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Aperiodic linear networked control considering variable channel delays: application to robots coordination.

Santos C, Espinosa F, Santiso E, Mazo M - Sensors (Basel) (2015)

Bottom Line: One of the main challenges in wireless cyber-physical systems is to reduce the load of the communication channel while preserving the control performance.This way, the controller manages the usage of the wireless channel in order to reduce the channel delay and to improve the availability of the communication resources.Implementation results applying the aperiodic linear control laws on four P3-DX robots are also included.

View Article: PubMed Central - PubMed

Affiliation: Electronics Department, Polytechnics School, University of Alcala, Campus Universitario, Ctra. Madrid-Barcelona, Km. 33,600, 28871. Alcalá de Henares, Madrid, Spain. carlos.santos@depeca.uah.es.

ABSTRACT
One of the main challenges in wireless cyber-physical systems is to reduce the load of the communication channel while preserving the control performance. In this way, communication resources are liberated for other applications sharing the channel bandwidth. The main contribution of this work is the design of a remote control solution based on an aperiodic and adaptive triggering mechanism considering the current network delay of multiple robotics units. Working with the actual network delay instead of the maximum one leads to abandoning this conservative assumption, since the triggering condition is fixed depending on the current state of the network. This way, the controller manages the usage of the wireless channel in order to reduce the channel delay and to improve the availability of the communication resources. The communication standard under study is the widespread IEEE 802.11g, whose channel delay is clearly uncertain. First, the adaptive self-triggered control is validated through the TrueTime simulation tool configured for the mentioned WiFi standard. Implementation results applying the aperiodic linear control laws on four P3-DX robots are also included. Both of them demonstrate the advantage of this solution in terms of network accessing and control performance with respect to periodic and non-adaptive self-triggered alternatives.

No MeSH data available.


Gamma cumulative distribution function of the channel delay. Case study: L = 3.
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f1-sensors-15-12454: Gamma cumulative distribution function of the channel delay. Case study: L = 3.

Mentions: Owing to the fact that the actual network delay is not measured until the robot receives the control information, the possible delays are classified into zones and compensated for the worst case scenario of each zone, thus guaranteeing stability. Therefore, the network delay range is divided into L zones with a single σ value associated with each one. The definition of the different zones is supported by the gamma cumulative distribution function. The election of L presents a trade-off between the computational complexity of the control law implementation and the flexibility to take advantage of the actual channel status. Nonetheless, the number of L zones increases L times the amount of data sent over the network. In the case of packet-based networks, it is not a problem if the size of one packet is not exceed [3]. If the network is non-packet-based, the designer should be aware of the drawback regarding the increase of the data amount. An example where L = 3 is shown in Figure 1.


Aperiodic linear networked control considering variable channel delays: application to robots coordination.

Santos C, Espinosa F, Santiso E, Mazo M - Sensors (Basel) (2015)

Gamma cumulative distribution function of the channel delay. Case study: L = 3.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4507601&req=5

f1-sensors-15-12454: Gamma cumulative distribution function of the channel delay. Case study: L = 3.
Mentions: Owing to the fact that the actual network delay is not measured until the robot receives the control information, the possible delays are classified into zones and compensated for the worst case scenario of each zone, thus guaranteeing stability. Therefore, the network delay range is divided into L zones with a single σ value associated with each one. The definition of the different zones is supported by the gamma cumulative distribution function. The election of L presents a trade-off between the computational complexity of the control law implementation and the flexibility to take advantage of the actual channel status. Nonetheless, the number of L zones increases L times the amount of data sent over the network. In the case of packet-based networks, it is not a problem if the size of one packet is not exceed [3]. If the network is non-packet-based, the designer should be aware of the drawback regarding the increase of the data amount. An example where L = 3 is shown in Figure 1.

Bottom Line: One of the main challenges in wireless cyber-physical systems is to reduce the load of the communication channel while preserving the control performance.This way, the controller manages the usage of the wireless channel in order to reduce the channel delay and to improve the availability of the communication resources.Implementation results applying the aperiodic linear control laws on four P3-DX robots are also included.

View Article: PubMed Central - PubMed

Affiliation: Electronics Department, Polytechnics School, University of Alcala, Campus Universitario, Ctra. Madrid-Barcelona, Km. 33,600, 28871. Alcalá de Henares, Madrid, Spain. carlos.santos@depeca.uah.es.

ABSTRACT
One of the main challenges in wireless cyber-physical systems is to reduce the load of the communication channel while preserving the control performance. In this way, communication resources are liberated for other applications sharing the channel bandwidth. The main contribution of this work is the design of a remote control solution based on an aperiodic and adaptive triggering mechanism considering the current network delay of multiple robotics units. Working with the actual network delay instead of the maximum one leads to abandoning this conservative assumption, since the triggering condition is fixed depending on the current state of the network. This way, the controller manages the usage of the wireless channel in order to reduce the channel delay and to improve the availability of the communication resources. The communication standard under study is the widespread IEEE 802.11g, whose channel delay is clearly uncertain. First, the adaptive self-triggered control is validated through the TrueTime simulation tool configured for the mentioned WiFi standard. Implementation results applying the aperiodic linear control laws on four P3-DX robots are also included. Both of them demonstrate the advantage of this solution in terms of network accessing and control performance with respect to periodic and non-adaptive self-triggered alternatives.

No MeSH data available.