A review of methods for input/output selection <s> Ezciency of manipulation <s> Abstract This paper is intended to report preliminary results in the area of actuator placement in a lumped parameter system subjected to a disturbance at a known location. A criterion has been established relating the optimal actuator position to the location of the disturbance. Linear time invariant lumped parameter models were chosen to investigate the effect of control actuator locations. The results of the simulations show that the optimal actuator location is the one closest to the source of the unknown disturbance. This work suggests placing the actuator as close as possible to the source of the known disturbance. <s> BIB001 </s> A review of methods for input/output selection <s> Ezciency of manipulation <s> Abstract Two open-loop analysis techniques for assessing input-output controllability in the presence of control constraints are presented. The techniques used are a modified singular value analysis technique and a new optimization-based approach. The case study discussed has, depending on the choice of manipulated variables and measurable outputs, several alternative two-input two-output control schemes. The results are verified by performing closed loop simulations with PI controllers. These show that both methods are able to predict the process' closed loop performance in the presence of control constraints. <s> BIB002
In BIB001 , the cost function J S takes essentially the same form as in the well-known linear quadratic regulator (LQR) problem: Q"Q250 and R"R2'0 are weights. The input set yielding the smallest J S is the most appropriate one. Al-Sulaiman and Zaman (1994) only evaluate J S after a state feedback has been designed by pole placement and a closed-loop simulation for a disturbance has been run over the time interval [0, t C ]. The input selection thus depends on the choice of the disturbance signal.  use a similar cost function with t C "R. They look for the IO set yielding the smallest J S under noise-free static output feedback, by solving a nonlinear programming problem. For nonlinear systems, BIB002 consider selection of inputs with magnitude constraints. The cost function J S takes the form with z P a speci"ed setpoint for the controlled variables z and with Q'0 a diagonal weighting matrix. For a given input set, J S is minimized as a function of the input signal u(t) and the "nal time t C , subject to the constraints u J 4u(t)4u S and subject to the nonlinear system behavior f (x , x, z, u, t)"0 and a given initial and "nal system state. The input set that yields the smallest J S is preferred. Input selection based on this optimization requires large computational e!ort (Cao, 1995, Section 8.2 ) and the results may depend on the choices of z P and the initial conditions.