Increment Count Method and Its PLC-Based Implementation for Autotuning of Reduced-Order ADRC With Smith Predictor

Grelewicz, Patryk; Nowak, Paweł; Czeczot, Jacek; Musial, Jakub

doi:10.1109/tie.2020.3045696

Cited by 20 publications

(8 citation statements)

References 20 publications

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“…The result presented in Theorem 1 shows that as t → ∞, the first component of (12) resulting from the non-zero initial conditions converges to zero, while the norm of the overall estimation error is bounded around some vicinity of zero dependent on the values D and n. Although the general parametrization from ( 9)- (11) did not allow us to analytically present a direct correlation between the observer gains and the convergence speed or the disturbance rejection impact, result (12) proved that an increase of observer gains decrease the estimation robustness with respect to the measurement noise.…”

Section: Control Plant Description and Conventional Robust Control De...mentioning

confidence: 92%

“…In [39] and [6], the authors considered also the control cost as a factor that needs to be minimized to reduce the energy consumption of the robust control process, while in [26] the observation error of the measured signals was taken into account. Tuning procedures described in [28] and [12] have utilized prior knowledge about the plant structure and some known or identified model parameters to obtain assumed control performance requirements. In [28] and [14], the authors presented an observer tuning method that is relative to gains of the selected ADRC controller.…”

Section: Introductionmentioning

confidence: 99%

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Tuning of extended state observer with neural network-based control performance assessment

Kicki¹,

Łakomy²,

Lee³

2021

Preprint

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The extended state observer (ESO) is an inherent element of robust observer-based control systems that allows estimating the impact of disturbance on system dynamics. Proper tuning of ESO parameters is necessary to ensure a good quality of estimated quantities and impacts the overall performance of the robust control structure. In this paper, we propose a neural network (NN) based tuning procedure that allows the user to prioritize between selected quality criteria such as the control and observation errors and the specified features of the control signal. The designed NN provides an accurate assessment of the control system performance and returns a set of ESO parameters that delivers a near-optimal solution to the user-defined cost function. The proposed tuning procedure, using an estimated state from the single closed-loop experiment produces near-optimal ESO gains within seconds.

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Section: Control Plant Description and Conventional Robust Control De...mentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Tuning of extended state observer with neural network-based control performance assessment

Kicki¹,

Łakomy²,

Lee³

2021

Preprint

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“…PLCs are providing the bridge between the cyber and physical worlds by controlling devices such as valves, pumps, and motors in response to operator input or their preprogrammed control logic [19]. Input-output specification of the PLC-based function block for considered control law has to be compatible with the specification of the presented "Identification Block" [20]. The goal of PLC data collection is to record both the input and output values whenever there is a change in any of the I/O values [21].…”

Section: Introductionmentioning

confidence: 99%

Plumbing leakage detection system with water level detector controlled by programmable logic controller type Omron CPM2A

Hartanto

Desmayadi

2022

J. Mechatron. Electr. Power Veh. Technol.

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There is a chance of leakage in the plumbing caused by water pressure in the pipes, improper installation of pipe connections, or external influences, such as earthquakes. Plumbing leakage that is detected too late can cause damage to other systems. It is necessary to have a plumbing leakage detection system to detect a leak in the plumbing. Therefore, in this research, a plumbing leakage detection system is designed with a water level detector (WLD) controlled by a programmable logic controller (PLC) type Omron CPM2A. The method used in this research is designing the optimal model form of the system, which is distinguished by designing hardware and software, testing the devices, such as power supply, WLD, and channel relay module (CRM), and making conclusions. From the results of this research, it was found that the system works well in detecting leakage of plumbing, as indicated by all transistors' ability to work well where the electrodes (E1 and E2) are connected by water. The transistor in the WLD module will work as a switch or transistor in the saturation position. In this research, it can be seen that even though there is a leakage from the relay contacts of 1.8 VDC, it is still considered in a safe condition because to provide a trigger to the 3B3D Module, a minimum of 12 VDC is required. In addition, when the relay is not working or off, the measurement at the normally closed (NC) terminal is 12 VDC.

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“…The implemented ADRC algorithm consists of linear functions that simplify the design. In [20,21,22], various general-purpose function blocks were developed for industrial implementation of ADRC in programmable logic controllers (PLCs). Two versions of ADRC library for PLCs were proposed in [20], one as a standard version (with basic functionalities) and the other dedicated for experienced ADRC users.…”

Section: Introductionmentioning

confidence: 99%

“…The latter provides additional features, like partial dynamic model incorporation, relative disturbance order selection, and nonlinear estimation error function. In [21,22], the idea of general-purpose ADRC function block for PLCs has been realized from the perspective of typical process control systems. It thus addresses practically important aspects like the impact of the derivative backoff, autotuning (based only on the process step response), or reduced-order ADRC and its combination with a Smith predictor for handling system time-delay.…”

Section: Introductionmentioning

confidence: 99%

Active Disturbance Rejection Control (ADRC) Toolbox for MATLAB/Simulink

Giernacki¹,

Łakomy²,

Michalski³

et al. 2021

Preprint

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In this paper, an active disturbance rejection control (ADRC) toolbox for MAT-LAB/Simulink is introduced. Although ADRC has already been established as a powerful robust control framework with successful industrial implementations and strong theoretical foundations, a comprehensive tool for computer-aided design of ADRC has not been developed until now. The proposed ADRC Toolbox is a response to a growing need of both scientific community and control industry looking for a straightforward software utilization of the ADRC methodology. Its main purpose is to fill the gap between current theories and applications of ADRC and to provide an easy-to-use solution to users in various control fields wanting to employ the ADRC scheme in their applications. The ADRC Toolbox contains a single, general-purpose, drag-and-drop function block allowing to synthesize a predefined ADRC-based strategy with minimal design effort. Its efficacy is validated here in both simulations and hardware experiments, conducted using a variety of problems known from motion, process, and power control areas. The proposed ADRC Toolbox is an open-source project 1 .

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Increment Count Method and Its PLC-Based Implementation for Autotuning of Reduced-Order ADRC With Smith Predictor

Cited by 20 publications

References 20 publications

Tuning of extended state observer with neural network-based control performance assessment

Tuning of extended state observer with neural network-based control performance assessment

Plumbing leakage detection system with water level detector controlled by programmable logic controller type Omron CPM2A

Active Disturbance Rejection Control (ADRC) Toolbox for MATLAB/Simulink

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