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Proportional-Integral-Derivative (PID) controllers are widely used in various control systems to enhance stability and performance. In a thermostat, it adjusts heating or cooling based on the temperature difference between the actual and desired levels. They are often used in automotive speed systems, effectively managing sudden speed changes while maintaining a constant speed under varying conditions. On the other hand, PI controllers, commonly employed in voltage regulation, enhance stability...
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Related Experiment Video

Updated: Jan 19, 2026

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Controlling industrial dead-time systems: When to use a PID or an advanced controller.

Lucian Ribeiro da Silva1, Rodolfo César Costa Flesch2, Julio Elias Normey-Rico2

  • 1Programa de Pós-Graduação em Engenharia de Automação e Sistemas, Universidade Federal de Santa Catarina, 88040-900 Florianópolis, SC, Brazil.

ISA Transactions
|September 19, 2019
PubMed
Summary

Comparing PID, DTC, and MPC control strategies for industrial processes with dead time, this study finds advanced methods offer minimal gains over PID for robust, unconstrained systems. However, complex strategies are justified for well-modeled processes, and PID with anti-windup rivals MPC for constrained systems.

Keywords:
ConstraintsDead-time compensatorsModel predictive controlPID controlTime delay

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Area of Science:

  • Process Control
  • Control Engineering
  • Industrial Automation

Background:

  • Industrial processes often feature dead time, noisy measurements, and modeling errors, complicating control system design.
  • Traditional Proportional-Integral-Derivative (PID) controllers are widely used but may face limitations in performance for complex dynamics.
  • More advanced strategies like Direct Torque Control (DTC) and Model Predictive Control (MPC) offer potential improvements but require more complex implementation.

Purpose of the Study:

  • To conduct a comparative analysis of PID, DTC, and MPC control strategies.
  • To evaluate their performance in Single-Input Single-Output (SISO) processes with dead time, considering industrial constraints.
  • To assess the impact of noisy measurements and modeling errors on control strategy effectiveness.

Main Methods:

  • Comparative analysis of PID, DTC, and MPC control algorithms.
  • Simulation of SISO processes with dead time under various conditions, including noise and model uncertainty.
  • Evaluation of controller performance based on criteria such as robustness, speed of response, and disturbance rejection.

Main Results:

  • For unconstrained processes requiring high robustness, advanced strategies offer little to no performance improvement over PID.
  • For processes with well-defined models, even small dead times justify the use of more complex control structures like DTC or MPC.
  • For constrained processes, PID controllers with anti-windup mechanisms can achieve performance comparable or superior to MPC, especially when robustness is prioritized.

Conclusions:

  • The choice of control strategy depends critically on process characteristics, including model accuracy, constraints, and robustness requirements.
  • PID controllers, particularly with anti-windup, remain a competitive and effective solution for many industrial control problems, even when compared to advanced methods.
  • Advanced control strategies like DTC and MPC demonstrate value in specific scenarios, such as well-modeled processes or when precise trajectory tracking is essential.