A typical serial manipulator consists of a servo motor, a serial mechanism and an independent joint placed between the motor and the serial mechanism. Both the time-varying characteristics of the inertia of the serial mechanism and the flexibility characteristics of the independent joint are widely found in serial manipulator servo drive systems. These two characteristics not only increase the resonance magnitude of serial manipulators, but also affect the dynamic characteristics of the system. In order to obtain a stable output speed of serial manipulators, the variable parameters of a PI control strategy is applied to a serial manipulator servo drive system. Firstly, dynamic model of a serial manipulator servo drive system is established based on a two-inertia system. Then the transfer function from motor speed to motor electromagnetic torque is derived by the state-space equation. Furthermore, the parameters of the PI controller are designed and optimized utilizing three different pole assignment strategies with the identical radius, the identical damping coefficients, and the identical real parts. The results indicate that a serial manipulator servo drive system can obtain good dynamic characteristics by selecting parameters of the PI controller appropriately.
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.
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