The Model-Based Disturbance Rejection with MOMI Tuning Method for PID Controllers

Vrančić, Damir; Oliveira, P. B. de Moura; Cvejn, Jan

doi:10.1007/978-3-319-43671-5_8

Cited by 2 publications

(7 citation statements)

References 10 publications

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“…To improve the disturbance-rejection performance, the optimisation criteria of the MOMI method were modified accordingly. The new method, referred to as the DRMO (Disturbance-Rejection-Magnitude-Optimum) method, achieved significantly improved disturbance rejection performance [9,16,17].…”

Section: Momi and Drmo Tuning Methodsmentioning

confidence: 99%

“…The PID controller parameters are calculated according to the following expressions when using the DRMO method [9,16,17]:…”

Section: Momi and Drmo Tuning Methodsmentioning

confidence: 99%

“…should be optimised according to the modified MO criterion [9,16]. Note that expression (17) holds when the process and the model transfer functions are equivalent. Since the disturbance filter time constant is defined, and all of the controller and the model parameters are calculated, the only optimisation parameter is the gain K FD .…”

Section: De-momi Tuning Methodsmentioning

confidence: 99%

“…Similarly, the MOMI method primarily optimises the tracking response, while its modification, the Disturbance-Rejection-Magnitude-Optimum (DRMO) method, aims at optimising the disturbance rejection response. The latter significantly improves the disturbance rejection response, while the tracking response slows down due to the implemented reference-weighting gain or reference signal filter [9,16,17].…”

Section: Introductionmentioning

confidence: 99%

“…First, the parameters of the PID controller are optimised for tracking performance. Then, a simple disturbance estimator is introduced to significantly increase the disturbance rejection performance [18,19]. The advantages of the above approach are twofold.…”

Section: Introductionmentioning

confidence: 99%

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Improving Disturbance-Rejection by Using Disturbance Estimator

Vrančić¹,

Huba²

2021

Control Based on PID Framework - The Mutual Promotion of Control and Identification for Complex Systems

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The main tasks of control in various industries are either tracking the setpoint changes or rejecting the process disturbances. While both aim at maintaining the process output at the desired setpoint, the controller parameters optimised for setpoint tracking are generally not suitable for optimal disturbance rejection. The overall control performance can be improved to some extent by using simpler 2-DOF PID controllers. Such a controller structure allows the disturbance rejection to be optimised, while it also improves the setpoint tracking performance with additional controller parameters (usually through the setpoint weighting factors). Since such 2-DOF structures are usually relatively simple, the optimization of tracking performance is usually limited to the reduction of process overshoots instead of achieving an optimal (fast) tracking response. In this chapter, an alternative approach is presented in which the parameters of the PID controller are optimised for reference tracking, while the performance of the disturbance rejection is substantially increased by introducing a simple disturbance estimator approach. The mentioned estimator requires adding two simple blocks to the PID controller. The blocks are the second-order transfer functions whose parameters, including the PID controller parameters, can be calculated analytically from the process characteristic areas (also called process moments). The advantage of such an approach is that the mentioned areas can be analytically calculated directly from the process transfer function (of any order with time delay) or from the time response of the process when the steady state of the process is changed. Both of the above calculations are absolutely equivalent. Moreover, the output noise of the controller is under control as it is considered in the design of the controller and compensator. The closed loop results on several process models show that the proposed method with disturbance estimator has excellent tracking and disturbance rejection performance. The proposed controller structure and tuning method also compare favourably with some existing methods based on non-parametric description of the process.

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Section: Momi and Drmo Tuning Methodsmentioning

confidence: 99%

“…The PID controller parameters are calculated according to the following expressions when using the DRMO method [9,16,17]:…”

Section: Momi and Drmo Tuning Methodsmentioning

confidence: 99%

Section: De-momi Tuning Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Improving Disturbance-Rejection by Using Disturbance Estimator

Vrančić¹,

Huba²

2021

Control Based on PID Framework - The Mutual Promotion of Control and Identification for Complex Systems

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The magnitude optimum tuning of the PID controller: Improving load disturbance rejection by extending the controller

Cvejn

Vrančić

2017

Transactions of the Institute of Measurement and Control

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The Magnitude Optimum (MO) tuning method for PID controllers, applied on stable and non-oscillating plants, usually gives fast tracking responses and offers very good process output disturbance-rejection performance, even if the process contains significant dead time. On the other hand, when an exogenous disturbance affects the process indirectly, for example, via the plant input, slow disturbance rejection responses may be obtained. The paper proposes a way of removing this problem by means of adding two first-order filters into the control loop, without modifying the controller parameters. The filter parameters are determined so that the disturbance lag is partially compensated and the stability margin properties of the MO tuning are preserved.

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The Model-Based Disturbance Rejection with MOMI Tuning Method for PID Controllers

Cited by 2 publications

References 10 publications

Improving Disturbance-Rejection by Using Disturbance Estimator

Improving Disturbance-Rejection by Using Disturbance Estimator

The magnitude optimum tuning of the PID controller: Improving load disturbance rejection by extending the controller

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