Case Study: Bio-inspired Self-adaptive Strategy for Spike-based PID Controller

Junxiu Liu, Jim Harkin, W McElholm, LJ McDaid, A Jimenez-Fernandez, Alejandro Linares-Barranco

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Citations (Scopus)

Abstract

A key requirement for modern large scale neuromorphic systems is the ability to detect and diagnose faults and to explore self-correction strategies. In particular, to perform this under area-constraints which meet scalability requirements of large neuromorphic systems. A bio-inspired online fault detection and self-correction mechanism for neuro-inspired PID controllers is presented in this paper. This strategy employs a fault detection unit for online testing of the PID controller; uses a fault detection manager to perform the detection procedure across multiple controllers, and a controller selection mechanism to select an available fault-free controller to provide a corrective step in restoring system functionality. The novelty of the proposed work is that the fault detection method, using synapse models with excitatory and inhibitory responses, is applied to a robotic spike-based PID controller. The results are presented for robotic motor controllers and show that the proposed bio-inspired self-detection and self-correction strategy can detect faults and re-allocate resources to restore the controller’s functionality. In particular, the case study demonstrates the compactness (
LanguageEnglish
Title of host publicationUnknown Host Publication
Number of pages4
Publication statusPublished - 24 May 2015
EventIEEE International Symposium on Circuits & Systems (ISCAS) - Lisbon
Duration: 24 May 2015 → …

Conference

ConferenceIEEE International Symposium on Circuits & Systems (ISCAS)
Period24/05/15 → …

Fingerprint

Controllers
Fault detection
Robotics
Failure analysis
Scalability
Large scale systems
Managers
Testing

Keywords

  • Bio-inspired system
  • robotics
  • fault tolerant
  • self-correction
  • hardware adaption

Cite this

Liu, J., Harkin, J., McElholm, W., McDaid, LJ., Jimenez-Fernandez, A., & Linares-Barranco, A. (2015). Case Study: Bio-inspired Self-adaptive Strategy for Spike-based PID Controller. In Unknown Host Publication
Liu, Junxiu ; Harkin, Jim ; McElholm, W ; McDaid, LJ ; Jimenez-Fernandez, A ; Linares-Barranco, Alejandro. / Case Study: Bio-inspired Self-adaptive Strategy for Spike-based PID Controller. Unknown Host Publication. 2015.
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title = "Case Study: Bio-inspired Self-adaptive Strategy for Spike-based PID Controller",
abstract = "A key requirement for modern large scale neuromorphic systems is the ability to detect and diagnose faults and to explore self-correction strategies. In particular, to perform this under area-constraints which meet scalability requirements of large neuromorphic systems. A bio-inspired online fault detection and self-correction mechanism for neuro-inspired PID controllers is presented in this paper. This strategy employs a fault detection unit for online testing of the PID controller; uses a fault detection manager to perform the detection procedure across multiple controllers, and a controller selection mechanism to select an available fault-free controller to provide a corrective step in restoring system functionality. The novelty of the proposed work is that the fault detection method, using synapse models with excitatory and inhibitory responses, is applied to a robotic spike-based PID controller. The results are presented for robotic motor controllers and show that the proposed bio-inspired self-detection and self-correction strategy can detect faults and re-allocate resources to restore the controller’s functionality. In particular, the case study demonstrates the compactness (",
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Liu, J, Harkin, J, McElholm, W, McDaid, LJ, Jimenez-Fernandez, A & Linares-Barranco, A 2015, Case Study: Bio-inspired Self-adaptive Strategy for Spike-based PID Controller. in Unknown Host Publication. IEEE International Symposium on Circuits & Systems (ISCAS), 24/05/15.

Case Study: Bio-inspired Self-adaptive Strategy for Spike-based PID Controller. / Liu, Junxiu; Harkin, Jim; McElholm, W; McDaid, LJ; Jimenez-Fernandez, A; Linares-Barranco, Alejandro.

Unknown Host Publication. 2015.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AU - Harkin, Jim

AU - McElholm, W

AU - McDaid, LJ

AU - Jimenez-Fernandez, A

AU - Linares-Barranco, Alejandro

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N2 - A key requirement for modern large scale neuromorphic systems is the ability to detect and diagnose faults and to explore self-correction strategies. In particular, to perform this under area-constraints which meet scalability requirements of large neuromorphic systems. A bio-inspired online fault detection and self-correction mechanism for neuro-inspired PID controllers is presented in this paper. This strategy employs a fault detection unit for online testing of the PID controller; uses a fault detection manager to perform the detection procedure across multiple controllers, and a controller selection mechanism to select an available fault-free controller to provide a corrective step in restoring system functionality. The novelty of the proposed work is that the fault detection method, using synapse models with excitatory and inhibitory responses, is applied to a robotic spike-based PID controller. The results are presented for robotic motor controllers and show that the proposed bio-inspired self-detection and self-correction strategy can detect faults and re-allocate resources to restore the controller’s functionality. In particular, the case study demonstrates the compactness (

AB - A key requirement for modern large scale neuromorphic systems is the ability to detect and diagnose faults and to explore self-correction strategies. In particular, to perform this under area-constraints which meet scalability requirements of large neuromorphic systems. A bio-inspired online fault detection and self-correction mechanism for neuro-inspired PID controllers is presented in this paper. This strategy employs a fault detection unit for online testing of the PID controller; uses a fault detection manager to perform the detection procedure across multiple controllers, and a controller selection mechanism to select an available fault-free controller to provide a corrective step in restoring system functionality. The novelty of the proposed work is that the fault detection method, using synapse models with excitatory and inhibitory responses, is applied to a robotic spike-based PID controller. The results are presented for robotic motor controllers and show that the proposed bio-inspired self-detection and self-correction strategy can detect faults and re-allocate resources to restore the controller’s functionality. In particular, the case study demonstrates the compactness (

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Liu J, Harkin J, McElholm W, McDaid LJ, Jimenez-Fernandez A, Linares-Barranco A. Case Study: Bio-inspired Self-adaptive Strategy for Spike-based PID Controller. In Unknown Host Publication. 2015