Managing Temporal Constraints in Control Systems

Design and implementation of motion control applications include the mapping of control design to real-time system implementation. Important parameters from control design include deviation from nominal period time of an activity, end-to-end timing constraints, temporal correlation between different sampling tasks, and constraints on temporal variations in output. These parameters should also be considered in the real-time systems design, since translating them to simple deadlines may lead to sub-optimal solutions. Many real-time systems in industry today are based on pre-emptive priority based run-time systems, and hence, it is highly desirable to fulfill the temporal requirements by correctly assigning attributes such as priorities and offsets to the tasks executing in such systems. However, this is a non-trivial mapping, which should be supported by appropriate methods and tools. In this paper we propose a method, which by assigning priorities and offsets to tasks provides guarantees that complex timing constraints are met. The method handles periodic and sporadic tasks, shared resources, and varying execution times of tasks. We present the method, which uses a genetic algorithm, together with simulation results, showing that the proposed method is capable to efficiently handle complex constraints on task sets of realistic sizes covering most embedded control systems.