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.

In this paper, a novel parameter identification method for closed-loop two-input two-output (TITO) processes from step-test is proposed. Through sequential step change ofset points, the coupled closed-loop TITO system is decoupled equivalently into four independent single open-loop processes with same input signal acting on the four transfer functions. Consequently, existing identification methods for single-loop process can be extended to TITO systems and the parameters of first-or second-order plus dead-time models for each transfer function can be directly obtained by using the linear regression equations derived for the decoupled identification system. The proposed method is simple for engineering application and robust in the presence of large amounts of measurement noise. Simulation examples are given to show both effectiveness and practicality of the identification method for a wide range of multivariable processes. © 2004 Elsevier Ltd. All rights reserved.

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.

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.

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.