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intelligent optimal control system for ball mill grinding process

控制理论与应用（英文版） 2013年第3期11卷 454-462页

作者： Dayong ZHAO Tianyou CHAI State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Research Center of Automation Northeastern University

Operation aim of ball mill grinding process is to control grinding particle size and circulation load to ball mill into their objective limits respectively, while guaranteeing producing safely and stably. The grinding process is essentially a multi-input multi-output system （MIMO） with large inertia, strong coupling and uncertainty characteristics. Furthermore, being unable to monitor the particle size online in most of concentrator plants, it is difficult to realize the optimal control by adopting traditional control methods based on mathematical models. In this paper, an intelligent optimal control method with two-layer hierarchical construction is presented. Based on fuzzy and rule-based reasoning （RBR） algorithms, the intelligent optimal setting layer generates the loops setpoints of the basic control layer, and the latter can track their setpoints with decentralized PID algorithms. With the distributed control system （DCS） platform, the proposed control method has been built and implemented in a concentration plant in Gansu province, China. The industrial application indicates the validation and effectiveness of the proposed method.

关键词： intelligent optimal control Fuzzy control Rule-based reasoning Grinding process Particle size

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Integrated Automation System for Rare Earth Countercurrent Extraction Process

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Journal of Rare Earths 2004年第6期22卷 752-758页

作者：柴天佑杨辉 Research Center of Automation Northeastern University Shenyang 110004 China School of Electronics and Electrical Engineering East China Jiaotong University Nanchang 330013 China

Lower automation level in industrial rare-earth extraction processes results in high production cost, inconsistent product quality and great consumption of resources in China. An integrated automation system for extraction process of rare earth is proposed to realize optimal product indices, such as product purity,recycle rate and output. The optimal control strategy for output component, structure and function of the two-gradcd integrated automation system composed of the process management grade and the process control grade were discussed. This system is successfully applied to a HAB yttrium extraction production process and was found to provide optimal control, optimal operation, optimal management and remarkable benefits.

关键词： countercurrent extraction intelligent optimal control integrated automation system rare earths

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维普期刊数据库

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An identifier-actor-optimizer policy learning architecture for optimal control of continuous-time nonlinear systems

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Science China(Physics,Mechanics & Astronomy) 2020年第6期63卷 42-53页

作者： Lin Cheng ZhenBo Wang FangHua Jiang JunFeng Li School of Aerospace Engineering Tsinghua UniversityBeijing 100084China Department of Mechanical Aerospaceand Biomedical EngineeringUniversity of TennesseeKnoxville TN 37996USA

An intelligent solution method is proposed to achieve real-time optimal control for continuous-time nonlinear systems using a novel identifier-actor-optimizer(IAO)policy learning *** this IAO-based policy learning approach,a dynamical identifier is developed to approximate the unknown part of system dynamics using deep neural networks(DNNs).Then,an indirect-method-based optimizer is proposed to generate high-quality optimal actions for system control considering both the constraints and performance ***,a DNN-based actor is developed to approximate the obtained optimal actions and return good initial guesses to the *** this way,the traditional optimal control methods and state-of-the-art DNN techniques are combined in the IAO-based optimal policy learning *** to the reinforcement learning algorithms with actor-critic architectures that suffer hard reward design and low computational efficiency,the IAO-based optimal policy learning algorithm enjoys fewer user-defined parameters,higher learning speeds,and steadier convergence properties in solving complex continuous-time optimal control problems(OCPs).Simulation results of three space flight control missions are given to substantiate the effectiveness of this IAO-based policy learning strategy and to illustrate the performance of the developed DNN-based optimal control method for continuous-time OCPs.

关键词： continous-time nonlinear systems identifier-actor-optimizer architecture intelligent optimal control deep neural net-works

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