基于数据与虚拟未建模动态驱动的非线性自适应切换控制

The project concentrates on the key technological issues in industry and control theory, a nonlinear adaptive switching control based on data and virtual unmodeled dynamics driven is studied for a class of complex industrial system, which with uncertainty and nonlinear nature generally make it very difficult, if not impossible, to obtain an accurate model, especially when the model structure is unclear. The data-driven control method and model-based control method are integrated, while the basic research and application research are combined. First, the virtual unmodeled dynamic estimation algorithm is studied, in particular, how to improve the estimation accuracy and convergence of the algorithm is considered at the same time to reduce the complexity of estimation algorithm. Secondly, based on the novel virtual dynamic estimation algorithm, a nonlinear compensator is performed. With the above development, the nonlinear adaptive switching control method and the stability and convergence analysis of the closed-loop switched system are established. Finally, through the simulation based comparative study and the experiment of the proposed control on a tank level adaptive control system, the effectiveness of the proposed method is justified. The results proposed by this project can further develop and improve nonlinear adaptive control theory, it has important significance for both the theory and practical application. The project also provides the new control approaches and theoretic basis for dealing with some unclear structure and comprehensive complex industrial processes.

本项目围绕工业界和控制理论专家提出的亟待解决的关键科技难题，全面考虑工业过程的强非线性、不确定性、机理不清以及难以用精确数学模型描述的复杂工业装置，将数据驱动控制方法与基于模型的控制方法相集成，基础研究与应用研究相结合，开展基于数据和虚拟未建模动态估计与补偿的非线性自适应切换控制方法的研究。首先，研究虚拟未建模动态估计算法，提高估计算法的精度和收敛性、同时降低算法复杂性的科学问题；其次，基于新的虚拟未建模动态估计算法，研究非线性补偿器的设计方法；在此基础上，研究非线性自适应切换控制方法以及闭环系统的稳定性和收敛性分析方法等问题，获得高水平的理论研究成果；最后，以双容水箱液位系统为实例，进行仿真和应用验证研究。本项目的研究无论从理论还是实际应用方面都对促进和发展非线性系统的自适应控制有着重要的理论意义和科学价值，对实现一类结构未知并具有综合复杂性的工业过程的控制提供新的方法和理论依据。

该项目针对工业过程中存在的一类具有强非线性、不确定性、机理不清并难以用精确数学模型描述的复杂工业装置，将数据驱动控制方法与基于模型的控制方法相集成，开展了基于数据和虚拟未建模动态估计与补偿的非线性自适应切换控制方法的研究。首先，研究了虚拟未建模动态估计算法，提高了估计算法的精度和收敛性、同时降低了算法的复杂性；其次，基于新的虚拟未建模动态估计算法，研究非线性补偿器的设计方法；在此基础上，研究了非线性自适应切换控制方法以及闭环系统的稳定性和收敛性分析方法等问题。以双容水箱液位系统、电熔镁炉、氢氧化镍钴矿浆中和过程为实例，进行仿真和应用验证研究。. 本项目的研究成果在《IEEE Transactions on neural network and Learning systems》、《IEEE Transact ions on Control Systems Technology》、《IEEE Transact ions on Systems, Man, and Cybernetics: Systems》、《Powder Technology》、《Neurocomputing》、《自动化学报》等国际与国内期刊上共发表学术论文6篇，其中IEEE Trans 系列SCI期刊regular paper：4篇，发表《自动化学报》长论文3篇；在国际和国内控制领域的顶级会议上发表论文4篇；参与编写《自适应控制》专著一部，参与申请专利1项。