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Decentralised and partially decentralised control strategies are very popular in practice. To come up with a suitable decentralised or partially decentralised control structure, it is important to select the appropriate input and output pairs for control design. This procedure is called control configuration selection. It is well known that a suitable control configuration selection is an important prerequisite for a successful industrial control. In this paper the problem of control configuration selection for multiple-input and multiple-output (MIMO) bilinear processes is addressed. First, the concept of the cross-gramian is developed for bilinear systems. The conditions for the existence of generalised cross-gramian are derived. It is shown that if the cross-gramian exists it is the solution to the generalised Sylvester equation. To obtain the cross-gramian in a more computationally efficient way, an iterative method for solving the generalised Sylvester equation is proposed. The generalised cross-gramian is used to form the generalised Hankel interaction index array. The generalised Hankel interaction index array is used for control configuration selection of MIMO bilinear processes. Most of the results on control configuration selection, which have been proposed so far, can only support linear systems. The proposed method supports bilinear processes, takes the effects of dynamics of the process into account and can be used to propose a richer (sparse or block diagonal) controller structure. More importantly, since for each element of generalised Hankel interaction index array just one generalised Sylvester equation is needed to be solved, the proposed control configuration selection method is computationally more efficient than its gramian-based counterparts. [ABSTRACT FROM AUTHOR]

Water leakage is an important component of water loss. Many methods have emerged from urban water supply systems (WSSs) for leakage control, but it still remains a challenge in many countries. Pressure management is an effective way to reduce the leakage in a system. It can also reduce the power consumption. To have a better understanding of leakage in WSSs, to control pressure and leakage effectively, and for optimal design of WSSs, suitable modeling is an important prerequisite. In this paper a model with the main objective of pressure control and consequently leakage reduction is presented. Following an analogy to electric circuits, first the mathematical expression for pressure drop over each component of the pipe network (WSS) such as pipes, pumps, valves and water towers is presented. Then the network model is derived based on the circuit theory and subsequently used for pressure management in the system. A suitable projection is used to reduce the state vector and to express the model in standard state-space form. [ABSTRACT FROM PUBLISHER]

Accurate and reliable process measurements play a key role in the success of process control. In addition to selecting the instrumentation hardware, it is important to determine which variables should be measured in a process. Therefore it is important to study techniques for placing sensors on the processes. Dually the problem of placing actuators on the processes is equally important. In this paper, the problem of determining optimal sensor and actuator locations for the linear systems is addressed. The problem of the sensor locations is viewed as the problem of maximizing the output energy generated by a given state and for the actuator locations is viewed as the problem of minimizing the input energy required to reach a given state. Such design problems occur in many applications, and therefore have been studied extensively. Unfortunately, the results in this context, which have been proposed so far, only support stable systems. However, in industrial practices it is often the case that the system, which is needed to be controlled, is not stable. The method which is proposed in this paper is a general method in the sense that it supports both stable and unstable systems. The technique is successfully used for determining the optimal sensor locations of the linearized Continuous Stirred-Tank Reactor unstable model. [ABSTRACT FROM AUTHOR]

A new mixed method for relative error model reduction of linear time-invariant systems is proposed in this paper. This order reduction technique is mainly based upon the time-weighted balanced stochastic model reduction method and singular perturbation model reduction technique. Compared to the other analogous counterparts, the proposed method provides more accurate results in terms of time-weighted norms when applied to the practical examples. It is shown that important properties of the time-weighted stochastic balanced reduction technique are extended to the mixed reduction method by using the concept and properties of the reciprocal systems. The results are further illustrated by two practical numerical examples: a model of compact disc player and a model of the atmospheric storm track. [ABSTRACT FROM PUBLISHER]