Paper
On-line PID tuning for engine idle-speed control using continuous action reinforcement learning automata
Published Feb 1, 2000 · Mark N Howell, M. Best
Control Engineering Practice
Q1 SJR score
109
Citations
1
Influential Citations
Abstract
Abstract hidden due to publisher request; this does not indicate any issues with the research. Click the full text link above to read the abstract and view the original source.
Study Snapshot
Continuous action reinforcement learning automata (CARLA) significantly improves engine idle-speed control performance compared to standard tuning methods.
PopulationOlder adults (50-71 years)
Sample size24
MethodsObservational
OutcomesBody Mass Index projections
ResultsSocial networks mitigate obesity in older groups.
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References
Application of l1 optimal control to the engine idle speed control problem
The l/sub 1/ optimal control methodology effectively reduces engine idle speed errors, but may require extra control energy during transients.
1999·71citations·K. Butts et al.·IEEE Trans. Control. Syst. Technol.
IEEE Trans. Control. Syst. Technol.
Continuous learning automata and adaptive digital filter design
Continuous action reinforcement learning automata show global convergence in adaptive digital filter design, overcoming multi-modal error surfaces and enabling multi-modal optimization of filter coefficients.
1998·9citations·Mark N Howell et al.
Continuous action reinforcement learning applied to vehicle suspension control
The new reinforcement learning algorithm effectively minimizes mean square acceleration in vehicle suspension control, improving ride isolation qualities in a parallel computing environment.
1997·97citations·Mark N Howell et al.·Mechatronics
Mechatronics
MODELS AND CONTROL METHODOLOGIES FOR IC ENGINE IDLE SPEED CONTROL DESIGN
This paper surveys various internal combustion engine models and control design methodologies for idle speed control, examining both classical and advanced control theories.
1996·200citations·D. Hrovat et al.·Control Engineering Practice
Control Engineering Practice
Citations
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2024·0citations·Ning Yang et al.·Journal of Polymer Science and Engineering
Journal of Polymer Science and Engineering
Deep reinforcement learning implementation on IC engine idle speed control
Reinforcement Learning (RL) using the Deep Q-Network algorithm outperforms PID control in reducing idle speed fluctuations and improving engine performance in automotive engines.
2024·2citations·Ibrahim Omran et al.·Ain Shams Engineering Journal
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2023·0citations·Di Luo et al.·2023 6th International Conference on Robotics, Control and Automation Engineering (RCAE)
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Dynamic simulation based on feature transfer learning with source domain adaptive optimization: Application of data-driven model for aero-engines
The Feature Similarity-based Transfer Learning (FSTL) based on Deep Deterministic Policy Gradients (DDPG) framework improves aero-engine dynamic simulation performance and matches physical laws compared to other methods.
2023·7citations·Xingyun Jia et al.·Measurement
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On-line automatic controller tuning of a multivariable grinding mill circuit using Bayesian optimisation
Automatic controller tuning using Bayesian optimisation improves the performance of multi-input multi-output (MIMO) controllers in ore milling circuits.
2023·5citations·J. V. van Niekerk et al.·Journal of Process Control
Journal of Process Control