Handbook of Pi and Pid Controller Tuning Rules

O’Dwyer, Aidan

doi:10.1142/9781860949104

Cited by 393 publications

(147 citation statements)

References 10 publications

(1 reference statement)

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“…The PID•filter controller in Eq. (8) is an extension to the modified PID controller structure developed by Rivera et al [2], Horn et al [5], Lee et al [6] Shamsuzzoha and Lee [8][9][10] and Dwyer [11].…”

Section: Imc-pid Approach For Pid•filter Controller Designmentioning

confidence: 99%

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Analytical design of PID controller cascaded with a lead-lag filter for time-delay processes

Shamsuzzoha

Lee

2009

Korean J. Chem. Eng.

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An analytical method for the design of a proportional-integral-derivative (PID) controller cascaded with a second-order lead-lag filter is proposed for various types of time-delay process. The proposed design method is based on the IMC-PID method to obtain a desired, closed-loop response. The process dead time is approximated by using the appropriate Pade expansion to convert the ideal feedback controller to the proposed PID•filter structure with little loss of accuracy. The resulting PID•filter controller efficiently compensates for the dominant process poles and zeros and significantly improves the closed-loop performance. The simulation results demonstrate the superior performance of the proposed PID•filter controller over the conventional PID controllers. A guideline for the closed-loop time constant, λ, is also suggested for the FOPDT and SOPDT models.

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Section: Imc-pid Approach For Pid•filter Controller Designmentioning

confidence: 99%

“…Consider a general second-order plus dead time (SOPDT) process system, (11) where ζ is the damping factor. The process model is decomposed into P M =K/(τ 2 s 2 +2τζs+1) and P A =e −θs .…”

Section: Second-order Plus Dead Time Processmentioning

confidence: 99%

Analytical design of PID controller cascaded with a lead-lag filter for time-delay processes

Shamsuzzoha

Lee

2009

Korean J. Chem. Eng.

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“…Assume that the real parameter a can be chosen arbitrarily, provided that the constraint a > 1 holds. Moreover, let u max be the value satisfying (14). If we define Q = u/a 2 , we can choose a so that: 1 + Q 2 m = β Q m , where Q m corresponds to u max and β > 0 is a new parameter depending on Q m and hence on a only.…”

Section: The Proposed Tuning Methodsmentioning

confidence: 99%

“…However, in spite of their central role in control engineering, PID have attracted relatively little attention of research community (see [1]). Only recently, indeed, many authors have observed that the practice of PID can take considerable benefits from specific research in this field (see [1,14]). Certainly, a better understanding of these control devices is highly desirable because it can contribute to enhance the quality of products and the efficiency of manufacturing.…”

mentioning

confidence: 99%

New tuning rules for fractional PIα controllers

Maione

Lino

2006

Nonlinear Dyn

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This paper describes a new tuning method for fractional PI α controllers. The main theoretical contribution of the paper is the analytical solution of a nonlinear function minimization problem, which plays a central role in deriving the tuning formulae. These formulae take advantage of the fractional order α to offer an excellent tradeoff between dynamic performances and stability robustness. Finally, a position control is implemented to compare laboratory experiments with computer simulations. The comparison results show the good performance of the tuning formulae.

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“…Each node i determines, based on the event intensity sensed by itself and by neighboring nodes, the normalized vectorx ie (t) directed toward the event, and modulates the intensity of the exchange forces by dynamically adapting G ik (t) so as to reach, at the equilibrium, the target inter-nodal distance. More specifically, G ik (t) is computed exploiting a first order Proportional Integrative (PI) controller driven by the error between the current distance d ik (t) and the target value d T ik (t) [19] . Notice that our solution does not require global knowledge of the absolute position of the event.…”

Section: Algorithmmentioning

confidence: 99%

Sensor Deployment and Relocation: A Unified Scheme

Garetto

Gribaudo

Chiasserini

et al. 2008

J. Comput. Sci. Technol.

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Sensor networks are envisioned to revolutionize our daily life by ubiquitously monitoring our environment and/or adjusting it to suit our needs. Recent progress in robotics and low-power embedded systems has made it possible to add mobility to small, light, low-cost sensors to be used in teams or swarms. Augmenting static sensor networks with mobile nodes addresses many design challenges that exist in traditional static sensor networks. This paper addresses the problem of topology control in mobile wireless networks. Limitations in communication, computation and energy capabilities push towards the adoption of distributed, energy-efficient solutions to perform self-deployment and relocation of the nodes. We develop a unified, distributed algorithm that has the following features. During deployment, our algorithm yields a regular tessellation of the geographical area with a given node density, called monitoring configuration. Upon the occurrence of a physical phenomenon, network nodes relocate themselves so as to properly sample and control the event, while maintaining the network connectivity. Then, as soon as the event ends, all nodes return to the monitoring configuration. To achieve these goals, we use a virtual force-based strategy which proves to be very effective even when compared to an optimal centralized solution. We assess the performance of our approach in the presence of events with different shapes, and we investigate the transient behavior of our algorithm. This allows us to evaluate the effectiveness and the response time of the proposed solution under various environmental conditions

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Handbook of Pi and Pid Controller Tuning Rules

Cited by 393 publications

References 10 publications

Analytical design of PID controller cascaded with a lead-lag filter for time-delay processes

Analytical design of PID controller cascaded with a lead-lag filter for time-delay processes

New tuning rules for fractional PIα controllers

Sensor Deployment and Relocation: A Unified Scheme

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