機器學習控制：修订间差异
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機器學習控制（Machine learning control、MLC）是机器学习、智能控制及控制理论中的一部份，是由机器学习的方式來求解最优控制問題。主要的應用是一些不適用控制系統方法的複雜非線性系統。
問題和任務的分類
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Four types of problems are commonly encountered.
- Control parameter identification: MLC translates to a parameter identification[1] if the structure of the control law is given but the parameters are unknown. One example is the 遗传算法 for optimizing coefficients of a PID控制器[2] or discrete-time optimal control.[3]
- Control design as regression problem of the first kind: MLC approximates a general nonlinear mapping from sensor signals to actuation commands, if the sensor signals and the optimal actuation command are known for every state. One example is the computation of sensor feedback from a known 全狀態回授. A 神經網路 is commonly used technique for this task.[4]
- Control design as regression problem of the second kind: MLC may also identify arbitrary nonlinear control laws which minimize the cost function of the plant. In this case, neither a model, nor the control law structure, nor the optimizing actuation command needs to be known. The optimization is only based on the control performance (cost function) as measured in the plant. 遗传编程 is a powerful regression technique for this purpose.[5]
- Reinforcement learning control: The control law may be continually updated over measured performance changes (rewards) using 强化学习.[6]
MLC comprises, for instance, neural network control, genetic algorithm based control, genetic programming control, reinforcement learning control, and has methodological overlaps with other data-driven control, like 人工智能 and 機械人控制.
應用
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MLC has been successfully applied to many nonlinear control problems, exploring unknown and often unexpected actuation mechanisms. Example applications include
- Attitude control of satellites.[7]
- Building thermal control.[8]
- Feedback turbulence control.[2][9]
- Remotely operated under water vehicle.[10]
- Many more engineering MLC application are summarized in the review article of PJ Fleming & RC Purshouse (2002).[11]
As for all general nonlinear methods, MLC comes with no guaranteed convergence, optimality or robustness for a range of operating conditions.
參考資料
- ^ Thomas Bäck & Hans-Paul Schwefel (Spring 1993) "An overview of evolutionary algorithms for parameter optimization", Journal of Evolutionary Computation (MIT Press), vol. 1, no. 1, pp. 1-23
- ^ 2.0 2.1 N. Benard, J. Pons-Prats, J. Periaux, G. Bugeda, J.-P. Bonnet & E. Moreau, (2015) "Multi-Input Genetic Algorithm for Experimental Optimization of the Reattachment Downstream of a Backward-Facing Step with Surface Plasma Actuator", Paper AIAA 2015-2957 at 46th AIAA Plasmadynamics and Lasers Conference, Dallas, TX, USA, pp. 1-23.
- ^ Zbigniew Michalewicz, Cezary Z. Janikow & Jacek B. Krawczyk (July 1992) "A modified genetic algorithm for optimal control problems", [Computers & Mathematics with Applications], vol. 23, no 12, pp. 83-94.
- ^ C. Lee, J. Kim, D. Babcock & R. Goodman (1997) "Application of neural networks to turbulence control for drag reduction", Physics of Fluids, vol. 6, no. 9, pp. 1740-1747
- ^ D. C. Dracopoulos & S. Kent (December 1997) "Genetic programming for prediction and control", Neural Computing & Applications (Springer), vol. 6, no. 4, pp. 214-228.
- ^ Andrew G. Barto (December 1994) "Reinforcement learning control", Current Opinion in Neurobiology, vol. 6, no. 4, pp. 888–893
- ^ Dimitris. C. Dracopoulos & Antonia. J. Jones (1994) Neuro-genetic adaptive attitude control, Neural Computing & Applications (Springer), vol. 2, no. 4, pp. 183-204.
- ^ Jonathan A. Wright, Heather A. Loosemore & Raziyeh Farmani (2002) "Optimization of building thermal design and control by multi-criterion genetic algorithm, [Energy and Buildings], vol. 34, no. 9, pp. 959-972.
- ^ Steven J. Brunton & Bernd R. Noack (2015) Closed-loop turbulence control: Progress and challenges, Applied Mechanics Reviews, vol. 67, no. 5, article 050801, pp. 1-48.
- ^ J. Javadi-Moghaddam, & A. Bagheri (2010 "An adaptive neuro-fuzzy sliding mode based genetic algorithm control system for under water remotely operated vehicle", Expert Systems with Applications, vol. 37 no. 1, pp. 647-660.
- ^ Peter J. Fleming, R. C. Purshouse (2002 "Evolutionary algorithms in control systems engineering: a survey" Control Engineering Practice, vol. 10, no. 11, pp. 1223-1241
延伸閱讀
- Dimitris C Dracopoulos (August 1997) "Evolutionary Learning Algorithms for Neural Adaptive Control", Springer. ISBN 978-3-540-76161-7.
- Thomas Duriez, Steven L. Brunton & Bernd R. Noack (November 2016) "Machine Learning Control - Taming Nonlinear Dynamics and Turbulence", Springer. ISBN 978-3-319-40624-4.