### Automatics

**Chapter 28 PID controllers tuning**

**Chapter 28.1 Introduction**

In

**chapters 23..27**we discussed the principle of operation of the

**P, PD, I, PI**and

**PID**controllers. I think that by manually selecting

**Kp, Ti**and

**Td**settings by

**trial**and

**error**method, you understood their roles well. By the way, you learned the

**first**method of setting settings.

**Trial**and

**error**method, also used in other fields far from

**automatics**.

**Example**

You are very

**careful**when

**skiing**for the first time. You make big bends, you brake for a long time, you fall over. You are similar a

**PID**controller with very careful settings. Small

**Kp**and

**Td**, there is almost no integration, i.e. very large

**Ti**. As you learn, you gain experience, the movements are more and more

**precise**. And what does this mean in the language of

**automatics**? The fact that you

**improve**the settings of your private controller

**Kp, Ti, Td**encoded in your brain. After all, you are slalom between the poles. Your controller is now almost

**optimal**. I suspect that the best tennis players in the world, have settings of their regulators very similar to each other. But only the champion has a little better. I’m done with this naive and greatly simplified philosophy.

What did it stand for? That the

**first**approach to tuning

**controllers**(i.e. matching

**Kp, Ti and Td**) is the

**trial**and

**error**method –> otherwise, the

**manual**method. Now let’s move on to the serious methods.

I will present some

**old-fashioned**methods invented during World War II by a certain

**Ziegler**and

**Nichols**:

**– Step response**method

**–**method

**Self oscillation**Each is an example of a different school. The first requires a

**mathematical description**, i.e. the

**Go(s)**transmittance of the object. The

**second**is only to

**examine**the object, and more precisely, to measure the period of

**oscillations**with some

**gains**of the so-called

**critical.**So she is less fussy about the exact knowledge of the

**object**.

**Chapter 28.2 Step response method****Chapter 28.2.1 Introduction**

Requires knowledge of the **mathematical** model of the **object**.**We can** get the model by:**– accurate mathematical** analysis through knowledge of** physical** and **chemical** processes. This is of course a task for the **ambitious**.**– We do not go** into any thermodynamics, we only study the **response** to a unit step **x(t)**. The task is **less ambitious** and **easier**, so we will discuss it.**There** will be** 4 stages** in it.**1-Studying** the object’s response to a step **x(t)** in an** open** system and determining the approximate transmittance **Gp(s)** as inertia **T** and delay **To****2-Determination** from the appropriate tables of optimal settings **Kp, Ti** and **Td****3-Checking** the response to a **closed** system **step****4-Trying** to find better settings **manually**

**Chapter 28.2.2 Step 1 – Examining of the object’s response to a step in an open system**

**Fig. 28-1**

This is a typical **multi-inertial** unit, here **four-inertial**

On a real object, of course, you don’t know this **transmittance**. You only guess that it is a **multi-inertial** unit. What is this “**multi**“, what are the time constants **T1,T2,T3** and** T4**? You do not know that. I will only add that many processes, especially in the **chemical industry**, are of this nature. Focus on the **black** step **x(t)** and the** red** response **y(t)**. You see the characteristic **elbow**, or **inflection** point. A **tangent** was drawn through this point and the parameters **K**, **T** and **To** of the equivalent transmittance **Gp(s)** were determined from this construction. This topic has been discussed in detail in **chapter 10 Fig. 10-5**.

Fig.28-2

Determined object parameters** K, T** and** To**.**Chapter 28.2.3 Step 2 – Determination from the appropriate tables of optimal settings Kp, Ti and Td**

According to **Mr. Ziegler**, there are several optimal combinations of **Kp, Ti, Td** for the same step **x(t)** response. how is it? After all, the **optimal** set of **Kp, Ti** and **Td**. There can only be one winner. Holy truth, but the** first optimum** is for the **shortest** time **without overshoots**. The** second** is also the **shortest time**, but e.g. **with 20% overshoot**, and the **third** is the **criterion** for the **minimum integral** of the** square** of the** error**. The very name of the **third** arouses **reluctance**. Therefore, we will** tune** the **controller** according to criterion **No. 2**, and it says this:

For the **PID** controller and the object from **Fig. 28-3**, the following settings of the **PID** controller will ensure the** shortest** control** time** with an **overshoot** of no more than **20%**.

Fig. 28-3**Formulas** and **calculated** settings for an object with parameters from** Fig. 28-2**. There are some **limitations**. Namely, the **To** delay It must be in the range of **0.15T…0.6T**. So, for example, pure **inertia** or pure **delay** is out. The condition is obviously met because **2.1 sec<4 sec<8.4 sec**. So let’s check what the response to a step **x(t)** looks like with the above** settings**.**Chapter 28.2.4 Step 3 – Closed loop response checking****Fig. 28-4**

Closed system with optimal settings from **Fig. 28-3**.

**Fig. 28-5****What** would I say here? **First** of all, the overshoot is **50%**, not **20%**. **Secondly**, the control time is over **1** minute. A decent controller should give a faster response than the open system (without a controller) in **Fig.28-1**.

