采用基于模糊逻辑推理的半主动控制技术对风机塔筒进行风致振动控制,通过对塔筒结构实时的动力响应进行模糊逻辑推理,利用半主动控制算法调节调频质量阻尼器(Tuned Mass Damper,TMD)的阻尼系数,输出不同的阻尼力,对塔筒结构进行振动控...采用基于模糊逻辑推理的半主动控制技术对风机塔筒进行风致振动控制,通过对塔筒结构实时的动力响应进行模糊逻辑推理,利用半主动控制算法调节调频质量阻尼器(Tuned Mass Damper,TMD)的阻尼系数,输出不同的阻尼力,对塔筒结构进行振动控制。通过Simulink软件进行半主动模糊控制系统仿真,结果表明,基于模糊逻辑推理的半主动控制比传统被动控制的控制效果更好,可以大幅降低塔筒结构顶端的位移响应。展开更多
In design optimization of crane metal structures, present approaches are based on simple models and mixed variables, which are difficult to use in practice and usually lead to failure of optimized results for rounding...In design optimization of crane metal structures, present approaches are based on simple models and mixed variables, which are difficult to use in practice and usually lead to failure of optimized results for rounding variables. Crane metal structure optimal design（CMSOD） belongs to a constrained nonlinear optimization problem with discrete variables. A novel algorithm combining ant colony algorithm with a mutation-based local search（ACAM） is developed and used for a real CMSOD for the first time. In the algorithm model, the encoded mode of continuous array elements is introduced. This not only avoids the need to round optimization design variables during mixed variable optimization, but also facilitates the construction of heuristic information, and the storage and update of the ant colony pheromone. Together with the proposed ACAM, a genetic algorithm（GA） and particle swarm optimization（PSO） are used to optimize the metal structure of a crane. The optimization results show that the convergence speed of ACAM is approximately 20% of that of the GA and around 11% of that of the PSO. The objective function value given by ACAM is 22.23% less than the practical design value, a reduction of 16.42% over the GA and 3.27% over the PSO. The developed ACAM is an effective intelligent method for CMSOD and superior to other methods.展开更多
文摘采用基于模糊逻辑推理的半主动控制技术对风机塔筒进行风致振动控制,通过对塔筒结构实时的动力响应进行模糊逻辑推理,利用半主动控制算法调节调频质量阻尼器(Tuned Mass Damper,TMD)的阻尼系数,输出不同的阻尼力,对塔筒结构进行振动控制。通过Simulink软件进行半主动模糊控制系统仿真,结果表明,基于模糊逻辑推理的半主动控制比传统被动控制的控制效果更好,可以大幅降低塔筒结构顶端的位移响应。
基金Supported by National Natural Science Foundation of China(Grant No.51275329)the Youth Fund Program of Taiyuan University of Science and Technology,China(Grant No.20113014)
文摘In design optimization of crane metal structures, present approaches are based on simple models and mixed variables, which are difficult to use in practice and usually lead to failure of optimized results for rounding variables. Crane metal structure optimal design（CMSOD） belongs to a constrained nonlinear optimization problem with discrete variables. A novel algorithm combining ant colony algorithm with a mutation-based local search（ACAM） is developed and used for a real CMSOD for the first time. In the algorithm model, the encoded mode of continuous array elements is introduced. This not only avoids the need to round optimization design variables during mixed variable optimization, but also facilitates the construction of heuristic information, and the storage and update of the ant colony pheromone. Together with the proposed ACAM, a genetic algorithm（GA） and particle swarm optimization（PSO） are used to optimize the metal structure of a crane. The optimization results show that the convergence speed of ACAM is approximately 20% of that of the GA and around 11% of that of the PSO. The objective function value given by ACAM is 22.23% less than the practical design value, a reduction of 16.42% over the GA and 3.27% over the PSO. The developed ACAM is an effective intelligent method for CMSOD and superior to other methods.
