PID control system analysis, design, and technology

By Kiam Heong Ang, G. Chong, Yun Li. IEEE Transactions on Control Systems Technology, 2005

3,018 citations recorded by the source corpus.

Subfields: Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/819e12c46c95add85a4bbfcffa8fe6ba5b1924a4

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

1. Limin Zhu
2. Guoying Gu
3. Han Ding
4. Sergej Fatikow
5. Afeez Abidemi
6. Dirk Abel
7. Emmanuel Gbenga Dada
8. Cees Bil

Citations in the corpus

• Modeling and Control of Piezo-Actuated Nanopositioning Stages: A Survey (2016, background): “With its three-term functionality covering treatment to both transient and steady-state responses, PID control offers the simplest solution and yet most efficient solution in industrial applications [161].”
• Review on model predictive control: an engineering perspective (2021, background): “The PID controller is the best known controller with an outstanding importance and spread in industrial applications [4].”
• Extending the Symmetrical Optimum criterion to the design of PID type-p control loops (2012, background): “Throughout the literature, it is evident that the PID control aw offers the simplest, feasible and yet most efficient solution o many real-world control problems, see [5–22].”
• Metaheuristic algorithms for PID controller parameters tuning: review, approaches and open problems (2022, methodology): “…based on nature and usage can be classified in five (5) categories (the composition can be found in Rodríguez-Molina et al. (2020); Villarreal-Cervantes and Alvarez-Gallegos (2016); Ang et al. (2005)): i. Analytical methods: These methods involve analysis of closed loop model to attain stability.”
• PID controller design for robust performance (2013, methodology): “For such reasons, even now, the concern about the powerful design method of a PID controller is very high, and many papers have been reported actively [1] , Optimization methods for determining PID parameters are undertaken by many authors.”
• Backstepping control design for motion coordination of self‐propelled vehicles in a flowfield (2011, background): “Comparison to proportional control Since proportional control is widely used in engineering applications and simple to understand [20], we compare backstepping to a proportional control law.”
• Regenerative Braking System of Electric Vehicle Driven by Brushless DC Motor (2014, methodology): “When the fuzzy reasoning is slower than PID control, the braking torque can be real-time controlled by PID control [14].”
• Predictive Maintenance and Intelligent Sensors in Smart Factory: Review (2021, background): “After omitting medical and ecological articles, the most cited publications focused on proportional-integral-derivative (PID) control [79], monitoring, and fault diagnosis in production [80].”
• Parameter-efficient fine-tuning of large-scale pre-trained language models (2023, methodology): “Finally, by applying the theories of controller design 50,51 , we expect more delta-tuning methods proposed with theoretical guarantees and better exploitation of the power of PLMs 52 .”
• Wing morphing control with shape memory alloy actuators (2013, methodology): “The PID controller with anti-windup compensator was implemented using an incremental algorithm (Ahola et al., 2009; Ang et al., 2005; Zhang et al., 2003) (Figure 13).”
• PID control system analysis and design (2006, neutral): “ACKNOWLEDGMENTS This article is based on [25].”
• Conditional Affordance Learning for Driving in Urban Environments (2018, background): “An autonomous vehicle is a cognitive system and hence follows the concept of a rational agent: in order to operate safely it must accurately observe its environment, make robust decisions and perform actions based on these decisions [1].”