Filtered PI and PID control of an Arduino based thermal plant

Huba, Mikuláš; Bisták, Pavol; Huba, T.

doi:10.1016/j.ifacol.2016.12.060

Cited by 19 publications

(4 citation statements)

References 7 publications

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“…For illustrating the controllers proposed, temperature control of an Arduino-based laboratory plant TOM1A [70][71][72] will be considered. Its heat channel consists of a 5 W bulb representing a heat source, a cooling fan used to generate disturbances, and a temperature sensor.…”

Section: Illustrative Example-temperature Controlmentioning

confidence: 99%

“…Thereby, for the mentioned laboratory plant, the identification results from the previous papers [70,71] yield K sm = 0.01; a m = 0.05s −1 ; T dm = 0.3s; T s = 0.02s (57) The identified plant dead time covers delay of the faster heat transfer by radiation, contribution of several possible shorter time delays and transport delay due to information processing and control signal calculation. Parameter a m corresponds to a plant time constant T 1m = 1/a m ≈ 20s, or it is chosen as a m = 0 (for IPDT model).…”

Section: Loop Dynamics Modellingmentioning

confidence: 99%

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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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Section: Illustrative Example-temperature Controlmentioning

confidence: 99%

Section: Loop Dynamics Modellingmentioning

confidence: 99%

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

et al. 2021

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“…In the work of Sobota (2013) an Arduino board was used for interaction with the physical world via its inputs and outputs and the REX Control System (Balda;Schlegel, 2012) was used for the algorithms monitoring. Huba;Bisták and Huba (2016) developed an Arduino based thermos-opto-mechanical laboratory plant to study different control tuning strategies using filter approaches. Docekal and Golembiovsky (2018) designed a low-cost laboratory plant for control system education based on an Arduino platform.…”

Section: Introductionmentioning

confidence: 99%

Use of Arduino for Monitoring the Air Quality of Indoor Environments

Siqueira

Santos

2022

J. Eng. Exact Sci.

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In this work, a prototype was developed to assess indoor air quality using an Arduino system. The system is based on the measurement of the light level, smoke concentration, temperature and air humidity. A thermal comfort index (TCI) was proposed, where a scale from 0 to 1 based on the weighted average of the individual indices was developed. Another approach, based on artificial neural networks, was formulated in which the parameters were used as input arguments of the network and the TCI was used as a target parameter. The response of the two models was compared, where it was possible to observe that the TCI values calculated by the network were considerably close to the values obtained by the deterministic model, with MSE of 1.73?10-5.

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“…From the closed-loop transients, we expected to reach the highest possible speed under the given control constraints, with the minimal deviations from ideal shapes of piecewise monotonic responses [13,20]. The paper, which is an amalgamation of several publications on the subject (see, e.g., [2,[30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48]) provides a refinement of the original step-responsebased method of Ziegler and Nichols in terms of the nearly time-optimal responses to step setpoint and input disturbances changes in systems with limited control action also under the influence of measurement noise. It is written pragmatically with the aim of making the subject accessible to the widest possible audience.…”

Section: Introductionmentioning

confidence: 99%

Making the PI and PID Controller Tuning Inspired by Ziegler and Nichols Precise and Reliable

Huba

Chamraz

Bisták

et al. 2021

Sensors

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This paper deals with the design of a DC motor speed control implemented by an embedded controller. The design is simple and brings some important changes to the traditional Ziegler–Nichols tuning. The design also includes a novel anti-windup implementation of the controller and an integrated noise-reduction filter design. The proposed tuning method considers all important aspects of the control, such as pre-processing of the measured signals and filtering (to attenuate the measurement noise), time delays of the process, modeling and identification of the process, and constraints on the control signal. Three important aspects of designing PI and PID controllers for processes with noisy output on Arduino-type embedded computers are considered. First, it deals with the integrated design of the input filter and the controller parameters, since both are interdependent. Secondly, the method of setting the controllers from step responses by Ziegler and Nichols is modified for the case of digital signal processing (without drawing the tangent), while it recommends the suitability of its modification in terms of the use of both integral and static models. Third, the most suitable anti-windup solution for the given controller structure is proposed. In summary, the paper shows that an appropriate design of the embedded controller can achieve excellent closed-loop performance even in a noisy process environment with limited control signals.

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Filtered PI and PID control of an Arduino based thermal plant

Cited by 19 publications

References 7 publications

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

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

Use of Arduino for Monitoring the Air Quality of Indoor Environments

Making the PI and PID Controller Tuning Inspired by Ziegler and Nichols Precise and Reliable

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