CPU Resource Management and Noise Filtering for PID Control

University dissertation from Department of Automatic Control, Lund Institute of Technology, Lund University

Abstract: The first part of the thesis deals with adaptive CPU resource management for multicore platforms. The work was done as a part of the resource manager component of the adaptive resource management framework implemented in the European ACTORS project. The framework dynamically allocates CPU resources for the applications. The key element of the framework is the resource manager that combines feedforward and feedback algorithms together with reservation techniques. The resource requirements of the applications are provided through service level tables. Dynamic bandwidth allocation is performed by the resource manager which adapts applications to changes in resource availability, and adapts the resource allocation to changes in application requirements. The dynamic bandwidth allocation allows to obtain real application models through the tuning and update of the initial service level tables. The second part of the thesis deals with the design of measurement noise filters for PID control. The design is based on an iterative approach to calculate the filter time constant, which requires the information in terms of an FOTD model of the process. Tuning methods such as Lambda, SIMC, and AMIGO are used to obtain the controller parameters. New criteria based on the trade-offs between performance, robustness, and attenuation of measurement noise are proposed for assessment of the design. Simple rules for calculating the filter time constant based on the nominal process model and the nominal controller are then derived, thus, eliminating the need for iteration. Finally, a complete tuning procedure is proposed. The tuning procedure accounts for the effects of filtering in the nominal process. Hence, the added dynamics are included in the filtered process model, which is then used to recalculate the controller tuning parameters.