That’s how I explain it. The settings determined by the** Ziegler-Nichols** criterion are only the **first** approximation. It’s as if someone took us by helicopter to the **Mount Everest** area and said. Now look for the **peak**-optimum **alone**. It is true that he did not drop us off at the **peak** on **Mount Everest**, but he made the **job** much easier. The search **area** has **narrowed**. So we will continue to search for optimal settings by trial and error from the starting point **Kp=4.2** **Ti=8 sec**. **Td=1.6 sec**. There will be fewer **Kp, Ti, Td** combinations to explore than if we were starting from a random (read “further”) starting point.**Chapter 28.2.5 Step 4-Trying to find better settings by manual method**I did a few tests with different settings. Finally, I found such controller settings

**Fig. 28-6**

**Kp=3 Ti=7 sec Td=3 sec**

**Fig. 28-7**

There is an improvement? Is. Even the **overshoot** is around **20%**. But why did the first shot (**Fig. 28-6)** miss? I tried clumsily to defend **Mr. Ziegler**. That this is only the **first** approximation to the optimal settings. That the parameters **Kp, T, To** (especially **To**)were not determined exactly, that … etc.

**Chapter 28.3 – Self oscillation method**

**Chapter 28.3.1 Introduction**

In

**chapter 28.2**, we determined the optimal

**Kp, Ti**and

**Td**settings based on the mathematical

**model**describing the object. It doesn’t matter that it was a

**simplified**model – equivalent transmittance

**Gp(s)**of the

**inertia**with

**delay**type in

**Fig. 28-1**. It doesn’t matter that it was based on experiment, not theoretical considerations. It is important that we used the

**object model**. It was the

**first**school of controllers

**tuning**.

The

**method is an example of the**

**self oscillation****second**school. No model model is needed here. All you have to do is do “something” to the object and observe its behavior. This “something” can be, for example, a gradual increase in

**Kp**gain in a closed system until it becomes

**unstable**. In other words, it will become a

**generator**.

**Chapter 28.3.2 Test No. 1**

Fig. 28-8

Fig. 28-8

How is it different from

**Fig. 28-4**?

**1-Through**zeros, the components – integrating and differentiating have been

**excluded**. So the

**PID**controller became the

**P**controller

**2-Force x(t)**in

**3**seconds is now a

**Dirac**pulse (actually a pseudo

**pulse**)

I remind you that

**unstable**systems can “stay still”. Like a

**sharpened**pencil placed upright on a table. It can go on like this until the end of the world, although it is clearly an

**unstable**system. Therefore, to observe instability, we give a

**Dirac**impulse to the input.

**Note**

The

**advantage**of the method is that it concerns a

**closed system**. “Opening” a closed system in order to study it can be

**troublesome**for an industrial facility.

**Fig. 28-9**

We** hit** the object on the nose with a **Dirac** hammer. It hit and the system returned from the initial state **0** to the **final** state (equilibrium)** 0**. So for **K=3** the system is **stable**.**Chapter 28.3.3 Test No. 2**Scheme from

**Fig. 28-8**, only

**Kp=5**

**Fig. 28-10**It lingers longer.

**Conclusion**– We have approached

**instability**but the system is still

**stable**.

**Chapter 28.3.4 Test No. 3**

Scheme from

**Fig. 28-8**, only

**Kp=6.27**

**Fig. 28-11**It swings

**endlessly**with a

**constant**amplitude.

**Conclusion**– We are on the verge of stability. What a coincidence that we chose

**Kp=6.27**!

Oscillation period

**Tosc=16.3 sec**.

Let’s remember

**2**values from this

**experiment**:

**–**gain

**Kp=6.27**, which we will call the critical gain

**Kkr=6.27**

**–**oscillation period

**Tosc=16.3**

Both parameters will be needed to determine the optimal settings

**Kp,**

**Ti**and

**Td**in

**Chapter. 28.3.7**.

We don’t need to increase the

**Kp**gain any more. But let’s do it just out of curiosity.

**Chapter 28.3.5 Test No. 5**

Scheme from

**Fig. 28-8**, only

**Kp=6.5**

**Fig. 28-12**

I guess that’s what we expected. The amplitude slowly increases to **infinity**. If it was a **real** system and not a **linear ideal**, the amplitude would increase to +**/- saturation**.**Chapter 28.3.6 Determination of optimal parameters**

Fig. 28-13

Fig. 28-13

Optimal parameters

**Kp, Ti**and

**Td**are calculated according to very simple patterns–>

**Fig. 28-13a.**They should ensure the shortest control time with an overshoot of no more than

**30%**. When we substitute

**Kkr=6.27**and

**Tosc=16.3**sec from

**Fig. 28-11**into these formulas, we will obtain optimal settings ->

**Fig. 28-13b**. I suggest checking with a calculator.

**Chapter 28.3.7 Step response at optimal settings**

**The settings are from**

Fig. 28-14

Fig. 28-14

**Fig. 28-13b**, i.e. they are

**optimal**according to the

**method**

**Self Oscillation**

**Fig. 28-15**

It does not bring you to your knees, but compared to the previous method of **step response** in **Fig.28-5** the response is **closer** to the optimal **one**. Of course, this does not mean that the method is better. Maybe it’s different with another facility? I’m not taking a position here.

We can try to correct the parameters manually, so that we get an response not worse than above.