PID temperature controller in pig nursery: spatial characterization of thermal environment

Barros, Juliana de Souza Granja; Rossi, Luiz Alexandre Solano; Souza, Zigomar Menezes de

doi:10.1007/s00484-017-1479-x

Cited by 9 publications

(2 citation statements)

References 22 publications

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“…For example, at work [2], a PID is used to control a mobile firefighter robot. The authors of [3] use a PID controller to control the thermal environment variables in a pig farm, obtaining better performance than overhead electrical resistance with a thermostat. At work [4], the authors develop a two-degree PID to control the motor speed in an automatic lawnmower that uses solar energy as the primary source.…”

Section: Introductionmentioning

confidence: 99%

Self-Tuning Neural Network PID With Dynamic Response Control

et al. 2021

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PID controllers are widely used and adaptable to various types of systems. However, for the response to be adequate under different conditions, the PID gains must be adjusted. The tuning is made according to the difference between the reference value and the real value (error). This work presents a selfadjusting PID controller based on a backpropagation artificial neural network. The network calculates the appropriate gains according to the desired output, that is, the dynamic response desired which is composed of the transient part and the stationary part of the step response of a system. The contribution of the work is that in addition to using the error for network training, the maximum desired values of overshoots, settling times, and stationary errors were used as input data for the network. An offline training database was created using genetic algorithms to obtain the dynamic response data associated with PID gains. The genetic algorithm allows getting data in different operating ranges and allows using only stable gains combinations. The database was used for training. Subsequently, the neural network estimates an appropriate gain combination, adapting to the error and the desired response. The method performance is evaluated by controlling the speed of a direct current motor. The results indicate an average error of 4% for the database between the requested and system response. On the other hand, the gains estimated by the network in the test dataset (1544 combinations) did not cause instability and complying with the expected dynamic response in 86% of the dataset.

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Section: Introductionmentioning

confidence: 99%

Self-Tuning Neural Network PID With Dynamic Response Control

et al. 2021

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“…In this paper, we adopt micro bimodal sensors that can simultaneously detect spatial airflow and temperature to support a fast, robust, self-adaptive thermal and energy management. However, at the same time, it is not suitable for multi-input multi-output (MIMO) control problems [10][11][12]. The MIMO temperature control problem is complex because of the strong coupling that exists in the controlled object.…”

Section: Introductionmentioning

confidence: 99%

Thermal and Energy Management Based on Bimodal Airflow-Temperature Sensing and Reinforcement Learning

Zhang

Zhu

2018

Energies

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Multi-physical field sensing and machine learning have drawn great attention in various fields such as sensor networks, robotics, energy devices, smart buildings, intelligent system and so on. In this paper, we present a novel efficient method for thermal and energy management based on bimodal airflow-temperature sensing and reinforcement learning, which expedites an exploration process by self-learning and adjusts action policy only through actuators interacting with the environment, being free of the controlled object model and priori experiences. In general, training of reinforcement learning requires a large amount of data iterations, which takes a long time and is not suitable for real-time control. Here, we propose an approach to speed up the learning process by indicating the action adjustment direction. We adopt tailor-designed bimodal sensors to simultaneously detect airflow and temperature field, which provides comprehensive information for reinforcement learning. The proposed thermal and energy management incorporates bimodal parametric sensing with an improved actor-critic algorithm to realize self-learning control. Experiments of thermal and energy management in a multi-module integrated system validate the effectiveness of the proposed methodology, which demonstrate high efficiency, fast response, and good robustness in various control scenarios. The proposed methodology can be widely applied to thermal and energy management of diverse integrated systems.

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Effects of heat stress on piglet production/performance parameters

Guo

Liu

2018

Trop Anim Health Prod

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Heat stress is problematic for pigs raised in tropical environments. The animal is large and has proportionally high body heat, and subcutaneous fat thickness reduces the animal's ability to thermoregulate. It is not clear how stress affects pig litter size. For this reason, we performed a meta-analysis of the effect of heat stress on pig litter size. We reviewed behavioral, endocrine, and cellular data and the signaling pathways involved in heat stress. We found that heat stress did not affect litter size or the body weight of offspring. However, heat stress was found to decrease litter body weight gain due to reductions in sow milk production. For this reason, artificial feeding of piglets may be appropriate.

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PID temperature controller in pig nursery: spatial characterization of thermal environment

Cited by 9 publications

References 22 publications

Self-Tuning Neural Network PID With Dynamic Response Control

Self-Tuning Neural Network PID With Dynamic Response Control

Thermal and Energy Management Based on Bimodal Airflow-Temperature Sensing and Reinforcement Learning

Effects of heat stress on piglet production/performance parameters

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