Abstract
Gravity and flexibility will cause fluctuations of the rotation angle in the servo system for flexible manipulators. The fluctuation will seriously affect the motion accuracy of end-effectors. Therefore, this paper adopts a control method combining the RBF (Radial Basis Function) neural network and pole placement strategy to suppress the rotation angle fluctuations. The RBF neural network is used to identify uncertain items caused by the manipulator’s flexibility and the time-varying characteristics of dynamic parameters. Besides, the pole placement strategy is used to optimize the PD (Proportional Differential) controller’s parameters to improve the response speed and stability. Firstly, a dynamic model of flexible manipulators considering gravity is established based on the assumed mode method and Lagrange’s principle. Then, the system’s control characteristics are analyzed, and the pole placement strategy optimizes the parameters of the PD controllers. Next, the control method based on the RBF neural network is proposed, and the Lyapunov stability theory demonstrates stability. Finally, numerical analysis and control experiments prove the effectiveness of the control method proposed in this paper. The means and standard deviations of rotation angle error are reduced by the control method. The results show that the control method can effectively reduce the rotation angle error and improve motion accuracy.
Highlights
Accepted: 5 April 2021Flexible manipulators are complex systems with multiple inputs and multiple outputs
The control method combining the pole placement strategy and the RBF neural network is applied to reduce the fluctuation of the rotation angle of flexible manipulators
The RBF neural network is used to distinguish the uncertain items of the system
Summary
Flexible manipulators are complex systems with multiple inputs and multiple outputs. They are widely used in in-depth space exploration, industrial assembly, and other fields [1,2]. If the gravity factor is considered in flexible manipulators’ dynamic modeling processes, the nonlinear factor is introduced again This further increases the difficulty of the servo system controller design. In literature [18], internal control loops with nonlinear terms are added to compensate for the influence of gravity factors on rigid manipulators. Literature [19,20] uses the neural network to identify the uncertain items in the dynamic system of flexible-joint manipulators, improving the control precision. The RBF neural network is used to identify the uncertain items of the servo system for flexible manipulators. The rest of the paper is organized as follows: Section 2 establishes the dynamic equation of the servo drive system for flexible manipulators with gravity found using the AMM and Lagrange’s principle.
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