A proportional–integral–derivative controller (PID controller or three-term controller) is a control loop mechanism employing feedback that is widely used in industrial control systems and a variety of other applications requiring continuously modulated control. A PID controller continuously calculates an error value as the difference between a desired setpoint (SP) and a measured process variable (PV) and applies a correction based on proportional, integral, and derivative terms (denoted P, I, and D respectively), hence the name. PID systems automatically apply accurate and responsive correction to a control function. An everyday example is the cruise control on a car, where ascending a hill would lower speed if constant engine power were applied. The controller's PID algorithm restores the measured speed to the desired speed with minimal delay and overshoot by increasing the power output of the engine in a controlled manner. The first theoretical analysis and practical application of PID was in the field of automatic steering systems for ships, developed from the early 1920s onwards. It was then used for automatic process control in the manufacturing industry, where it was widely implemented in pneumatic and then electronic controllers. Today the PID concept is used universally in applications requiring accurate and optimized automatic control. The distinguishing feature of the PID controller is the ability to use the three control terms of proportional, integral and derivative influence on the controller output to apply accurate and optimal control. The block diagram on the right shows the principles of how these terms are generated and applied. It shows a PID controller, which 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 controller attempts to minimize the error over time by adjustment of a control variable , such as the opening of a control valve, to a new value determined by a weighted sum of the control terms.

About this result
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.
Related courses (12)
ME-524: Advanced control systems
This course covers some theoretical and practical aspects of robust and adaptive control. This includes H-2 and H-infinity control in model-based and data-driven framework by convex optimization, dire
EE-465: Industrial electronics I
The course deals with the control of grid connected power electronic converters for renewable applications, covering: converter topologies, pulse width modulation, modelling, control algorithms and co
ME-321: Control systems + TP
Provides the students with basic notions and tools for the analysis and control of dynamic systems. Shows them how to design controllers and analyze the performance of controlled systems.
Show more
Related lectures (69)
Cascade Control: Stability Limits and Offset Error Analysis
Discusses stability limits, offset error analysis, and properties of cascade control systems.
StateSpace Reference Tracking
Covers reference tracking in state-space systems and implementing control laws.
Design of Feedforward Controllers
Covers the design of feedforward controllers and ideal disturbance rejection in process control.
Show more
Related publications (366)

Violation-aware contextual Bayesian optimization for controller performance optimization with unmodeled constraints

Colin Neil Jones, Bratislav Svetozarevic, Wenjie Xu

We study the problem of performance optimization of closed -loop control systems with unmodeled dynamics. Bayesian optimization (BO) has been demonstrated to be effective for improving closed -loop performance by automatically tuning controller gains or re ...
Elsevier Sci Ltd2024

Robust Data-Driven Controller Design with Finite Frequency Samples

Alireza Karimi, Philippe Louis Schuchert

Modern control synthesis methods rely on accurate models to derive a performant controller. Obtaining a good model is often a costly step, and has led to a renewed interest in data-driven synthesis methods. Frequency-response-based synthesis methods have b ...
2024

Koopman-based Data-driven Robust Control of Nonlinear Systems Using Integral Quadratic Constraints

Alireza Karimi, Mert Eyuboglu

This paper introduces a novel method for data-driven robust control of nonlinear systems based on the Koopman operator, utilizing Integral Quadratic Constraints (IQCs). The Koopman operator theory facilitates the linear representation of nonlinear system d ...
2024
Show more
Related MOOCs (21)
IoT Systems and Industrial Applications with Design Thinking
The first MOOC to provide a comprehensive introduction to Internet of Things (IoT) including the fundamental business aspects needed to define IoT related products.
Show more

Graph Chatbot

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.