems will also influence the control performance, especially at the end effector. An adaptive neural network compensator is added to the mini's original control system, and it can estimate and eliminate the dynamic coupling effect coming from the macro system in real-time in an on-line manner. Simulation shows that even if the model of the macro is unknown and its controller unchanged, in the presence of external disturbance at macro manipulator, the dynamic coupling effect can be almost eliminated in both position and force controls by the appended adaptive neural network compensator. Experimental results also demonstrate that as compared to the mini based on feedback linearization with the PID controller, vibration at the end point due to the coupling effect can be obviously suppressed using the adaptive compensator, and the steady state error is also gradually improved.