子空间辨识作为一种新的辨识算法，由于其对先验知识要求较少，对于多变量系统辨识广泛适用，以及在数值计算上的优势，受到了控制和辨识领域的广泛关注。在开环情况下，由于不可测噪声与输入之间无关性条件成立，使得辨识结果满足一致无偏性要求。但是，由于工艺或者稳定性需要，许多工业过程必须在闭环条件下进行辨识，当系统中存在反馈，上述无关性不再成立，成为子空间方法应用于闭环辨识的主要障碍。本文结合线性代数和几何学的基本概念，将输入输出误差序列包含至输入子空间中，基于输入扩张的状态空间构造方法，在子空间辨识框架内提出一种新的闭环辨识算法；解决开环算法应用于闭环系统辨识时产生有偏估计，甚至不能正确辨识的问题；实现闭环条件下对系统状态空间矩阵的一致估计，并理论证明该辨识算法的渐进一致性；最后通过仿真实例来验证本算法的有效性。

针对一类工业过程中可描述成Wiener模型的非线性系统，其辨识问题可等价成以估计参数为优化变量的非线性极小值优化问题。利用粒子群优化（PSO）算法在整个参数空间内并行搜索获得极小值优化问题的最优解（Wiener模型的最优估计），通过对粒子的迭代轨迹进行分析，改进了PSO算法中惯性权重和学习因子的选择。通过一个Wiener模型的数值仿真验证了本文提出的辨识方法的有效性和实用性，并将该方法应用在连续退火机组加热炉产品质量模型的辨识研究，取得了满意的辨识效果。

This paper presents an effcient distributed model predictive control scheme based on Nash optimality, in which the on-line optimization of the whole system is decomposed into that of several small co-operative agents in distributed structures, thus it can signi ficantly reduce computational complexity in model predictive control of large-scale systems. The relevant nominal stability and the performance on single-step horizon under the communication failure are investigated. The Shell heavy oil fractionator benchmark control problem is illustrated to verify the effectiveness of the proposed control algorithm. © 2004 Elsevier Inc. All rights reserved.

Abstract-This paper proposes a generalized varying-domain optimization method for fuzzy goal programming incorporating multiple priorities. According to the three possible styles of the objective function, the varying-domain optimization method and its generalization are corresponding proposed. In contrast to the previous method, the proposed method can make that the higher priority achieving the higher satisfaction degree. In this way, the decision-maker can get the optimal solution as well as guarantee the priorities of the multiple objective optimization problem. We demonstrate the power of this proposed method by three illustrative examples and a practice application.

Performance assessment of control loops has got the attention of many researchers. This study focuses on performance assessment of model predictive control. An MPC-achievable benchmark is proposed based on subspace identification; the advantage of this benchmark is that only system input and output data is used and the state space model need not explicitly calculated during identification, in addition, it can be implemented to both univariate and multivariate process. Two performance measures can be constructed to assess the control loop, which can evaluate the potential benefit to update the new identified model. To find the potential benefit by tuning the parameter, tradeoff curves similar to LQG benchmark can be draw as reference. The effect of constraints imposed on process variables can be evaluated by the installed controller benchmark. Simulation about an industrial example was done using the proposed method.