Abstract
An adaptive PD control scheme is proposed for the support system of a wire-driven parallel robot (WDPR) used in a wind tunnel test. The control scheme combines a PD control and an adaptive control based on a radial basis function (RBF) neural network. The PD control is used to track the trajectory of the end effector of the WDPR. The experimental environment, the external disturbances, and other factors result in uncertainties of some parameters for the WDPR; therefore, the RBF neural network control method is used to approximate the parameters. An adaptive control algorithm is developed to reduce the approximation error and improve the robustness and control precision of the WDPR. It is demonstrated that the closed-loop system is stable based on the Lyapunov stability theory. The simulation results show that the proposed control scheme results in a good performance of the WDPR. The experimental results of the prototype experiments show that the WDPR operates on the desired trajectory; the proposed control method is correct and effective, and the experimental error is small and meets the requirements.
Highlights
Neural networks are widely used in engineering, especially in the field of industrial automation [1]
We propose a new adaptive PD control scheme based on an radial basis function (RBF) neural network for a wire-driven parallel robot (WDPR) with 6 DOF and driven by 8 wires; the nature and task requirements are based on the WDPR described in reference [13]
The main contributions of this study are the following: (1) the PD control is used to track the trajectory of the end effector; (2) the RBF neural network approximation is used to compensate for the uncertainties of the system, such as external disturbances; (3) the adaptive control is used to achieve a high trajectory tracking performance of the WDPR; (4) the simulation results and the prototype experimental results show that the RBF neural network results in good control performance of the WDPR and meets the requirements of the wind tunnel test
Summary
Neural networks are widely used in engineering, especially in the field of industrial automation [1]. Sabahi et al [7] proposed a new indirect type-2 fuzzy neural network predictive (T2FNNP) controller for a class of nonlinear inputdelay systems in the presence of unknown disturbances and uncertainties. The main contributions of this study are the following: (1) the PD control is used to track the trajectory of the end effector; (2) the RBF neural network approximation is used to compensate for the uncertainties of the system, such as external disturbances; (3) the adaptive control is used to achieve a high trajectory tracking performance of the WDPR; (4) the simulation results and the prototype experimental results show that the RBF neural network results in good control performance of the WDPR and meets the requirements of the wind tunnel test.
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