Function of PD, PI, ID and PID microcontrollers on a system

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Sam Groves
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Well different microcontrollers act differently on a system. A pure P microcontroller reduces the error and increases the final value , a pure I microcontroller nullifies the error and increases the final value and a pure D microcontroller increases the error and keeps the final value the same.

However when combining pure microcontrollers the net result will be somewhere in between right?

Since generally a microcontroller in the s domain can be written down as:K0+K1/s+K2s the net result depends on the constants K0,K1 and K2.Given K0,K1,K2 can we evaluate how the PID would act out?Is it like the RGB color or is it more complicated(K0 has greater weight than K1 or K2)?
 
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Sam Groves said:
Is it like the RGB color
No

Sam Groves said:
is it more complicated
Yes, it's more complicated. I don't think there's a good answer without knowing the nature of the system being controlled. Feedback compensation is tailored for each system to optimize performance based on desired behavior.

BTW, there's nothing special about PID. As a controls guy, I find it kind of annoying that that is what is always taught. You could have two integrator terms, no proportional term, etc. In fact my general advice is to just not use the derivative term unless you both need it and have a really good understanding of stability of feedback systems. It usually causes more problems than it solves.

Sorry, I don't know of a simple reference to explain this. You can search for "feedback compensation" to dig deeper; there's lots of good stuff on the web.
 
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@Dave:
I don't disagree with anything that you said. I think that the emphasis on 'PID' is a result of the fact that for most standard industrial control hardware (PLCs, etc.), 'canned' PID is what is available. Many of the people implementing controls in an industrial setting have little/no understanding of the (mathematical) theory behind the 'knobs' that they're turning when they 'tune' a system. There are some surprisingly good empiricists running around. They lack the classical training that EEs get, but their approach accounts for the practical limitations of feedback (accuracy, latency, etc.) that some EEs never seem to 'get'. Obviously, a trained EE (with actual experience) is ideal.

I agree that 'D' is much less commonly required. Where 'D' is rquired, you absolutely need to know what you're doing - It's not useful in a temperature control for an oven with a 12-Hour time constant. For servo-valves modulating the application of aggregate to multi-colored roof shingles zooming by at several FPS, it's absolutely required. 'D' can always (at least theoretically) 'improve' the response - knowing when it's worth the trouble is the trick.
 
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1. What is the function of a PID controller in a system?

A PID (Proportional, Integral, Derivative) controller is a control loop feedback mechanism widely used in industrial control systems. A PID controller continuously calculates an error value as the difference between a desired setpoint and a measured process variable and applies a correction based on proportional, integral, and derivative terms. The PID controller aims to minimize the error by adjusting the process control inputs. Its main function is to ensure that the output of a system reaches a desired setpoint efficiently without delay or overshoot, making it ideal for systems where precision is crucial.

2. How do PD, PI, and ID controllers differ from PID controllers?

PD, PI, and ID controllers are simplified versions of the PID controller, each missing one of the terms:- PD (Proportional-Derivative) controllers do not include the integral term. They react to the current error and the rate at which the error is changing but do not account for the history of the error.- PI (Proportional-Integral) controllers lack the derivative term. They respond to the current error and accumulate past errors, which helps eliminate residual steady-state errors but can lead to overshoot.- ID (Integral-Derivative) controllers are less common and exclude the proportional term, focusing on the accumulation of past errors and the rate of change of the error, which makes them less responsive to the magnitude of the error.Each type of controller is suited to specific applications depending on the nature of the system and its response characteristics.

3. What are the typical applications of PID controllers in industrial systems?

PID controllers are versatile and used in various applications where precise and optimal automatic control is required. Common applications include temperature control, speed control, pressure control, and flow rate control. They are essential in systems like HVAC (heating, ventilation, and air conditioning), automotive control systems, drones, and manufacturing processes. The ability of PID controllers to ensure stability and accuracy makes them invaluable in these applications.

4. How do you tune a PID controller?

Tuning a PID controller involves setting the proportional, integral, and derivative gains (Kp, Ki, Kd) to get the desired response from the control system. The most common methods for tuning include manual tuning, Ziegler-Nichols, Cohen-Coon, and software-based optimization. Manual tuning involves adjusting the gains based on trial and error, observing system response, and making adjustments accordingly. Ziegler-Nichols and Cohen-Coon provide more systematic approaches, using specific test responses to determine appropriate settings. Software-based methods might involve simulation models to optimize controller parameters for the best performance.

5. What challenges are associated with implementing PID controllers?

Implementing PID controllers can present several challenges, including overshooting, instability, and slow response times if not properly tuned. Noise in the measured variables can also lead to performance issues, as the derivative term can amplify this noise, leading to erratic control signals. Furthermore, the process model might not accurately represent the real system dynamics, especially in non-linear systems, making it difficult to set the right tuning parameters. Achieving a balance between responsiveness and stability often requires careful tuning and sometimes modifications to the controller algorithm or hardware.

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