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 contributionpeer-review

16 Citations (Scopus)


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 (
Original languageEnglish
Title of host publicationUnknown Host Publication
Number of pages4
Publication statusPublished (in print/issue) - 24 May 2015
EventIEEE International Symposium on Circuits & Systems (ISCAS) - Lisbon
Duration: 24 May 2015 → …


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


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


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