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.

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.

Abstract-Stability and design issues of simple T-S fuzzy control system with simplified linear rule consequent (TSS) are investigated. A systematic approach to find a common matrix P for TSS fuzzy system is presented, where system matrix is decomposed into proportional part Āi and the remainder ΔĀ. Hence an iterative approach to find a common matrix P for pairwise commutative Āi's can be used. The stability of the global system is guaranteed if ΔĀ satisfies certain conditions. Qualitative instructions for TSS control system design are summarized. A physical example is given to illustrate the issues discussed throughout the paper.

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.