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.

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.

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 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.

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。