Authors:
Abhinna Jain
;
C. M. Krishna
;
Israel Koren
and
Zahava Koren
Affiliation:
University of Massachusetts, United States
Keyword(s):
Cyber-physical Systems, Task Scheduling, Cost Functions, Controlled Plant Dynamics.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computer and Microprocessor-Based Control
;
Energy Efficiency and Green Manufacturing
;
Engineering Applications
;
Formal Methods
;
Industrial Engineering
;
Informatics in Control, Automation and Robotics
;
Intelligent Components for Control
;
Intelligent Control Systems and Optimization
;
Optimization Algorithms
;
Planning and Scheduling
;
Real-Time Systems Control
;
Robotics and Automation
;
Signal Processing, Sensors, Systems Modeling and Control
;
Simulation and Modeling
;
Symbolic Systems
;
Vehicle Control Applications
Abstract:
In Cyber Physical Systems (CPS), computational delays can cause the controlled plant to exhibit degraded control. The traditional approach to scheduling in such systems has been to define controller task deadlines, based on the dynamics of the controlled plant. Controller tasks are then scheduled to meet these deadlines; meeting the deadline is considered the sole criterion for scheduling success.\\
This traditional approach has the advantage of simplicity, but overlooks the fact that the quality of control depends on the actual task response times. Two different schedules, each satisfying the task deadlines, can provide very different levels of control quality, if their task response times are different.\\
In this paper, we consider using cost functions of task response time to capture the impact of computational delay on the quality of control. Since the controller workload typically consists of multiple tasks, these cost functions are multivariate in nature. Furthermore, since the
se tasks are generally coupled, the response time of one control task can affect the sensitivity of the controlled plant to the response times of other tasks.\\
In this paper, we first demonstrate how a multivariate cost function can be formulated to quantify the effect of computational delays in vehicles. We then develop cost-sensitive real-time control task scheduling algorithms. We use as an application example an automobile: the controller workload consists of steering and torque control. Our results indicate that cost-function-based scheduling provides superior control to the traditional deadline-only-based approach.
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