Compensator-Based Self-Learning: Optimal Operational Control for Two-Time-Scale Systems With Input Constraints

Li, Jinna; Yang, Mingwei; Lewis, Frank L.; Zheng, Meng

doi:10.1109/tii.2024.3384621

IEEE Trans. Ind. Inf.

2024

DOI: 10.1109/tii.2024.3384621

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Compensator-Based Self-Learning: Optimal Operational Control for Two-Time-Scale Systems With Input Constraints

Jinna Li,

Mingwei Yang,

Frank L. Lewis

et al.

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Cited by 3 publications

References 27 publications

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An efficient three‐phase two‐level voltage source inverter with the suppression of the dead time effect

Wang,

Gong,

Wang

2024

Circuit Theory & Apps

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The paper carries out the research on a novel two‐level voltage source inverter to further improve the output power quality of the inverter under the premise of efficient operation. Main switches can realize zero‐voltage switching in a wide load range to ensure the efficient operation of the designed inverter. Total harmonic distortion (THD) of the output current at low output frequencies can be improved by the modification of resonant tanks in the designed inverter. In the dead time, the modified resonant tanks can result in a shorter duration of nonlinear changes in the output phase voltage at lower load current, which is beneficial for reducing the output voltage error caused by dead time. The improved power quality at low output frequencies is conductive to the use of the designed inverter in the drive system of the low‐speed AC motor. The paper expounds every operating status during a switching period. The experiment manifests that switches realize soft switching. The efficiency of the designed inverter reaches 98.6% at rated operation state, which is 0.3% and 0.5% more than that of two comparison objects, respectively. Moreover, when the output frequency reduces to 5 Hz, the THD of the output current is only 2%, which is also less than that of comparison objects. Hence, the designed inverter has advantages in the efficiency and the output power quality.

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An efficient three‐phase two‐level voltage source inverter with the suppression of the dead time effect

Wang,

Gong,

Wang

2024

Circuit Theory & Apps

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An advanced robust integral reinforcement learning scheme with the fuzzy inference system

Liu,

Wang,

Qiao

2024

Intl J Robust & Nonlinear

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In this paper, the model‐free robust control problem is investigated for nonlinear systems with a relaxed condition of initial admissible control. An advanced integral reinforcement learning method is developed, which merges the adaptive network‐based fuzzy inference system (ANFIS) and pre‐training of the initial weights. To loose the condition for choosing the initial control law, pre‐training of initial weights is established by utilizing the ANFIS to provide the information corresponding to the system model, which is applicable to the model‐free issue. Based on the actor‐critic structure, the approximate optimal control law is obtained by employing adaptive dynamic programming. Redesigning the obtained control law, the robust controller can be derived to stabilize the system with the uncertain term. Eventually, two examples are utilized to verify the effectiveness of the constructed algorithm.

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Self-organizing neural intelligent control for nonlinear discrete-time systems with particle swarm optimization

Ma,

Wang,

Ren

et al. 2024

Nonlinear Dyn

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Compensator-Based Self-Learning: Optimal Operational Control for Two-Time-Scale Systems With Input Constraints

Cited by 3 publications

References 27 publications

An efficient three‐phase two‐level voltage source inverter with the suppression of the dead time effect

An efficient three‐phase two‐level voltage source inverter with the suppression of the dead time effect

An advanced robust integral reinforcement learning scheme with the fuzzy inference system

Self-organizing neural intelligent control for nonlinear discrete-time systems with particle swarm optimization

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