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。