In the application of linear motors, accurate position, speed and acceleration control are essential. Whether in high-precision processing equipment or precision automated production lines, linear motors need to be able to run accurately according to preset trajectories and parameters. However, linear motors are subject to interference from a variety of factors during operation, such as load changes, friction, electromagnetic interference, etc., which makes it challenging to achieve accurate closed-loop control.
Position closed-loop control is to detect the actual position of the linear motor in real time through a position sensor and compare it with the given target position. Common position sensors include grating scales and magnetic scales. The controller changes the driving force of the motor by adjusting the input current or voltage of the motor according to the position error signal, so that the linear motor moves toward the target position. In order to improve the accuracy of position control, a proportional-integral-differential (PID) controller is usually used. It can dynamically adjust the control amount according to the size, rate of change and historical error information of the position error, so that the linear motor can reach the target position quickly and accurately and remain stable at the target position.
The purpose of speed closed-loop control is to make the linear motor run at a set speed. Speed sensors, such as tachometers or encoders, are used to measure the actual speed of the motor. Similar to position closed-loop control, speed closed-loop also uses a PID controller. The controller compares the actual speed with the given speed and adjusts the motor input according to the speed error to keep the speed stable. In some applications that require high speed accuracy, a feedforward control strategy is also used to adjust the control amount in advance to compensate for speed fluctuations by estimating factors such as load changes.
Acceleration closed-loop control is very important for some applications that require fast start and stop and dynamic response. Acceleration sensors can measure the actual acceleration of linear motors. By comparing the actual acceleration with the given acceleration, the controller can adjust the driving force of the motor in real time to achieve precise acceleration control. Acceleration closed-loop control is usually combined with speed and position closed-loop control to form a multi-loop control system. For example, in the startup phase, the motor is first quickly brought to the set acceleration through acceleration closed-loop control, then gradually transitioned to speed closed-loop control, and finally switched to position closed-loop control when approaching the target position to achieve precise positioning.
PID controller parameter tuning is the key to achieve accurate closed-loop control. The goal of parameter tuning is to make the controller have good control performance under different working conditions. Common parameter tuning methods include trial and error, Ziegler-Nichols method, etc. The trial and error method is to repeatedly adjust the PID parameters and observe the response of the system until a satisfactory control effect is achieved. The Ziegler-Nichols law determines the initial value of the PID parameters based on the open-loop characteristics of the system, and then fine-tunes them. In addition, modern intelligent control algorithms such as genetic algorithms and particle swarm algorithms are also used to optimize PID parameters to improve the efficiency and accuracy of parameter tuning.
In order to ensure that the closed-loop control system can operate stably under various interference conditions, anti-interference and robustness design are required. This includes using filtering technology to suppress electromagnetic interference, denoising sensor signals, and designing robust controllers. Robust controllers can maintain good control performance under changes in system parameters and external interference. For example, the use of robust control methods such as H∞ control and μ synthesis can make the linear motors control system more robust to model uncertainty and interference.
The linear motor control system also needs to have real-time monitoring and optimization functions. By monitoring the motor's operating status, current, voltage and other parameters, abnormal conditions in the system can be discovered in a timely manner and handled accordingly. At the same time, based on the actual operating data, the control system parameters can be optimized online to adapt to different working conditions and task requirements. For example, when the load changes, the system can automatically adjust the PID parameters to maintain stable control performance. Through the above comprehensive design and optimization measures, precise closed-loop control of linear motor position, speed and acceleration can be achieved to meet the needs of various high-precision applications.
