A Heuristic Task Periods Selection Algorithm for Real-Time Control Systems on a Multi-Core Processor

Fu, Haipeng; Liu, Jiankang; Han, Zhenyu; Shao, Zhongxi

doi:10.1109/access.2017.2768559

Cited by 4 publications

(8 citation statements)

References 38 publications

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“…Du et al [15] presented an analytical solution using the Lagrange multiplier method and an online algorithm for overloaded situations. Fu et al [9] developed a heuristic algorithm for multicore processors.…”

Section: Related Workmentioning

confidence: 99%

“…Among various models in the literature, we chose the linear control cost model from Bini and Cervin [6] that represents a control system's performance as an approximate linear cost function of its control period and delay [6]. This model has been used as a standard tool by many control-scheduling codesign studies [7][8][9][10]. The second step is to define the optimization constraint, that is, the schedulability constraint in our problem.…”

Section: Introductionmentioning

confidence: 99%

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AUTOSAR Runnable Periods Optimization for DAG-Based Complex Automobile Applications

Daeho¹,

Kim

2020

Applied Sciences

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When developing an automobile control application, its scheduling parameters as well as the control algorithm itself should be carefully optimized to achieve the best control performance from given computing resources. Moreover, since the wide acceptance of the AUTOSAR standard, where finer-granular scheduling entities (called runnables) rather than the traditional real-time tasks are used, the number of scheduling parameters to be optimized is far greater than the traditional task-based control systems. Hence, due to the vast problem space, it is not feasible to reuse existing time-consuming search-based optimization methods. With this motivation, this paper presents an analytical codesign method for deciding runnable periods that minimize given control cost functions. Our solution approach, based on the Lagrange multiplier method, can find optimized runnable periods in polynomial times due to its analytical nature. Moreover, our evaluation results for synthesized applications with varying complexities show that our method performs significantly better (12% to 59% of control cost reductions) than a state-of-the-art evolutionary algorithm. To the best of our knowledge, this study is one of the first attempts to find runnable periods that maximize a given system’s control performance.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

AUTOSAR Runnable Periods Optimization for DAG-Based Complex Automobile Applications

Daeho¹,

Kim

2020

Applied Sciences

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“…Real-time systems have grown in demand in the market especially in industrial environments [1,2]. They are time constrained and deterministic systems that respond within a specified time frame.…”

Section: Introductionmentioning

confidence: 99%

“…Real-time control is one of the most common applications of real time systems. Although numerous real-time control methods have been presented and achieved exciting results [1][2][3][4], the machine learning technology is not included for high performance control. More importantly, they are still restricted with constant control gain, which may not be applicable for real-world engineering domains under time-varying uncertainties [5].…”

Section: Introductionmentioning

confidence: 99%

Artificial Bee Colony Optimization Algorithm Incorporated With Fuzzy Theory for Real-Time Machine Learning Control of Articulated Robotic Manipulators

Huang

Chuang

2020

IEEE Access

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This paper presents a real-time machine learning control (MLC) of articulated robotic manipulators using artificial bee colony optimization (ABC) algorithm incorporated with fuzzy theory. The modified ABC with dynamic weight is used to optimize the fuzzy structure and fractional order. The fractional parameters, fuzzy membership functions and rule base are determined by means of the ABC computation. This ABC-fuzzy hybrid learning algorithm is applied to real-time MLC of robotic manipulators by including fractional order proportional-integral-derivative (FOPID) control strategy. The MLC's control gain parameters are online tuned via the ABC-fuzzy optimization. With the kinematics analysis of a sixdegree-of-freedom (DOF) articulated arm via reverse coordinates approach, an ABC-fuzzy MLC is developed to achieve motion control. A real-time operating system (RTOS) on a microprocessor collaborates with the ABC-fuzzy MLC to meet critical timing constraint by considering the dynamics of actuators. Finally, the mechatronic design and experimental setup of a six-DOF articulated robotic manipulator are constructed. Experimental results and comparative works are provided to demonstrate the merit of the proposed methods. Compared with the conventional control schemes, the proposed ABC-fuzzy MLC has theoretical and practice significance in term of real-time capability, online parameter tuning, convergent behavior and hybrid MLC. The proposed MLC methodologies are applicable to designing real-time modern controllers in both industry and academia. INDEX TERMS Artificial bee colony optimization, fuzzy theory, machine learning control, robotic arm.

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“…Since multi-core architecture has improved processor computing power and parallel processing capacity, multiple realtime applications can be integrated into a single multi-core processor platform, which conserves hardware resources and improves the overall system performance. With the increasing of integration and complexity, the design and optimization of multi-core processor based control system has become a more challenging work [10]. Traditional CNC system is a typical serial execution system.…”

Section: Introductionmentioning

confidence: 99%

Architecture Modelling and Task Scheduling of an Integrated Parallel CNC System in Docker Containers Based on Colored Petri Nets

et al. 2019

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With the diversification of product types and the development of complex CNC system functions, reconfigurable, and extensible CNC system must be developed to meet the needs of industrial production in the new era. To optimize the original architecture between the CNC system and the machine tool, and to improve the integration level of the industrial control system, an integrated parallel CNC system which could control multiple CNC machine tools based on high performance multi-core processor is proposed in this paper. Based on the virtualize container technology, the functional modules of the CNC system, such as the interpreter module and interpolation module, operate separately on each Docker containers. The system scheduling module uniformly schedules above functional modules and caches the data generated by each module to maximize the processing efficiency and resource utilization. For the modeling of system architecture, the colored Petri nets are used to establish the models of CNC system and task scheduling. Two strategies of task scheduling which based on task dependencies and data flow are presented to determine the priority of system function threads. According to the developed test platform, the real time of the virtual system environment, real-time system environment, and the communication module of integrated CNC are analyzed. Furthermore, test results show that the proposed integrated parallel CNC system has the ability of centralized control of two machine tools synchronously. INDEX TERMS Integrated parallel CNC system, docker containers, colored Petri nets, architecture modelling, task scheduling.

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A Heuristic Task Periods Selection Algorithm for Real-Time Control Systems on a Multi-Core Processor

Cited by 4 publications

References 38 publications

AUTOSAR Runnable Periods Optimization for DAG-Based Complex Automobile Applications

AUTOSAR Runnable Periods Optimization for DAG-Based Complex Automobile Applications

Artificial Bee Colony Optimization Algorithm Incorporated With Fuzzy Theory for Real-Time Machine Learning Control of Articulated Robotic Manipulators

Architecture Modelling and Task Scheduling of an Integrated Parallel CNC System in Docker Containers Based on Colored Petri Nets

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