以某三轴燃气轮机作为研究对象，针对燃气轮机各工况下转速失稳、不同海况下的动力响应需求、外部干扰等因素对燃气轮机工作状态的影响问题，设计了基于Q-learning强化学习理论的控制策略，以智能算法在线调试代替人工手动调试过程，实现人工智能理论到工程实际的应用在进行软件层智能算法验证后，通过强化学习算法控制参数的自动优化完成了基于强化学习的转速控制策略硬件在环试验。研究表明：该设计算法可以在燃气轮机运行过程中判断触发、自我训练、自我调整控制参数，保证燃气轮机在各种突发情况下的运行稳定；在三轴燃气轮机转速失稳时，在较短时间内即可完成转速的回稳，并可将转速误差控制在2 r/min内，从而实现了转速失稳时的自救。

This paper took a threeshaft gas turbine as the research object,under various operating conditions for gas turbine speed instability problem,dynamic response requirements of different sea conditions and the influence of external disturbance factors on the working state of gas turbine,designed the control strategy of the reinforcement learning theory based on the Qlearning,and realized the application of artificial intelligence theory to engineering practice by using the intelligent algorithm online debugging to replace the manual debugging process.After verifying the intelligent algorithm of software layer,through the automatic optimization of the control parameters of the reinforcement learning algorithm,the hardwareintheloop test of speed control strategy based on reinforcement learning was completed.The research indicates that the algorithm designed in this paper can judge the trigger,train itself,and selfadjust the control parameters in the gas turbine operation process to ensure the stable operation of the gas turbine in various emergencies.When the threeshaft gas turbine speed is unstable,the speed stabilization can be completed in a short time,and the speed error can be controlled within 2 r/min,so as to realize the selfrescue when the speed is unstable.

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国家科技重大专项（2017-V-0005-0055，2017-V-0015-0067)