An intelligent controller and self-tuning method

Abstract

The invention discloses a self-tuning method of an intelligent controller. A deviation value (SV-m) is set below a control target value SV to prevent oscillation of system output wave nearby the target value SV or the deviation value (SV-m) in the self-tuning process; excitation applied to a controlled object is stopped when the system sampling value PV is greater than the target value SV, thus ensuring that the system output wave can reach the control target value SV all the time, and the PID control parameter calculated by the controller is more accurate; in addition, in the self-tuning method, no matter how the initial state of the system is, a working condition signal is acquired first, and whether excitation is applied to the controlled object is determined by comparing the acquired value PV with the deviation value (SV-m), that is, working condition judgment is prior to control output, so that overshoot is effectively prevented and industrial accidents are avoided.

Description

technical field [0001] The invention relates to the field of automatic control of industrial processes, in particular to an intelligent controller and a self-tuning method. Background technique [0002] Commonly used industrial controllers are mainly used to realize the control of industrial signals such as temperature, pressure, liquid level, flow, etc. During control, the controller calculates the output control signal by substituting the deviation between the collected value and the set target value into the control parameters such as proportional band (P), integral time (I), and differential time (D), and real-time Send it to the actuator, and finally make the industrial signal constant at the control target value. In industrial control, due to the variety of control objects and complex working conditions, it is difficult to adjust the control parameters in a timely and accurate manner. Traditional methods often require operators to continuously adjust and test with suf...

AI Technical Summary

Problems solved by technology

The self-tuning method of the above-mentioned industrial controller sets the acceptable range of the sampling value, and the boundary point for the controller to determine whether the excitation is applied or not is the lower boundary value of the acceptable range, and the sampling value is smaller than the lower boundary value of the acceptable range, which has a negative effect on the control When the object is stimulated, the sampling value is greater than the lower boundary value of the acceptable range, and the application of excitation to the control object is stopped. In this way, the system output wave is easy to oscillate back and forth at the lower boundary value of the acceptable range, and the normal characteristics of the system cannot be captured, and In a fast response system (such as a fast heating system), the output wave peak cannot reach the control target value according to the system inertia. In this case, the PID control parameters calculated according to the excitation and its corresponding output characteristics are not accurate enough, and the self-tuning In the method, the control object is first stimulated and then judged. The control output is prioritized over the working condition judgment. When the initial state value of the system has exceeded the target value, this will not only lengthen the period of the self-tuning process, but also many working conditions do not allow exceeding the target value. Too much adjustment, prone to industrial accidents

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