A Smooth Angle Velocity Active Return-to-Centre Control Based on Single Neuron PID Control for Electric Power Steering System

Wu, Yangfei; Hou, Bochuan; Zhou, Guangfei; Yang, Jianhua; Jun, Fang; Zhang, Yuming

doi:10.1109/ccdc49329.2020.9164873

Cited by 4 publications

(2 citation statements)

References 4 publications

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“…. The neuron employs a supervised Hebbian learning rule for its learning process, a rule formulated by combining the supervised Δ (Delta) learning rule with the Hebbian learning rule [47]. According to this learning mechanism, the adjustment formulas for the three input weights of the neuron, kp w , ki w , kd w , can be derived.…”

Section: Design Of Snpid Controllermentioning

confidence: 99%

Intelligent Regulation of Temperature and Humidity in Vegetable Greenhouses Based on Single Neuron PID Algorithm

Huang,

Xiang,

Leng

et al. 2024

Electronics

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In order to meet the demands of autonomy and control optimization in solar greenhouse control systems, this paper developed an intelligent temperature and humidity control system for greenhouses based on the Single Neuron Proportional Integral Derivative (SNPID) algorithm. The system is centered around the Huada HC32F460 Micro-Controller Unit (MCU) and the RT-Thread operating system, integrated with the SNPID control algorithm. Through comprehensive simulation, model construction, and comparative experiments, this system was thoroughly evaluated in comparison with traditional PID control systems (cPID) that rely on overseas software and hardwsbuare. Simulation results show that our new system significantly outperforms traditional PID (Proportional Integral Derivative) systems in terms of temperature control stability and accuracy. Experimental data further confirm that, while ensuring cost-effectiveness, the new system achieves a remarkable 50.2% improvement in temperature and humidity control precision compared to traditional systems. The temperature Root Mean Square Error (RMSE) in the experimental greenhouse is 0.734 compared to 1.594 in the comparison greenhouse, indicating better stable temperature control capability. The vents in the experimental greenhouse have a maximum opening of 67 cm and a minimum of 5 cm, showing a quick response property to high temperatures. In contrast, the control greenhouse has a maximum vent opening of 55 cm, remaining unchanged during the test period, which reflects its slower response to temperature fluctuations. These results demonstrate the significant advantages of the designed solar greenhouse temperature and humidity control system in terms of autonomy and control optimization, providing an efficient and economical solution for solar greenhouse environmental management. This system shows significant practical application perspective in promoting intelligent agriculture and sustainable agricultural production, highlighting its broad impact and potential significance.

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Section: Design Of Snpid Controllermentioning

confidence: 99%

Intelligent Regulation of Temperature and Humidity in Vegetable Greenhouses Based on Single Neuron PID Algorithm

Huang,

Xiang,

Leng

et al. 2024

Electronics

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“…Based on the actual greenhouse conditions as well as the reading of the above articles, this paper establishes a relatively realistic model of heat change in a solar greenhouse based on the heat balance theory of thermodynamics and obtains the change of temperature inside the greenhouse according to the heat change. Thereafter, to obtain a better control effect than a classical PID controller, a single neuron PID controller (SNPID controller) is designed based on the theory of PID algorithm and single neuron learning [13], with the output designed according to the actual control situation. The SNPID controller is lightweight enough to operate in a low-power-consumption platform (such as the embedded platform used in the greenhouse mentioned in this paper), and the single neuron introduced into the controller brings the self-tuning feature, which is proven effective in several fields like textile industry [14], robot [15] and so on, but it's rarely deployed in the temperature control of solar greenhouse, so this paper also simulated a classical PID controller (cPID controller) as a comparison for checking if SNPID controller could bring an improvement.…”

Section: Introductionmentioning

confidence: 99%

Research on thermodynamic simulation modeling and air temperature control of solar greenhouse

Lian

Yu²,

Gao³

et al. 2022

International Workshop on Automation, Control, and Communication Engineering (IWACCE 2022)

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The solar greenhouse is an infrastructure widely used in agricultural production, and the precise control of its temperature can stabilize yield and production quality. To improve the air temperature control performance without affecting the actual production, a method for simulation modeling of the greenhouse and its air temperature control was proposed based on the thermodynamic principle. Firstly, by combining the following main influencing factors: the heat gained from solar radiation, the heat transferred via ventilation, conduction, long-wave radiation, and the heat storaged by wall, a mathematical model of the greenhouse was constructed. Secondly, a classical proportion-integrationdifferentiation (cPID) controller was deployed for the air temperature control of the greenhouse based on ventilation. Then an enhancement for cPID, a single neuron proportion-integration-differentiation (SNPID) controller was proposed to improve the control effect, and the former cPID controller acted as a comparison. Next, a MATLAB/Simulink simulation was carried out based on the solar greenhouse model and the PID controller models. As the experimental results show, with sunlight input and Gaussian noise interference in the controlled greenhouse model, the maximum positive and negative temperature offset of SNPID are both smaller than the cPID results, and after interference at the simulated time t=3.33 h, the adjustment time of SNPID is shortened by at least 45.0% compared with cPID. The temperature curve under SNPID control is closer to the target and more stable than the curve of cPID. Thus, the accuracy and robustness of control in this greenhouse are improved.

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Fuzzy PID Combined with Smith Predictor Control of GSHP Terminal Fan System

Chen¹,

Fei²,

Zheng³

et al. 2020

2020 7th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS)

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A Smooth Angle Velocity Active Return-to-Centre Control Based on Single Neuron PID Control for Electric Power Steering System

Cited by 4 publications

References 4 publications

Intelligent Regulation of Temperature and Humidity in Vegetable Greenhouses Based on Single Neuron PID Algorithm

Intelligent Regulation of Temperature and Humidity in Vegetable Greenhouses Based on Single Neuron PID Algorithm

Research on thermodynamic simulation modeling and air temperature control of solar greenhouse

Fuzzy PID Combined with Smith Predictor Control of GSHP Terminal Fan System

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