Torque Control of DC Torque Motor Based on Expert PID

Xu, Xinyu; Li, Dazhai

doi:10.1088/1742-6596/1626/1/012073

Cited by 3 publications

(2 citation statements)

References 2 publications

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“…The Expert PID control method combines control experience patterns from an expert knowledge base, deriving the parameters of the PID controller through logical reasoning mechanisms. However, it heavily relies on the expert's experience, and the proficiency of the expert determines the effectiveness of the controller (Hu and Liu, 2020;Xu and Li, 2020). The Fuzzy PID control method condenses empirical knowledge into a fuzzy rule model, achieving self-tuning of PID parameters through fuzzy reasoning.…”

Section: Introductionmentioning

confidence: 99%

Water level control of nuclear steam generators using intelligent hierarchical autonomous controller

Peng,

Ma,

Xia

2024

Front. Energy Res.

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The challenge of water level control in steam generators, particularly at low power levels, has always been a critical aspect of nuclear power plant operation. To address this issue, this paper introduces an IHA controller. This controller employs a CPI controller as the primary controller for direct water level control, coupled with an agent-based controller optimized through a DRL algorithm. The agent dynamically optimizes the parameters of the CPI controller in real-time based on the system’s state, resulting in improved control performance. Firstly, a new observer information is obtained to get the accurate state of the system, and a new reward function is constructed to evaluate the status of the system and guide the agent’s learning process. Secondly, a deep ResNet with good generalization performance is used as the approximator of action value function and policy function. Then, the DDPG algorithm is used to train the agent-based controller, and an advanced controller with good performance is obtained after training. Finally, the popular UTSG model is used to verify the effectiveness of the algorithm. The results demonstrate that the proposed method achieves rise times of 73.9 s, 13.6 s, and 16.4 s at low, medium, and high power levels, respectively. Particularly, at low power levels, the IHA controller can restore the water level to its normal state within 200 s. These performances surpass those of the comparative methods, indicating that the proposed method excels not only in water level tracking but also in anti-interference capabilities. In essence, the IHA controller can autonomously learn the control strategy and reduce its reliance on the expert system, achieving true autonomous control and delivering excellent control performance.

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Section: Introductionmentioning

confidence: 99%

Water level control of nuclear steam generators using intelligent hierarchical autonomous controller

Peng,

Ma,

Xia

2024

Front. Energy Res.

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“…This technology is unquestionably helpful in industrial settings because it avoids any mechanical impact on the actual installation. It is necessary to establish specific dc motor settings and characteristics before using indirect procedures [9], [10]. The accuracy and convenience with which the motor properties and parameters may be acquired generally define the success of this topology utilizing the indirect method [11], [12].…”

Section: Introductionmentioning

confidence: 99%

Torque control of a 5 KW, 220 V separately excited DC motor using microcomputer

Ahmed

El‐Emary

Alaas

et al. 2023

IJPEDS

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Fast torque control is crucial for various applications, including diesel-electric locomotives, electric automobiles, and steel plants. The purpose of this paper is to utilize a microcomputer to control the torque of a separately-excited DC motor fed from a single-phase semi-controlled converter. There are numerous strategies to determine torque using direct or indirect methods. The greatest issue of direct strategy using torque meters is that the measuring device must be installed between the motor and the load. In this paper, we avoid this challenge by employing the indirect approach, specifically by monitoring several DC machine-specific quantities such as voltage, current, and speed, which are easier to determine experimentally. This technology is unquestionably helpful in real-world applications since it eliminates any mechanical impact on the installation. This paper describes the system configuration and provides an explanation of the architecture and system features. The simulation of the proposed system using TUSTSIM dynamic simulation program which is capable of simulating the control system with a digital controller in the loop is presented. An implementation of microcomputer-assisted torque control of DC separately excited motor with proportional integral (PI) controllers is presented. A typical oscillography of the driving characteristics is provided along with the experimental results.

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Adaptive Neural Network Control of Four-Switch Buck–Boost Converters

Ren,

Wang,

Zhang

2024

Actuators

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Based on the adaptive control structure of neural networks, this paper proposes a novel output voltage control strategy for DC converters. The strategy regulates the inductor current to maintain a constant voltage by adjusting the duty cycle of four-switch buck—boost (FSBB) converters. A nonlinear average model for the FSBB converter, derived from its energy consumption, is introduced, and its effectiveness is demonstrated through simulations. The simulations confirm that the FSBB converter enables zero-voltage switching (ZVS) of the four switches across the entire operating voltage range. The comparative simulation results show that the proposed control strategy achieves faster voltage regulation while ensuring ZVS, leading to improved converter performance across the full power range.

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Torque Control of DC Torque Motor Based on Expert PID

Cited by 3 publications

References 2 publications

Water level control of nuclear steam generators using intelligent hierarchical autonomous controller

Water level control of nuclear steam generators using intelligent hierarchical autonomous controller

Torque control of a 5 KW, 220 V separately excited DC motor using microcomputer

Adaptive Neural Network Control of Four-Switch Buck–Boost Converters

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