复杂给矿过程信号补偿法驱动的智能PI控制OA
Intelligent PI control driven by signal compensation method for complex ore feeding process
复杂给矿过程采用常规PI控制难以保证系统稳定运行.本文首先根据给矿过程的机理特征建立由低阶线性模型和未知高阶非线性动态系统组成的混合动态模型.其次,提出了由信号补偿PI控制、规则推理、切换机制组成的智能PI控制方法,该方法利用一步最优控制律设计信号补偿器,削弱了非线性扰动和传输时延对给矿过程的影响,并结合规则推理改善了闭环控制效果.针对下矿时给矿机的切换问题,设计了智能切换机制,保证了给矿机的合理调配.最后,将所提方法应用于复杂给矿过程.实际应用效果表明:所提方法可将给矿量和给矿频率控制在工艺要求的目标值范围内.
Conventional PI control is difficult to ensure stable operation for the complex ore feeding process.Firstly,this article analyzes the complexity mechanism characteristics of the ore feeding process,and establishes a hybrid dynamic model consisting of a low order linear model and an unknown high-order nonlinear dynamic system.Secondly,an intelligent PI control method is proposed,which is composed of signal compensation method,rule inference control and switching mechanism.This method utilizes a one-step optimal control law to design a signal compensator,which weakens the influence of nonlinear disturbances and transmission delay on the ore feeding process,and combines rule inference to improve the closed-loop control effect.Regarding switching problem of the ore feeding machines,an intelligent switching mechanism has been designed to ensure the reasonable allocation of five ore feeding machines.Finally,the proposed method is applied to complex ore feeding process,the actual application results show that the proposed method can control the ore feeding amount and frequency within the target value range required by the process.
郭策;张亚军;贾瑶;郑锐;柴天佑
东北大学流程工业综合自动化国家重点实验室,辽宁沈阳 110819东北大学流程工业综合自动化国家重点实验室,辽宁沈阳 110819||国家冶金自动化工程技术研究中心,辽宁沈阳 110819东北大学流程工业综合自动化国家重点实验室,辽宁沈阳 110819||国家冶金自动化工程技术研究中心,辽宁沈阳 110819东北大学流程工业综合自动化国家重点实验室,辽宁沈阳 110819东北大学流程工业综合自动化国家重点实验室,辽宁沈阳 110819||国家冶金自动化工程技术研究中心,辽宁沈阳 110819
给矿过程混合动态模型信号补偿切换机制智能PI控制
ore feeding processhybrid dynamic modelsignal compensationswitching mechanismintelligent PI control
《控制理论与应用》 2026 (2)
287-295,9
国家自然科学基金项目(61991400,61991402,62173170),国家自然科学基金辽宁联合基金项目(U24A20275),中央高校基本科研业务费专项资金项目(N25YJS002)资助.Supported by the National Natural Science Foundation of China(61991400,61991402,62173170),the National Natural Science Foundation of China-Liaoning Joint Fund(U24A20275)and the Fundamental Research Funds for the Central Universities(N25YJS002).
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