A Dynamic System Matching Technique for Improving the Accuracy of Subsystems

Many complex systems can be described as a combination of subsystems, that is, the elements of the complex system are themselves systems. A subsystem design is often modelled by a differential equation with several coefficients rather than as a constant, as a resistor is modelled, or as a single parameter equation, as a capacitor is modelled. Manufacturing errors produce subsystems whose coefficients vary from the desired coefficients of subsystem design. The coefficient variations contribute to modelling errors in the subsystem, and thus to modelling errors in the complex system. As is the case of elements in an electronic circuit, one way of controlling the variability of a manufactured subsystem is to impose tight control on the manufacturing process so that the component values are within some specified, acceptable bounds. This can be expensive and, in some applications, it may be impossible to achieve acceptable bounds. In a recent paper, [2], the authors presented a method for combining the measurements from many MEMS gyroscopes using a technique based on the concept of dynamic element matching. This was shown to effectively control the modelling errors when the outputs of the many micro-gyroscopes are corrupted by manufacturing errors. This technique essentially transforms the effects of the many manufacturing errors into an additive white noise in the subsystem output. The effect of the additive white noise can be effectively decreased by applying a filter, e.g., a Kalman filter, to the output signal. In this paper, this technique is generalized to applying the concept of dynamic element matching to complex subsystems which may be ´elements´ in more complex systems. Because the method deals with systems rather than elements, it will be called dynamic system matching.