Load control method of indenter based on fuzzy predictive control and system thereof

A load control system of an indenter based on fuzzy predictive control, wherein the load control system comprises:

• a first actual measured force value acquiring module, which is configured to acquire an actual measured force value of a sensor in a loading stage, which is denoted as a first actual measured force value;
• a first expected force value acquiring module, which is configured to acquire an expected force value of an nth cycle in the loading stage, which is denoted as a first expected force value;
• a first error and first error change rate calculating module, which is configured to calculate a first error according to the first actual measured force value and the first expected force value, and determine a first error change rate based on a difference between a current cycle error and a previous cycle error;
• a fuzzy predictive controller establishing module, which is configured to establish a fuzzy predictive controller;
• an optimizing module, which is configured to optimize the fuzzy predictive controller;
• a motor movement step determining module in the loading stage, which is configured to input the first error and the first error change rate into the optimized fuzzy predictive controller to acquire movement steps of a motor in the loading stage;
• a first executing module, which is configured to send the movement steps of the motor in the loading stage to an actuator, control the motor to move until a set load is reached, and enter a full load stage;
• a second actual measured force value acquiring module, which is configured to acquire an actual measured value of the sensor in the full load stage, which is denoted as a second actual measured force value;
• a second expected force value acquiring module, which is configured to acquire an expected force value of an nth cycle in the full load stage, which is denoted as a second expected force value;
• a second error calculating module, which is configured to calculate a second error based on the second actual measured force value and the second expected force value;
• a second executing module, which is configured to control the motor to move based on the second error until full load time is reached, and enter an unloading stage;
• a third actual measured force value acquiring module, which is configured to acquire an actual measured force value of the sensor in the unloading stage, which is denoted as a third actual measured force value;
• a third expected force value acquiring module, which is configured to acquire an expected force value of an nth cycle in the unloading stage, which is denoted as a third expected force value;
• a third error and third error change rate calculating module, which is configured to calculate a third error according to the third actual measured force value and the third expected force value, and determine a third error change rate based on a difference between a current cycle error and a previous cycle error;
• a motor movement step determining module in the unloading stage, which is configured to input the third error and the third error change rate into the optimized fuzzy predictive controller to acquire movement steps of the motor in the unloading stage;
• and
• a third executing module, which is configured to send the movement steps of the motor in the unloading stage to the actuator, and control the motor to move until the load is reduced to zero.