Gene

Quantum mechanics

Control (management)

Process (computing)

Engineering

Construct (python library)

Control system

Control engineering

Event (particle physics)

Theoretical computer science

Supervisory control

Biochemistry

Computer science

Distributed computing

Computer network

Single-nucleotide polymorphism

Physics

Political science

Asynchronous communication

Genotype

Artificial intelligence

Supervisor

Chemistry

Programming language

Control theory (sociology)

Electrical engineering

Information Sciences

Asynchronous spiking neural P systems with multiple channels (SNP systems) operate in an asynchronous pattern, in which the firing of enabled rules is not obligatory. Asynchronous models are one of the most potential research carriers of supervisory control. However, a systematic understanding and process of leveraging SNPs to address control issues in discrete event systems are still lacking. This paper extends a type of SNP formalization, called vector-SNP, to explore the system’s intrinsic stability properties without configuration attention by introducing two classes of structural feature vectors: N-invariants and R-invariants. Then, a typical style of specifications, namely neuron mutual exclusion inequality constraints (NMEICS), is advanced for supervisory control using SNPs. An NMEIC is enforced by explicitly adding a supervisor (group of observation and control monitors represented by SNPs) to an SNP system. On top of that, the supervisor construct algorithm that utilizes the linear algebraic calculations is established to obtain the supervisors for pre-designed control constraints. Finally, we evaluate the proposed supervisory control theory using SNPs on an automated manufacturing system. Experimental results achieve maximally permissive controlled behaviors that reserve all feasible legal states.

Supervisory control of discrete event systems under asynchronous spiking neuron P systems