Milling chatter suppression based on the model predictive optimal control with Kalman state estimation

Chatter in the milling system can result in poor surface quality of the workpiece, low machining efficiency, damage to the tool, and so on. Owing to the limitations of the control position and the complexity of the processing environment, existing methods exhibit several shortcomings in the design and comprehension of the control system. Therefore, an optimal active chatter control method is proposed in this paper; the control weight parameters, input ways, and state prediction effects of the controller are explored in depth. First, a new active control device with adjustable control positions is designed utilizing piezoelectric actuators. The control system is expected to achieve good performance for the chatter induced by the flexibility of the milling tool. Then based on the presented chatter control system, a model predictive control (MPC) algorithm is used to calculate the optimal control force. In addition, the Kalman filter is applied to predict the optimal state feedback value, based on which an optimal active chatter control strategy is proposed. Simulation results indicate that the saturation problem of the actuator can be solved effectively by using incremental input control. The Kalman filter is a state observer that can predict the response of the system accurately. Finally, some milling experiments are carried out utilizing the designed active control device. The experimental results prove that the proposed optimal control method can suppress the milling chatter effectively.