Abstract
This paper describes a hybrid approach to the problem of controlling flexible link manipulators in the dynamic phase of the trajectory. A flexible beam/arm is an appealing option for civil and military applications, such as space-based robot manipulators. However, flexibility brings with it unwanted oscillations and severe chattering which may even lead to an unstable system. To tackle these challenges, a novel control architecture scheme is presented. First, a neural network controller based on the robot’s dynamic equation of motion is elaborated. Its aim is to produce a fast and stable control of the joint position and velocity and damp the vibration of each arm. Then, an adaptive Cerebellar Model Articulation Controller (CMAC) is implemented to balance unmodeled dynamics, enhancing the precision of the control. Efficiency of the new controller obtained is tested on a two-link flexible manipulator. Simulation results on a dynamic trajectory with a sinusoidal form show the effectiveness of the proposed control strategy.
Highlights
The trajectory control of a manipulator robot can be decomposed into two parts
While the majority of the existing researches on the control of flexible link manipulators concentrate on the positioning phase of the movement, very few of them deal with the dynamic phase of the movement
This paper presents a new control system structure to deal with the tracking control problem of flexible link manipulators on the dynamic phase of the trajectory
Summary
The trajectory control of a manipulator robot can be decomposed into two parts. Tracking the desired trajectory on the dynamic phase of the movement and positioning the tip of the link on the final phase of the movement. This paper presents a new control system structure to deal with the tracking control problem of flexible link manipulators on the dynamic phase of the trajectory. The controller presented in this paper is based on Artificial Neural Networks (ANNs) that approximate the dynamic model of the robot. Using ANNs, replacing nonlinear modeling, may simplify the structure of the controller, reduce its computation time and enhance its reactivity without a loss in the accuracy of the tracking control. It is easy to compute since it does not require the computation of all or part of the dynamic model This robust controller design method maximizes the control performance guaranteeing good precision when regulating the tip position of the flexible arm in the presence of large structured and unstructured uncertainties.
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