Resilient adaptive control based on renewal particle swarm optimization to improve production system energy efficiency

Abstract Considerable amount of energy may be wasted due to idleness or constraints from the interactions between the machines and buffers. To increase profits, it is desired to quickly identify the energy waste and properly manage machine operations for reducing this energy waste without jeopardizing system production. In this paper, a real-time system performance diagnostic method is developed using both system physical properties and sensor information to identify this energy waste, and evaluate system resilience against random disruption events. Furthermore, by utilizing the real-time system diagnostic results, a real-time resilient adaptive control policy is developed to improve system profit and energy efficiency, and deliver resilient performance against random disruption events. This control policy uses a novel renewal particle swarm optimization (RPSO) algorithm to update its controller parameters, such that the controller is well adapted to the slowly varying system reliabilities. A case study is given to demonstrate the effectiveness of the proposed control policy.

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