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The design aspects related to automatic control are extensive; I will list them one by one. Regarding the tuning of PID parameters: 1. Automatic tuning can be carried out using software； 2. The PID parameters automatically tuned may not be the best for the system, in which case manual tuning based on experience is required. If the P parameter is too small, it will take too long to reach dynamic equilibrium ； If the P parameter is too large, overshoot is likely to occur. Points to note regarding PID function blocks in ladder diagrams (programs): 1. It is advisable to use the PID wizard to create PID function blocks ； 2. I would like to mention the simplest and yet most easily overlooked issue: the enable control for the PID function block can only use a contact that remains closed at all times, such as SM0.0 or any one normally open contact of a memory in parallel with the normally closed contact of that same memory! During a previous project commissioning, I encountered such a problem: the PID block would function properly at times and malfunction at other times. When it malfunctioned, it seemed that there was nothing wrong with the PID block itself (the PID parameters were correct and it was properly enabled), but no output was generated. After searching for a long time, I suddenly realized it might be a problem with enabling – I had a hold relay for start/stop control connected in series at the enabling terminal; after changing it to SM0.0, everything worked fine! It also became clear why the PID function block works properly at times and not at other times: sometimes, keeping the relay in its active state after loading the program prevents problems; once the equipment is stopped, issues arise – once the PID function block is disabled, it no longer functions properly! I’ll share this with everyone to avoid making the same mistake. Below are the general methods for tuning PID controller parameters: Tuning the parameters of a PID controller is a key aspect of control system design. It determines the values of the proportional gain, integral time, and derivative time of the PID controller based on the characteristics of the process under control. There are many methods for tuning PID controller parameters, which can be broadly divided into two categories: one is the theoretical calculation tuning method. It primarily determines the controller parameters through theoretical calculations based on the system’s mathematical model. The calculation data obtained using this method may not be directly usable; it must still be adjusted and modified based on actual engineering conditions. The second is the engineering tuning method, which relies mainly on engineering experience and is carried out directly during the testing of control systems. It is a simple method that is easy to master, and it is widely used in practical engineering applications. The engineering tuning methods for PID controller parameters mainly include the critical ratio method, the response curve method, and the attenuation method. The three methods each have their own characteristics, and what they all have in common is that experiments are conducted first, followed by the tuning of controller parameters using engineering experience formulas. However, the controller parameters obtained using either method require final adjustment and refinement during actual operation. The critical ratio method is generally used nowadays. The steps for tuning the PID controller parameters using this method are as follows: (1) First, select a sufficiently short sampling period to enable the system to operate ； (2) Only a proportional control element is added, until the system exhibits critical oscillation in its step response to the input; at that point, the proportional gain and the critical oscillation period are recorded ； (3) The parameters of the PID controller are calculated using formulas under a certain degree of control. Setting of PID parameters: This is done based on experience and familiarity with the process, by referring to the measured values and the setpoint curve in order to adjust the values of P, I, and D. The ratio I/differential D is 2; the specific value can be determined based on the instrument, after which the proportional band P can be adjusted. If P is too large, it takes longer to reach stability; if P is too small, oscillations occur and the set point will never be achieved. For the engineering tuning of PID controller parameters, the following are some empirical values for P.I.D parameters in various control systems: For temperature T: P=20~60%, T=180~600s, D=3-180s ； Pressure P: P=30~70%, T=24~180s ； Liquid level L: P=20~80%, T=60~300s ； Flow rate L: P=40~100%, T=6~60s. Common mnemonic in the book: To determine the optimal parameter settings, check in ascending order from small to large ； First ratio, then integration, and finally adding the derivative ； The curve oscillates very frequently; the scale dial needs to be enlarged ； The curve floats around the large bay; turn the scale dial inwards ； The curve deviates slowly from its original state, and the integration time decreases ； The curve has a long fluctuation period, so the integration time needs to be increased further ； The curve oscillates at a high frequency; first reduce the derivative ； Large differential motion results in slow fluctuations. The differential time should be increased ； The ideal curve has two waves, higher at the front and lower at the back in a 4-to-1 ratio ； Examine carefully, make adjustments as needed, and conduct thorough analysis – the quality of the adjustments will not be poor. Based on my many years of work experience, I believe that the values of PID parameters should, on the one hand, be determined according to the specific characteristics of the control object ； On the other hand is experience. P is used to address amplitude oscillations; a higher value of P results in larger amplitudes of these oscillations, but a lower frequency of oscillation, and thus it takes longer for the system to reach stability ； I determines the speed of action response; a larger value of I results in a slower response speed, while a smaller value leads to a faster one ； D is used to eliminate static errors; generally, its value is set to be small, and it has a minimal impact on the system. For the temperature control system P, it is between 5-10% ； I is between 180-240s ； D is below 30. For the pressure control system P between 30-60% ； I is between 30-90s ； D is below 30. Here is an empirical method introduced. This method is essentially a trial-and-error approach; it is an