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Discussion on Adaptive Motion Control Technology of Industrial Robot

Source: Time:2017-06-01 10:01:50 views:

        In the future, each feedback controller is adaptive, and when the measured value and the setting are different, it can change the control to accommodate the change. A true adaptive controller should be able to adjust its own parameters or otherwise change the algorithm to accommodate changes in the behavior of the control process.
Adaptive Motion Control Technology of Industrial
        For example, an adaptive proportional controller observes too fast or too slow during the control process and may adjust its gain. This approach is suitable for the control process with strict requirements, such as the variable load control process of the robot.

        If the robot carries a particularly heavy load, the movement speed will slow down. The adaptive controller analyzes the measurement results and adds gain to the robot. On the contrary, if the load suddenly decreases, the movement should be radical, you can give it minus the gain.

        In either case, the controller must be able to measure the process change to determine which tonic to take. If conditions permit, you can also directly measure the load from A point to point B how long it takes or how far to measure the path.

        Unfortunately, the adaptive controller from the detection of changes in the control process is very slow, so there will be long-term changes easily concealed, short-term interference will produce confusion. The control process usually distinguishes between long and Silver Wing short-term effects, and it does not indicate that it should be used even if the behavioral changes are detected in the control project.

        In spite of these challenges, the adaptive motion controller can also optimize the trajectory and achieve its purpose by learning. As long as the controller learns the robot motion response process, the robot's final position and the required sequence of instructions are calculated. The mathematical model needed to complete this artificial learning is very complex. Once the process model is developed, the controller can adjust its control algorithm and even predict the future process behavior.

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