Seeking a unique view to control of simple models

Huba, Mikuláš; Žáková, Katarı́na; Bisták, Pavol; Hypiusova, Maria; Ťapák, Peter

doi:10.1016/j.ifacol.2019.08.130

Cited by 11 publications

(8 citation statements)

References 6 publications

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“…In PID m n control this convergence improves with increasing m [26]. Starting from the controller tuning by Ziegler and Nichols [51], similar properties applicable to simplification of the controller tuning may also be found in other model-based control structures 3 Implementation of FO-PID controllers is generally based on approximations by higher order IO transfer function [49], [5], [8], but together with the fractional-orders choice it further requires specification of several additional filter parameters [50], [52], even when they use the adjective model-free. The magic of integral models denoted as "ultra-local" ones, in contrast to the usual "local" linear models, is used both in the intelligent PID control [53] and in the advanced disturbance rejection control [54].…”

Section: E Magic Of Integral Modelsmentioning

confidence: 91%

PID Control With Higher Order Derivative Degrees for IPDT Plant Models

2021

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Section: E Magic Of Integral Modelsmentioning

confidence: 91%

PID Control With Higher Order Derivative Degrees for IPDT Plant Models

2021

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“…Use of output disturbances was typical for the internal model control (IMC). Since it lead to several problems in control of integrative and unstable plants (as unobservable output disturbances of integrative plants, see e.g., [21,41], or unbounded disturbances of unstable ones [42]), it is more appropriate to deal with input disturbances d, as in ADRC. There, all the plant-model discrepancies and external actions are merged into an eqwuivalent input disturbances.…”

Section: Extended State Observermentioning

confidence: 99%

“…At the same time, the considered structures have been used to illustrate the core ideas behind the commonly used, but usually not sufficiently clarified, ADRC structures. Since with each simplification we get closer to oversimplification, the method should be presented clearly enough and should be compatible to other alternative control designs [21], especially those tending to higher-order controllers [22] with a more effective noise attenuation.…”

Section: Introductionmentioning

confidence: 99%

A Set of Active Disturbance Rejection Controllers Based on Integrator Plus Dead-Time Models

et al. 2021

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The paper develops and investigates a novel set of constrained-output robust controllers with selectable response smoothing degree designed for an integrator-plus-dead-time (IPDT) plant model. The input-output response of the IPDT system is internally approximated by several time-delayed, possibly higher-order plant models of increasing complexity. Since they all contain a single integrator, the presented approach can be considered as a generalization of active disturbance rejection control (ADRC). Due to the input/output model used, the controller commissioning can be based on a simplified process modeling, similar to the one proposed by Ziegler and Nichols. This allows it to be compared with several alternative controllers commonly used in practice. Its main advantage is simplicity, since it uses only two identified process parameters, even when dealing with more complex systems with distributed parameters. The proposed set of controllers with increasing complexity includes the stabilizing proportional (P), proportional-derivative (PD), or proportional-derivative-acceleration (PDA) controllers. These controllers can be complemented by extended state observers (ESO) for the reconstruction of all required state variables and non-measurable input disturbances, which also cover imperfections of a simplified plant modeling. A holistic performance evaluation on a laboratory heat transfer plant shows interesting results from the point of view of the optimal least sensitive solution with smooth input and output.

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“…The disturbance d i delayed equally as the measured plant output may then be simply calculated by subtracting the equally delayed controller output from the reconstructed plant input signal [45,46]. Similarly as in [59], with respect to ESO, the DOB-reconstruction delay may be decreased by one step to k t = k + 1 steps.…”

Section: Controller Design For the Shortest Possible Transient Responsesmentioning

confidence: 99%

Dead-Time Compensation for the First-Order Dead-Time Processes: Towards a Broader Overview

et al. 2021

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The article reviews the results of a number of recent papers dealing with the revision of the simplest approaches to the control of first-order time-delayed systems. The concise introductory review is extended by an analysis of two discrete-time approaches to dead-time compensation control of stable, integrating, and unstable first-order dead-time processes including simple diagnostics of the model used and focusing on the possibility of simplified but reliable plant modelling. The first approach, based on the first historically known dead-time compensator (DTC) with possible dead-beat performance, is based on the reconstruction of the actual process variables and the compensation of input disturbances by an extended state observer (ESO). Such solutions play an important role both in a disturbance observer (DOB) based control and in an active disturbance rejection control (ADRC). The second approach considered comes from the Smith predictor with two degrees of freedom, which combines feedforward control with output disturbance reconstruction and compensation by the parallel plant model. It is shown that these two approaches offer advantageous properties in the case of actuator limitations, in contrast to the commonly used PID controllers. However, when applied to integrating and unstable first-order systems, the unconstrained and possibly unobservable output disturbance signal of the second solution must be eliminated from the control loop, due to the hidden structural instability of the Smith predictor-like solutions. The modified solutions, usually referred to as filtered Smith predictor (FSP), then no longer provide a disturbance signal and thus no longer fully fit into the concept of Industry 4.0, which is focused on further optimization, predictive maintenance in dynamic systems, diagnosis, fault detection and fault identification of dynamic processes and forms the basis for the digitalization of smart production. Nevertheless, the detailed analysis of the elimination of the unstable disturbance response mode is also worth mentioning in terms of other possible solutions. The application of both approaches to the control of a thermal process shows almost equivalent quality, but with different dependencies on the tuning parameters used. It is confirmed that a more detailed identification of the controlled process and the resulting higher complexity of the control algorithms does not necessarily lead to an increase in the resulting quality of the transients, which underlines the importance of the simplified plant modelling for practice.

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Seeking a unique view to control of simple models

Cited by 11 publications

References 6 publications

PID Control With Higher Order Derivative Degrees for IPDT Plant Models

PID Control With Higher Order Derivative Degrees for IPDT Plant Models

A Set of Active Disturbance Rejection Controllers Based on Integrator Plus Dead-Time Models

Dead-Time Compensation for the First-Order Dead-Time Processes: Towards a Broader Overview

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