针对系统工作点连续可变、具有强非线性且其解析模型难以获取的有约束复杂工业过程，从为预测控制建模的理念出发，构建可准确描述该类对象动态特性的、依存于系统状态的一般化状态相依ARX模型框架。此框架下的模型可通过系统采样数据辨识得到，适用于鲁棒预测控制算法的设计。研究开发可保证该类辨识模型具有与实际系统一致的稳定性及动态特性模式、且具有良好的对象局部特征描述能力及长期预测能力的模型参数估计与评价方法。针对该类有约束系统，研究开发基于一般化状态相依ARX模型的、能克服建模误差和不确定性干扰影响的鲁棒稳定、可在线快速实现、控制性能优良的预测控制算法及其实现方法。这种为预测控制建模，把模型特点充分融合到预测控制算法设计中的研究实践，将有助于推动非线性时间序列建模方法和预测控制方法相关研究的相互融合与相互促进，将为该类复杂非线性工业过程的建模与优化控制问题提供一种系统性的、有效的、可实现的综合解决方案。

For constrained systems with continuously varying working-point, strongly nonlinear characteristics and whose first-principles models are hard to be obtained, from point of view of predictive control-oriented modeling, a generalized system state-dependent ARX (SD-ARX) model framework is built in this program. The SD-ARX model will be obtained by identification using input-output sample data, and can represent the system behavior and especially be suitable to be applied to design of robust predictive control algorithm. Methods of the model parameters estimation and its evaluation are developed for guaranteeing that the estimated model has the same stability and dynamic mode with original system, and has excellent local and long-term prediction capabilities. On the basis of the generalized SD-ARX model, we will design predictive control algorithm with constraints, which is robustly stable under the effect of modeling error and uncertain disturbance, and has good control performance and also can be quickly implemented on-line. This program is based on the idea of modeling for predictive control all along, and the built model’s features are sufficiently applied to the design of predictive control. This research will propel mutual fusion and mutual promotion of the related studies between nonlinear time series modeling method and predictive control theory, and it will provide a systematic, effective and realizable integrated solution for problems of nonlinear system modeling and optimal control to the complicated systems being studied in this program.