Abstract-This paper presents the new development of the boiler-turbine coordinated control strategy using fuzzy reasoning and autotuning techniques. The boiler-turbine system is a very complex process that is a multivariable, nonlinear, slowly time-varying plant with large settling time and a lot of uncertainties. As there exist strong couplings between the main steam pressure control loop and the power output control loop in the boiler-turbine unit with large time-delay and uncertainties, automatic coordinated control of the two loops is a very challenging problem. This paper presents a new coordinated control strategy (CCS) which is organized into two levels: a basic control level and a high supervision level. Proportional-integral derivative (PID) type controllers are used in the basic level to perform basic control functions while the decoupling between two control loops can be realized in the high level. A special subclass of fuzzy inference systems, called the Gaussian partition with evenly (GPE) spaced midpoints systems, is used to self-tune the main steam pressure PID controller's parameters online based on the error signal and its first difference, aimed at overcoming the uncertainties due to changing fuel calorific value, machine wear, contamination of the boiler heating surfaces and plant modeling errors. For the large variation of operating condition, a supervisory control level has been developed by autotuning technique. The developed CCS has been implemented in a power plant in China, and satisfactory industrial operation results demonstrate that the proposed control strategy has enhanced the adaptability and robustness of the process. Indeed, better control performance and economic benefit have been achieved.

Multiple model predictive control (MMPC) strategy based on the Takagi-Sugeno (T-S) model is proposed in this paper. A T-S modeling method using fuzzy satisfactory clustering (FSC) algorithm is introduced at first. FSC is designed to help quickly determine satisfactory number of rules of a T-S model. Based on the T-S model, MMPC strategy is presented using parallel distribution compensation (PDC) method, i. e. different predictive controllers are designed for different rules (local sub-systems). The global controller output is the fuzzy weighted integration of local ones. MMPC with system constraints are also considered in this paper. The presented modeling and controller design procedure is demonstrated on an MIMO simulated pH neutralization process. © 2003 Elsevier Inc. All rights reserved.

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.

多模型预测控制是对多变量有约束的复杂非线性系统进行控制设计的主要方法，问题的关键是非线性系统采用局部线性模型进行多步预测时的准确性。本文在多模型预测控制引入混合逻辑方法，将非线性过程描述成为一个混合逻辑动态系统模型，模型切换规则以先验知识的形式引入到多模型预测过程中，该模型可以全局地表征非线性过程的特性，从而解决了多模型约束非线性预测控制的模型预测与模型切换问题。

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。

本文针对一类具有多个平衡点的非线性系统，结合控制不变集与和增益调度算法，提出了一种基于扩张终端约束集的双模预测控制设计方法。该方法在非线性系统的有限个平衡点上设计局部LQR控制律，并计算它们的最大控制不变集，在平衡点的选取过程中确保它们的控制不变集是相互重叠的。本文将控制不变集的并集作为双模预测控制的终端约束集，研究了变时域和固定时域模式下双模预测控制的可行性与稳定性。由于使用了扩张终端约束集，使得较小的预测时域即可保证优化的可行性，从而降低了优化问题的规模，减少了在线计算量。仿真结果表明了本文算法的有效性。

This paper is concerned with air handling units (AHUs), the performances of which directly influence those of heating, ventilation and air conditioning systems. An autotuning recedinghorizon optimization method is proposed to synthesize a proportional-integral-derivative (PID) type controller for AHUs. This algorithm is composed of two levels of control. The lower level adopts a conventional PID controller to obtain an acceptable, but not necessarily optimal, performance. The higher level provides optimal low-level controller parameters through minimization of the generalized predictive control criterion. Because the method does not require changes in hardware and the definitions of conventional controller parameters, it can be both easily accepted by process engineers and widely applied to industrial areas. Compared with the performance of a well-tuned conventional PID controller, simulation and experimental results show that the proposed method for AHU systems can achieve a better performance under a wide range of operating conditions.

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.

本文充分考虑大多数复杂热工控制对象非线性特性与运行工况密切相关的实际特点，采用多模型动态矩阵控制方法。并将该方法应用于某电厂300MW机组锅炉过热汽温对象，在典型工况下通过试验数据获得其局部三阶子模型集，基于每个局部子模型分别设计子DMC控制器。通过跟踪实际工况变化来对子控制器加权以获得合适的控制增量。实验结果表明该方法对参数突变适应快，可取得令人满意的控制效果。