Electro-Hydraulic Servo System Identification of Continuous Rotary Motor Based on the Integration Algorithm of Genetic Algorithm and Ant Colony Optimization

In order to increase the robust performance of electro-hydraulic servo system, the system transfer function was identified by the intergration algorithm of genetic algorithm and ant colony optimization(GA-ACO), which was based on standard genetic algorithm and combined with positive feedback mechanism of ant colony algorithm. This method can obtain the precise mathematic model of continuous rotary motor which determines the order of servo system. Firstly, by constructing an appropriate fitness function, the problem of system parameters identification is converted into the problem of system parameter optimization. Secondly, in the given upper and lower bounds a set of optimal parameters are selected to meet the best approximation of the actual system. And the result shows that the identification output can trace the sampling output of actual system, and the error is very small. In addition, another set of experimental data are used to test the identification result. The result shows that the identification parameters can approach the actual system. The experimental results verify the feasibility of this method. And it is fit for the parameter identification of general complex system using the integration algorithm of GA-ACO.

Resource Allocation Algorithm Based on PSO-GA for Multi-User OFDM System

In order to minimize the transmitted power in the multi-user orthogonal frequency division multiplexing（OFDM） system, a scheme combining the improved particle swarm optimization（POS） algorithm with genetic algorithm（GA） is proposed to optimize the sub-carriers and bits allocation. In the algorithm, a random velocity between the maximum and minimum particle velocity is used as the updating velocity instead of maximum or minimum velocity when the updated particle velocity is higher than the maximum particle velocity or lower than the minimum particle velocity. Then, the convergence population is used as the initial population of the genetic algorithm to optimize the subcarriers and bits allocation further. Simulation results show that the transmitted power of the proposed algorithm is about 2 d B to 10 d B lower than that of the genetic algorithm, particle swarm optimization algorithm, and Zhang＇s algorithm.

Flexible networked rural electrification using levelized interpolative genetic algorithm

Networked rural electrification is an alternative approach to accelerate rural electrification.Using satellite photos and GIS tools,an electrical distribution network is used to connect villages and properly located generation facilities together to reduce electrification cost.To design the network,optimal paths connecting all node-pairs are identified,followed by finding a network topology that minimizes cost.Earlier work has illustrated that A*(A-star,an optimal path-finding algorithm)is inefficient for this application due to the complex topography in rural areas.The multiplier-accelerated A*(MAA*)algorithm overcomes key performance issues,but,like A*,produces only one path connecting each node-pair.Relying on one path increases project risk because adverse conditions,such as inaccurate GIS estimation,unexpected soil conditions,land-rights disputes,political issues,etc.can occur during implementation.In this paper,a hybrid path-finding method combining genetic algorithm and A*/MAA*algorithm is proposed.The proposed method provides a family of near-optimal paths instead of a single optimal path for routing.A family of paths allows a project implementer to quickly adapt to unexpected situations as new information becomes available,and flexibly change network topology before or during implementation with minimal impact on project cost.

基于改进相似性的装备部件剩余寿命预测及经济性储备策略

针对传统基于相似性的剩余寿命(remaining useful lifetime,RUL)预测方法未考虑运行条件差异,从而影响预测准确性及部件储备策略科学性的问题,提出一种基于改进相似性的装备部件RUL预测及经济性储备策略。基于提出的改进相似性方法,区分装备部件的运行条件类别,通过各类别内服役部件和参考部件的性能状态相似性,预测服役部件的RUL;基于RUL预测结果,以装备部件维修储备总费用最低为目标,以资源利用率为约束,建立经济性储备策略决策模型;采用差分进化算法对模型寻优求解,得到最优装备部件储备策略。实例分析表明,所提方法能够有效提升RUL预测的准确性和部件储备策略的科学性,具备工程应用价值。

Best compromising crashworthiness design of automotive S-rail using TOPSIS and modified NSGAⅡ

In order to reduce both the weight of vehicles and the damage of occupants in a crash event simultaneously, it is necessary to perform a multi-objective optimal design of the automotive energy absorbing components. Modified non-dominated sorting genetic algorithm II(NSGA II) was used for multi-objective optimization of automotive S-rail considering absorbed energy(E), peak crushing force(Fmax) and mass of the structure(W) as three conflicting objective functions. In the multi-objective optimization problem(MOP), E and Fmax are defined by polynomial models extracted using the software GEvo M based on train and test data obtained from numerical simulation of quasi-static crushing of the S-rail using ABAQUS. Finally, the nearest to ideal point(NIP)method and technique for ordering preferences by similarity to ideal solution(TOPSIS) method are used to find the some trade-off optimum design points from all non-dominated optimum design points represented by the Pareto fronts. Results represent that the optimum design point obtained from TOPSIS method exhibits better trade-off in comparison with that of optimum design point obtained from NIP method.

Fault self-repair strategy based on evolvable hardware and reparation balance technology

In the face of harsh natural environment applications such as earth-orbiting and deep space satellites, underwater sea vehicles, strong electromagnetic interference and temperature stress,the circuits faults appear easily. Circuit faults will inevitably lead to serious losses of availability or impeded mission success without self-repair over the mission duration. Traditional fault-repair methods based on redundant fault-tolerant technique are straightforward to implement, yet their area, power and weight cost can be excessive. Moreover they utilize all plug-in or component level circuits to realize redundant backup, such that their applicability is limited. Hence, a novel selfrepair technology based on evolvable hardware（EHW） and reparation balance technology（RBT） is proposed. Its cost is low, and fault self-repair of various circuits and devices can be realized through dynamic configuration. Making full use of the fault signals, correcting circuit can be found through EHW technique to realize the balance and compensation of the fault output-signals. In this paper, the self-repair model was analyzed which based on EHW and RBT technique, the specific self-repair strategy was studied, the corresponding self-repair circuit fault system was designed, and the typical faults were simulated and analyzed which combined with the actual electronic devices. Simulation results demonstrated that the proposed fault self-repair strategy was feasible. Compared to traditional techniques, fault self-repair based on EHW consumes fewer hardware resources, and the scope of fault self-repair was expanded significantly.