What are the main disadvantages of traditional PID control? How to improve the controller?

Principle and characteristics of PID control

In engineering practice, the most widely used regulator control laws are proportional, integral and differential control, referred to as PID control, also known as PID regulation. PID controller has a history of nearly 70 years since it came out. Because of its simple structure, good stability, reliable operation and convenient adjustment, it has become one of the main technologies of industrial control. When the structure and parameters of the controlled object can't be fully grasped, or accurate mathematics can't be obtained, it is difficult to adopt other technologies of control theory. The structure and parameters of the system controller must be determined through experience and field debugging, so it is most convenient to apply PID control technology. That is, when we don't fully understand a system and the controlled object, or we can't get the system parameters through effective measurement, PID control technology is the most suitable. PID control, in fact, there are also PI and PD control. PID controller is based on the system error, using proportion, integral and differential to calculate the control quantity to control.

proportional control

Proportional control is the simplest control method. The output of the controller is proportional to the input error signal. When there is only proportional control, there is a steady-state error in the system output.

Integral control

In integral control, the output of the controller is proportional to the integral of the input error signal. For an automatic control system, if there is a steady-state error after entering the steady state, it is called a system with steady-state error. In order to eliminate the steady-state error, an "integral term" must be introduced into the controller. The error of the integral term pair depends on the integration of time, and the integral term will increase with the increase of time. In this way, even if the error is small, the integral term will increase with the increase of time, thus pushing the output of the controller to increase and further reducing the steady-state error until it is zero. Therefore, the proportional-integral (PI) controller can make the system enter the steady state without steady-state error.

Differential control

In differential control, the output of the controller is directly proportional to the differential of the input error signal (that is, the rate of change of the error). In the process of adjusting to overcome the error, the automatic control system may oscillate or even become unstable. The reason is that there are components (links) with large inertia or delayed components, which can suppress errors, and their changes always lag behind the changes of errors. The solution is to introduce the change of error suppression effect, that is, when the error is close to zero, the error suppression effect should be zero. In other words, it is often not enough to introduce only the "proportion" item into the controller. The function of the proportional term is only to amplify the amplitude of the error. What needs to be added now is the "differential term", which can guess the trend of the error change. In this way, the controller with proportional differentiation can make the control function of restraining error zero or even negative in advance, thus avoiding the serious overshoot of the controlled quantity. Therefore, for the controlled object with large inertia or time delay, the proportional-differential (PD) controller can improve the dynamic characteristics of the system during the adjustment process.

The traditional control theory is based on the precise model of the controlled object (transfer function and state equation), but for some complex systems, it is difficult and sometimes impossible to establish its mathematical model, so it is impossible to realize automatic control by system control, and manual control is often better. Fuzzy control is to summarize the operator's experience, form language rules, and use fuzzy set theory to simulate the operator's manipulation and decision-making, thus realizing automatic control.