effective method developed through production practice and has been widely applied in the field. The basic procedure of this method is to first determine a set of controller parameters based on operational experience and put the system into closed-loop operation; thereafter, a step disturbance is artificially introduced (such as by changing the setpoint of the controller), and the step response curve of the controlled variable or the controller output is observed. If the control quality is deemed unsatisfactory, the regulator parameters are adjusted based on the impact of each tuning parameter on the control process. Repeat this process of testing until satisfactory results are achieved. The empirical method is simple and reliable, but it requires certain on-site operation experience, and subjectivity and one-sidedness can easily arise during adjustment. When a PID regulator is used, there are multiple tuning parameters, and as the number of trial adjustments increases, it becomes difficult to determine the optimal tuning parameters. Taking the PID regulator as an example, the following details the tuning steps of the empirical method: A. Set the integral coefficient S0 of the regulator to 0 and the actual derivative coefficient k to 0; then put the control system into closed-loop operation. Increase the proportional coefficient S1 gradually, apply step changes to the disturbance signal, and observe the control process until a satisfactory control performance is achieved. B. Take the proportionality coefficient S1 as the current value multiplied by 0.83, and increase the integration coefficient S0 from low to high; similarly, make the disturbance signal change in a stepwise manner until a satisfactory control process is obtained. C. Keep the integration coefficient S0 unchanged and vary the proportionality coefficient S1 to observe whether there is an improvement in the control process; if there is an improvement, continue adjusting until satisfaction is achieved. Otherwise, increase the original proportionality coefficient S1 slightly, and then adjust the integration coefficient S0 in an effort to improve the control process. This process of trial and error is repeated until satisfactory scale factor S1 and integration factor S0 are found. D. By introducing appropriate actual differential coefficients k and actual differential time TD, the proportional coefficient S1 and integral coefficient S0 can be increased appropriately at this time. As with the previous steps, the setting of the differentiation time also requires repeated adjustments until the control process is satisfactory. The PID parameters are determined based on the inertia of the control object. For applications with high inertia, such as temperature control in large drying ovens, the value of P is usually above 10, I ranges from 3 to 10, and D is around 1. For systems with low inertia, such as a small motor driving a water pump for pressure closed-loop control, PI control is generally sufficient. P=1-10, I=0.1-1, D=0; these values need to be adjusted during on-site calibration. PID control explanation: In practical engineering applications, the most widely used type of controller regulation is proportional, integral, and derivative control, commonly referred to as PID control or PID regulation. The PID controller has been around for nearly 70 years, and thanks to its simple structure, good stability, reliable performance, and ease of adjustment, it has become one of the key technologies in industrial control. When the structure and parameters of the controlled object cannot be fully understood, or an accurate mathematical model is not available, and other techniques from control theory cannot be applied, the structure and parameters of the system controller must be determined based on experience and on-site tuning; in such cases, PID control technology is the most convenient to use. That is, when we do not fully understand a system and its controlled object, or when it is not possible to obtain the system parameters through effective measurement methods, PID control technology is the most suitable approach. There is PID control, and in practice there are also PI and PD control. A PID controller controls the system by calculating the control amount using proportion, integration, and differentiation based on the system’s error. Proportional (P) control: Proportional control is the simplest form of control. The output of its controller is proportional to the input error signal. When only proportional control is used, there is a steady-state error in the system output. Integral (I) 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 it reaches steady state, then such a control system is said to have a steady-state error, or simply to be a system with error. To eliminate steady-state error, an “integral term” must be introduced into the controller. The integral term represents the time-dependent integration of the error, and as time increases, this integral term grows. In this way, even if the error is small, the integral term increases over time; it drives the output of the controller to increase, thereby further reducing the steady-state error until it becomes zero. Therefore, a proportional-plus-integral (PI) controller enables the system to have no steady-state error after reaching steady state. Differential (D) control: In differential control, the output of the controller is proportional to the derivative of the input error signal (i.e., the rate of change of the error). Automatic control systems may experience oscillations or even instability during the process of correcting errors. The reason is the presence of components with high inertia or components that exhibit lag, which have a role in suppressing errors; their changes always lag behind those of the errors. The solution is to make the change in the error-suppression effect \"proactive\", that is, when the error approaches zero, the error-suppression effect should be zero. In other words, it is often insufficient to introduce only a \"proportional\" term in the controller; the function of this proportional term is merely to amplify the magnitude of the error. What is needed now is a \"derivative\" term, which can predict the trend of error changes. Thus, a controller with both proportional and derivative terms can bring the control action to eliminate the error to zero, or even to a negative value, thereby preventing severe overshoot of the controlled variable. Therefore, for controlled objects with high inertia or lag, a proportional-plus-differential (PD) controller can improve the dynamic characteristics of the system during the regulation process.