Energy Consumption Optimization for Mobile Robots Motion Using Predictive Control

Yacoub, Mostafa; Necsulescu, D.; Sąsiadek, Jurek Z.

doi:10.1007/s10846-016-0352-1

Cited by 9 publications

(3 citation statements)

References 21 publications

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“…SOC and temperature for the battery of mobile robots can be monitored with the Internet of Things (IoT), as described by [13]. In literature already exist efficient predictive models of energy consumption for mobile robots, given the known state of charge, like the one employed by [14]. No model applicable to mobile manipulators was found.…”

Section: State Of the Artmentioning

confidence: 99%

Intelligent energy management for mobile manipulators using machine learning

Antonelli¹,

Aliev²

2022

FME Transactions

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Integrated robotic systems combining manipulators with mobile robots provide outstanding improvement opportunities for semi-automatic assembly processes leveraged by Industry 4.0. Factory operations are released from the rigid layout constraints imposed by conventional fixed robots. Thus, they introduce new challenges in managing the recharge cycles as the energy consumption of mobile manipulators is not simply related to the travelled distance but to the overall tasks executed. Its estimation requires a systemic approach. In the proposed solution, an intelligent monitoring system is implemented on board. Data gathered online, and Key Performance Indicators (KPIs) calculated during the working tasks are exploited by Machine Learning (ML) to optimize energy recharging cycles. Although the development of an intelligent monitoring framework for a mobile manipulator was the original objective of the research, the monitoring system is exploited here for energy management only, leaving space for other future applications.

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Section: State Of the Artmentioning

confidence: 99%

Intelligent energy management for mobile manipulators using machine learning

Antonelli¹,

Aliev²

2022

FME Transactions

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“…Since the data transmission-line used by RS-232 is unbalanced and is a differential-free reception method, the transmitted signal will be affected when the signal passes through the electrical interference environment. Therefore, the data transmission rate is limited to 20 KB/s; the transmission distance is limited to 15 m, but RS-232 is also the most widely used serial communication interface standard [8].…”

Section: Communication Modulementioning

confidence: 99%

Research on temperature control of heating furnace with intelligent proportional integral derivate control algorithm

Zheng

2020

THERM SCI

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In view of the problems of large overshoot and large oscillation frequency in current furnace temperature control, based on the development of intelligent control theory, expert control, fuzzy control, and neural network control in intelligent control theory are combined with proportional integral derivative (PID) control. The intelligent PID control algorithm is used to carry out numerical simulation and experimental research on these several control algorithms. The results show that the adjustment effect of the intelligent PID control algorithm is significantly better than the traditional PID control algorithm. Among them, the fuzzy self-tuning PID control algorithm and the fuzzy immune PID control algorithm are feasible in the application of furnace temperature control. The neural network PID control algorithm It also has good development and application potential.

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“…These AMRs with extended energy endurance, more precise motion ability, and effective control approaches have been applied in the transportation, security, and inspection domains. Thus, a precise motion controller for AMRs and energy saving are becoming increasingly important in the robotics application field, which have been discussed in many studies [1][2][3][4][5][6]. According to existing studies, it remains difficult to improve the control design when using an extremely simple structure and to realize energy saving in AMRs while accurately and effectively tracking the desired robot trajectory.…”

Section: Introductionmentioning

confidence: 99%

Energy Saving Control Approach for Trajectory Tracking of Autonomous Mobile Robots

Chen¹,

Chen²,

Lou³

et al. 2022

Intelligent Automation &Amp; Soft Computing

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This research presents an adaptive energy-saving H 2 closed-form control approach to solve the nonlinear trajectory tracking problem of autonomous mobile robots (AMRs). The main contributions of this proposed design are as follows: closed-form approach, simple structure of the control law, easy implementation, and energy savings through trajectory tracking design of the controlled AMRs. It is difficult to mathematically obtained this adaptive H 2 closed-form solution of AMRs. Therefore, through a series of mathematical analyses of the trajectory tracking error dynamics of the controlled AMRs, the trajectory tracking problem of AMRs can be transformed directly into a solvable problem, and an adaptive nonlinear optimal controller, which has an extremely simple form and energy-saving properties, can be found. Finally, two test trajectories, namely circular and S-shaped reference trajectories, are adopted to verify the control performance of the proposed adaptive H 2 closed-form control approach with respect to an investigated H 2 closed-form control design.

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Energy Consumption Optimization for Mobile Robots Motion Using Predictive Control

Cited by 9 publications

References 21 publications

Intelligent energy management for mobile manipulators using machine learning

Intelligent energy management for mobile manipulators using machine learning

Research on temperature control of heating furnace with intelligent proportional integral derivate control algorithm

Energy Saving Control Approach for Trajectory Tracking of Autonomous Mobile Robots

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