Neural-fuzzy control of a flexible dynamic tracking and adjusting manipulator

Abstract

This paper presents an adaptive neural-fuzzy control scheme for a dual-level-structure flexible manipulator with variable dynamic payload. The dynamic moving model of the flexible manipulator is derived and the state-space equation is formulated first. A control scheme that consists of a neural-fuzzy controller in the feedback channel and an image-guided identification network (IGIN) in the forward configuration is then proposed. The IGIN is employed to locate the object (e.g. bimetal) to achieve the tracking function, while the dynamic neural network is used to learn the weighting factor of the fuzzy controller. Finally, simulations are run for various modes to describe the dynamic tracking system, and simulated results show a good performance of the control tracking system.

Sampled-data extended state observer-based backstepping control of two-link flexible manipulator

“…Non-model-based methods are simpler and easier to implement than the model-based methods. In recent years, many non-model-based control methods have been developed to solve the tip-tracking control problem of the TLFM, for example, composite control (Lochan et al, 2018a), sliding mode control (Lochan et al, 2018b), robust control (Mohamed et al, 2016), adaptive control (Pradhan and Subudhi, 2012, 2014; Subudhi and Pradhan, 2016), intelligent control (Agee et al, 2014; Cheng, 2015), observer-based control (Zhang et al, 2017), and so forth. The above controllers handle tip-tracking issue under both kinematic and dynamics uncertainties.…”

Adaptive fuzzy backstepping output constraint control of flexible manipulator with actuator saturation

“…To finish the tasks more accurately, modeling and control for a flexible manipulator have been received much attention recently. Some approaches are proposed for the control problem of flexible-joint robots [1][2][3][4]. Cheng [1] proposed an adaptive neural-fuzzy control scheme for a class of flexible manipulator with variable dynamic payload and achieved the tracking control.…”

“…Some approaches are proposed for the control problem of flexible-joint robots [1][2][3][4]. Cheng [1] proposed an adaptive neural-fuzzy control scheme for a class of flexible manipulator with variable dynamic payload and achieved the tracking control. Fateh and Souzanchikashani [2] put forward an indirect adaptive fuzzy control for electrically driven flexible-joint robot manipulators in which a novel estimation technique is introduced to estimate the uncertainty.…”

“…The proposed output feedback control scheme can be used to control a single-link flexible manipulator system, which does not require that the speed of the generator rotor is measured directly. Note that the previous references [1][2][3][4][5][6] assumed that all the states in the systems are measurable. Thus, they cannot be used for dealing with the states immeasurable of the singlelink flexible manipulator system.…”

Adaptive time-delay control with a supervising switching technique for robot manipulators

“…The controllers (such as computed torque control, computed-torque-like control, sliding mode control and adaptive control) based on dynamic models of robots are highly complicated due to the calculation of the nonlinear terms of the robot dynamics equations (Geng et al, 2014;Lewis et al, 1993;Wang et al, 2014). Intelligent control techniques such as fuzzy control and neural networks have been used to obtain the 'black box' model of the robot manipulators for realization of model-free control (Cheng, 2015;Er and Gao, 2003;Han and Lee, 2014;Ishiguro et al, 1992;Lin, 2006). The use of intelligent controls, however, introduces another problem: one needs to tune a number of parameters which heavily affect the control performance.…”