针对实际工业过程中多变量系统存在着非线性、工况范围广的特点，本文提出了一种新的多模型建模方法。首先对系统调度变量进行满意模糊c均值聚类，在此基础上采用基于加权性能指标的多模型辨识算法辨识多模型系统，得到的模型在全局拟合与局部特性之间取得良好的权衡，同时能得到每个局部模型的适用域。以典型pH中和过程为对象，采用上述建模方法建立其系统多模型，仿真结果验证了该建模方法的有效性。

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.

Knowledge-based control tries to integrate the knowledge of human operators or process engineers into the controller design. Fuzzy control, one of the most popular intelligent techniques, has been successfully applied to a large number of consumer products and industrial processes. Model predictive control (MPC) has been used in process control systems with constraints; however, the constrained optimization problem involved in control systems has generally been solved in practice in a piece-meal fashion. To solve the problem systemically, the multi-objective fuzzyoptimization (MOFO) is used in the constrained predictive control for online applications as a means of dealing with fuzzy goals and fuzzy constraints in control systems. The conventional MPC is integrated with the techniques from fuzzy multicriteria decision making, translating the goals and the constraints to predictive control in a transparent way. This paper investigates how to use the fuzzy goal programming in predictive control and how to use the fuzzy goals and fuzzy constraints in predictive control. The presented method allows for a more flexible aggregation of the control objectives than the usual weighting sum of squared errors. It is shown that the model predictive controller based on MOFO allows the designers a more flexible aggregation of the control objectives than the usual weighting sum of squared errors in MPC. The visual robot path planning validates the efficiency of the presented algorithm.

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.

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.