Feedback Scheduling Of Real-Time Control Systems With Resource Constraints

With rapid evolution and application of information technologies, real-time control systems (RTCS) are becoming ever-increasingly resource limited in recent years. At the same time, real-time control systems often have to operate in dynamic environments that feature workload fluctuation. As a consequence, the available computing and communication resources are typically non-deterministic. In this new implementation environment, traditional control systems design and implementation methodology, which separates control from scheduling, cannot always provide quality of control (QoC) guarantees. From a resource scheduling perspective, existing open-loop real-time scheduling algorithms obviously lack flexibility when applied to RTCS, thereby cannot achieve optimal usage of available resources. Therefore, it is of paramount importance to develop novel frameworks and methods in order for enabling real-time control under dynamic environments and optimizing overall QoC under dynamic resource constraints of implementation platforms.This thesis observes the emerging trend of convergence of control with computing and communication. Following the methodology of control and scheduling codesign, we examine some practical and open problems in RTCS from a unique viewpoint of dynamic resource management. In the context of feedback scheduling, a series of novel and effective solutions are developed for performance optimization of RTCS subject to dynamic resource constraints. The essential technological issues of feedback scheduling are formulated, with real-world application requirements and state of the art in mind. Considering constraints of three representative kinds of resources, i.e. CPU time, energy, and network bandwidth, we present different feedback scheduling methods that exploit a unified framework. They provide enabling technologies for closed-loop dynamic resource scheduling, and tackle a set of essential problems in the emerging field of feedback scheduling. Meanwhile, this thesis holistically addresses the problem of dynamic allocation of bottleneck resources within RTCS, thus providing flexible QoC management mechanisms and achieving overall performance optimization under dynamic environments.As regarding CPU scheduling, we are concerned with multitasking embedded control systems where the processing capacity of the processor is limited. With the goal of optimizing overall control performance, we analytically formulate the problem of optimal feedback scheduling, and then discuss relevant algorithmic solutions. Because algorithmic optimizer is too computationally expensive to be used online, we suggest a new methodology called neural feedback scheduling using neural networks. It could considerably reduce scheduling overhead, while delivering almost-optimal overall control performance. Besides, it is also characterized by good adaptability, robustness, as well as fault-tolerance, etc. Taking into account the unavailability of task execution times and measurement noises, we propose a fuzzy feedback scheduling scheme, which introduces fuzzy control technique into the area of feedback scheduling. Thanksto the powerful capacity of fuzzy logic in handling non-linear, imprecise and uncertain situations, fuzzy feedback scheduling doesn't rely on task execution times, and makes the feedback scheduling system robust against measurement noises inside temporal parameters by copying with the uncertainty of workload and available resources in an intelligent fashion. Additional merits of this method include, e.g., ease of implementation and low scheduling overhead.In the part of energy management, we aim to reduce the energy consumption of the processor as much as possible while preserving the QoC of embedded control systems. A feedback control real-time scheduling approach, i.e. energy-aware feedback scheduling, which combines energy management with QoC management, is presented to solve such practical problems as CPU workload variations and task execution time uncertainty. This approach exploits dynamic voltage scaling technique and indirectly changes task execution times through dynamically adjusting CPU speed. The objective is to control the CPU utilization at a desired level. After analytically modeling the dynamic voltage scaling system, we present a control theoretic design and analysis method for feedback scheduler. In this way, deterministic performance of feedback scheduling is achieved and closed-loop energy management is realized. With the goal of further reducing energy consumption based on energy-aware feedback scheduling, we analyze the dynamic behavior of control systems with variable sampling periods and suggest an enhanced energy-aware feedback scheduling scheme, which adopts the methodology of graceful performance degradation. It dynamically adapts CPU speed using the dynamic voltage scaling technique, and at the same time, adjusts the sampling period of each control loop according to its current control performance. In this way, it takes advantage of flexible timing constraints of control tasks. Since sampling periods are enlarged as much as possible as long as the QoC is not jeopardized, energy consumption is further reduced, which in turn yields higher energy efficiency.With regard to bandwidth allocation, we focus on networked control systems that use priority-based fieldbuses and wireless control systems employing random medium access protocols. From a viewpoint of feedback control and network scheduling codesign, we present an integrated feedback scheduling scheme for multi-loop networked control systems. Exploiting a cascaded feedback scheduler, it adapts sampling periods with respect to the dynamic changes of available bandwidth. The objective is to maintain the deadline miss ratio at a desired low level and optimize the bandwidth allocation. In order to further improve overall control performance, we suggest a direct feedback scheduling mechanism, which re-assigns priorities to control loops at runtime based on their actual control performance. It is argued that integrated feedback scheduling could maximize resource utilization in underloaded scenarios, and could achieve graceful degradation of control performance under overload conditions. Consequently, the overall control performance could be significantly improved. Recognizing the inherent uncertainty of available link resources in wireless control systems, we present an adaptive feedback scheduling scheme that integrates the methodology of cross-layer design. This scheme deals with disturbing signals andvariations of link transmission rate by exchanging information between physical and application layers. The deadline miss ratio is controlled in a way that the overall control performance is optimized. Furthermore, we suggest an event-based invocation mechanism for feedback schedulers, which is quite effective in that quick response is enhanced while scheduling overhead decreases at the same time. The result is that practical efficiency of feedback schedulers would be considerably improved.In this thesis we go beyond the traditional control systems design methodology that features separation of concerns of control and scheduling, and discard the traditional control task model with fixed timing constraints. Completely fresh methodology and solutions are provided for RTCS research and development in the context of new technological trends. This thesis preliminarily establishes a systematic framework of feedback scheduling with a set of promising results, which enrich this emerging area from various perspectives. With an enabling technology produced, it is believed that our results will contribute to future holistic integration of control with computing and communication.