Simple Analytic PID Controller Tuning Rules Revisited

Lee, Jietae; Cho, Wonhui; Edgar, Thomas F.

doi:10.1021/ie4009919

Cited by 77 publications

(50 citation statements)

References 8 publications

(27 reference statements)

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“…2.1 vs. some existing model-based PID tuning methods, i.e. the Simple Internal Model Control (SIMC) (Skogestad (2003)) and the Korean-SIMC (K-SIMC) method (Lee et al (2014)). …”

Section: Linear Examplesmentioning

confidence: 99%

PD/PID controller tuning based on model approximations: Model reduction of some unstable and higher order nonlinear models

Dalen¹,

Ruscio²

2017

MIC

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A model reduction technique based on optimization theory is presented, where a possible higher order system/model is approximated with an unstable DIPTD model by using only step response data. The DIPTD model is used to tune PD/PID controllers for the underlying possible higher order system. Numerous examples are used to illustrate the theory, i.e. both linear and nonlinear models. The Pareto Optimal controller is used as a reference controller.

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“…2.1 vs. some existing model-based PID tuning methods, i.e. the Simple Internal Model Control (SIMC) (Skogestad (2003)) and the Korean-SIMC (K-SIMC) method (Lee et al (2014)). …”

Section: Linear Examplesmentioning

confidence: 99%

PD/PID controller tuning based on model approximations: Model reduction of some unstable and higher order nonlinear models

Dalen¹,

Ruscio²

2017

MIC

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“…The integral, proportional, and derivative terms were added to calculate the output of the PID controller. Defining u(t) as the controller output (Expected steering angle), the final form of the PID algorithm is as following [11,12]: The digital implementation of a PID controller requires the standard form of the PID controller to be discretized in a microcomputer, microcontroller (MCU) or FPGA device [13]. The first-order derivatives were obtained by backward finite differences.…”

Section: Pid Controller Algorithmmentioning

confidence: 99%

An Adaptive Cruise Control System Based on ARM CPU and PID for Agricultural Vehicles

Li¹,

Zhang²,

Zhu³

2014

TOAUTOCJ

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Abstract:The control system plays an important role in the agricultural vehicle guidance system. The previous guidance control system based on PI controller is the only linear, non-dynamic system, and does not work well under the complex cruising condition such as the typical nonlinear, dynamic system in the farm fields. This paper developed an Adaptive Cruise Control (ACC) system based on agricultural vehicle control system for improving its efficiency based on ARM microprocessor and PID algorithm. The real-time experiments on YTO plough tractor achieved better performance index characterizing the operation of the proposed ACC system. This work may provide a proper scheme compared with the present existing linear ACC which can be replaced by a better, nonlinear, more efficient adaptive control system.

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“…A large number of tuning rules are available for linear PI controllers. [3][4][5][6] When operating points are changing for nonlinear processes, linearized model parameters will also change. For nonlinear processes with mild changes of linearized model parameters, robust linear PI controllers designed based on the average or worst-case model parameters can be used.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear PI controllers with output transformations

Lee

Edgar

2015

AIChE Journal

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Well-designed nonlinear proportional-integral (PI) controllers are successful for nonlinear dynamical processes like linear PI controllers are for linear processes. Two nonlinear blocks representing proportional and integral terms can be designed so that the linearized controllers perform the same as linear PI controllers for linearized processes at the given operating points. For some nonlinear processes, nonlinear blocks for nonlinear PI controllers can be singular at some operating points, and control performances can be poor for set points near those points. To mitigate such disadvantages, new nonlinear PI controllers that introduce output transformations are proposed. Several examples are given, showing the performance of the proposed nonlinear PI controllers. V C 2015 American Institute of Chemical Engineers AIChE J, 61: [4264][4265][4266][4267][4268][4269] 2015

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Simple Analytic PID Controller Tuning Rules Revisited

Cited by 77 publications

References 8 publications

PD/PID controller tuning based on model approximations: Model reduction of some unstable and higher order nonlinear models

PD/PID controller tuning based on model approximations: Model reduction of some unstable and higher order nonlinear models

An Adaptive Cruise Control System Based on ARM CPU and PID for Agricultural Vehicles

Nonlinear PI controllers with output transformations

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