Mitigating the effect of potential disruptive events at the operating phase of an engineered system therefore improving the system’s failure resilience is an importance yet challenging task in system operation. For complex networked system, different stakeholders complicate the analysis process by introducing different characteristics, such as different types of material flow, storage, response time, and flexibility. With different types of systems, the resilience can be improved by enhancing the failure restoration capability of the systems with appropriate performance recovery strategies. These methods include but not limit to, rerouting paths, optimal repair sequence and distributed resource centers. Considering different characteristics of disruptive events, effective recovery strategies for the failure restoration must be selected correspondingly. However, the challenge is to develop a generally applicable framework to optimally coordinate different recovery strategies and thus lead to desirable failure restoration performances. This paper presents a post-disruption recovery decision-making framework for networked systems, to help decision-makers optimize recovery strategies, in which the overall recovery task is formulated as an optimization problem to achieve maximum resilience. A case study of an electricity distribution system is used to demonstrate the feasibility of the developed framework and the comparison of several recovery strategies for disruption management.
Biocompatibility and osteointegration of implants are highly desired in orthopedic and dentistry applications. The synthesis of a coating with ideal biocompatibility and osteogenic effect carries practical significance for improving the...
This article presents an adaptive integral backstepping controller (AIBC) for permanent magnet synchronous motors (PMSMs) with adaptive weight particle swarm optimization (AWPSO) parameters optimization. The integral terms of dq axis current following errors are introduced into the control law, and by constructing an appropriate Lyapunov function, the adaptive law with the differential term and the control law with the integral terms of the current error are derived to weaken the influence of internal parameters perturbation on current control. The AWPSO algorithm is used to optimize the parameters of the AIBC. Based on the analysis of single-objective optimization and multi-objective realization process, a method for transforming multi-objective optimization with convex Prato frontier into single-objective optimization is presented. By this method, a form of fitness function suitable for parameters optimization of backstepping controller is determined, and according to the theoretical derivation and large number of simulation results, the corresponding parameters of the optimization algorithm are set. By randomly adjusting the inertia weight and changing the acceleration factor, the algorithm can accelerate the convergence speed and solve the problem of parameters optimization of the AIBC. The feasibility and effectiveness of the proposed controller for PMSM are verified by simulation and experimental studies.
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