针对船舶主动力装置中机炉协调系统中存在的多变量强耦合、非线性及大延迟等特点,提出了基于SVM逆辨识的复合控制策略,并应用于协调控制系统的仿真研究.该方法将直接逆模控制与模糊控制结合,既利用了支持向量机逆控制设计简单,稳态精度高的优点,同时结合模糊控制响应快速,抗干扰能力强,鲁棒性好的优势,使得支持向量机模型即使没有经过足够充分的训练,也能保证控制器从一个快速和稳定的初始状态开始控制.仿真结果表明,该SVM复合控制算法在控制品质上有很大的提高.在动态响应速度、静态稳定性、抑制大超调以及鲁棒性等方面都表现出了极好的性能.%Multivariables, strong coupling, nonlinearity, and large delays characterize the boiler-turbine coordinated control systems for ship power equipment. To better deal with these conditions, a compound control strategy based on a support vector machine (SVM) with inverse identification was proposed and applied to research simulating coordinated control systems. This method combines SVM inverse control and fuzzy control, taking advantage of the merits of SVM inverse controls which can be designed easily and have high reliability, and those of fuzzy controls, which respond rapidly and have good anti-jamming capability and robustness. It ensures the controller can be controlled with near instantaneous adjustments to maintain a steady state, even if the SVM is not trained well. The simulation results show that the control quality of this fuzzy-SVM compound control algorithm is high, with good performance in dynamic response speed, static stability, restraint of overshoot, and robustness.
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机译：针对船舶主动力装置中机炉协调系统中存在的多变量强耦合、非线性及大延迟等特点,提出了基于SVM逆辨识的复合控制策略,并应用于协调控制系统的仿真研究.该方法将直接逆模控制与模糊控制结合,既利用了支持向量机逆控制设计简单,稳态精度高的优点,同时结合模糊控制响应快速,抗干扰能力强,鲁棒性好的优势,使得支持向量机模型即使没有经过足够充分的训练,也能保证控制器从一个快速和稳定的初始状态开始控制.仿真结果表明,该SVM复合控制算法在控制品质上有很大的提高.在动态响应速度、静态稳定性、抑制大超调以及鲁棒性等方面都表现出了极好的性能.%Multivariables, strong coupling, nonlinearity, and large delays characterize the boiler-turbine coordinated control systems for ship power equipment. To better deal with these conditions, a compound control strategy based on a support vector machine (SVM) with inverse identification was proposed and applied to research simulating coordinated control systems. This method combines SVM inverse control and fuzzy control, taking advantage of the merits of SVM inverse controls which can be designed easily and have high reliability, and those of fuzzy controls, which respond rapidly and have good anti-jamming capability and robustness. It ensures the controller can be controlled with near instantaneous adjustments to maintain a steady state, even if the SVM is not trained well. The simulation results show that the control quality of this fuzzy-SVM compound control algorithm is high, with good performance in dynamic response speed, static stability, restraint of overshoot, and robustness.
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