PID Controllers: Theory, Design, and Tuning

By K. Åström. 1995

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Citations in the corpus

Programmatically Interpretable Reinforcement Learning (2018, background): “Speciﬁcally, in T ORCS , our policy sketch allows an unbounded set of programs with branches guarded by unknown conditions, each branch representing a Proportional-Integral-Derivative (PID) controller ( ˚Astr¨om & H¨agglund, 1995) with unknown parameters.”
Comparison and Evaluation of Anti-windup PI Controllers (2011, methodology): “There exists a large overshoot, a slow settling time, and, sometimes, even instability in the speed response [1], [2].”
Increasing the Level of Automation in the Forestry Logging Process with Crane Trajectory Planning and Control (2014, methodology): “The gains of the PID controllers are tuned with the help of optimization methods (Åström and Hägglund, 1995), and fine-tuned onsite. The objective of the controller is tracking reference joint trajectories, which is accomplished utilizing position feedback and nonlinear compensation; see Figure 12. ”
The kinematic bicycle model: A consistent model for planning feasible trajectories for autonomous vehicles? (2017, methodology): “4) Model-free Control: At last, controllers based on correcting the tracking error with no modeling of the system such as the popular Proportional-Integral-Derivative (PID) controller [28] or the recently introduced Model-free Control [29] can be used to control the vehicles.”
Design of optimal disturbance rejection PID controllers using genetic algorithms (2001, methodology): “Another objective of the paper is to show the control engineer how to utilize an intelligent method based on evolutionary computation for the design of PI/PID controllers widely accepted in automated environments [8].”
PID control system analysis, design, and technology (2005, unknown): “An excellent summary on PID tuning methods can be found in [4], [18], [26], and [28].”
Foundations of fuzzy control (2007, methodology): “If PID control (proportional-integral-derivative control) is inadequate – for example, in the case of higher-order plants, systems with a long deadtime, or systems with oscillatory modes (Åström and Hägglund 1995) – fuzzy control is an option.”