No abstract

No abstract

Tracking control for hydraulic systems is a key system requirement, as these devices must often follow prescribed motions. Tracking control of hydraulic systems has been approached using both linear and nonlinear control laws. The latter provides improved performance, but at the expense of additional sensors. Further, the control laws often employ hydraulic fluid bulk modulus—a difficult-to-characterize quantity—as a parameter. To overcome these difficulties, we have developed a control design procedure that requires no additional sensors and is robust to variations in the bulk modulus. A dual approach of singular perturbation theory and Lyapunov techniques form the basis for the procedure. For the cases of a small-amplitude sinusoidal input and large-amplitude polynomial input, a candidate system achieved good tracking performance and exhibited outstanding robustness. The ability to accomplish good tracking in a robust manner with no additional sensors provides advantages over other nonlinear tracking algorithms.

Experiments indicate that at small servovalve spool displacements, leakage flow between the valve spool and body dominates the orifice flow through the valve. In precision positioning applications, where the servovalve operates within the null region, this flow, if ignored, may severely degrade the performance of a conventional servohydraulic design. We have developed an improved servovalve model that combines both leakage and orifice flow. The model was developed by reviewing experimental leakage flow data and identifying a simple mathematical form that (1) made physical sense and (2) replicated experimental data. When combined with orifice relations, the model extends the accuracy and region of applicability of existing servovalve models. Furthermore, the combined model is easily parameterized using available manufacturer data. [S0022-0434(00)00403-2]

The development of automated modeling software requires strategies for synthesizing mathematical models of systems with distributed and discrete characteristics. A model order deduction algorithm (MODA) is developed to deduce a Proper System Model by selecting the proper complexity of submodels of components in a system subject to a frequency based metric. A Proper Model in this context means that (1) the system model has the minimum spectral radius out of all possible system models of equivalent or greater complexity, and (2) any increase in the model complexity will result in spectral radius beyond a specific frequency range of interest. Proper Models are also defined to have physically meaningful parameters. Proper Models are intended to be useful for design, where mapping the relationship between design parameters and dominant system dynamics is critical. While MODA is illustrated using the application of machine-tool drive systems, it is readily applicable to other modeling applications.