Automated production systems typically comprise numerous electrical servo drives,many of which conduct positioning motions,e.g.for handling or manipulation tasks.The power electronics of modern multi-axis systems ofte...Automated production systems typically comprise numerous electrical servo drives,many of which conduct positioning motions,e.g.for handling or manipulation tasks.The power electronics of modern multi-axis systems often comprise coupled DC-links,enabling for internal exchange of recuperative brake energy.However,the motion sequences of manipulators are often commanded at maximum dynamics for minimum time motion,neglecting possible optimization potential,e.g.available idle time,leading to inefficient energy management.A robust trajectory optimization approach based on the particle swarm algorithm and well-established path planning methods is presented for the adaption of multi-axis positioning tasks with only two parameters per axis and positioning motion during system run-time.Experimental results prove that,depending on the positioning task and chosen optimization constraints,energy demands are distinctly reduced.The approach is applicable to diverse multi-axis configurations and enables for considerable energy savings without additional hardware invest.展开更多
基金Supported by the German Research Foundation(DFG)[grant number OR196/4-2].
文摘Automated production systems typically comprise numerous electrical servo drives,many of which conduct positioning motions,e.g.for handling or manipulation tasks.The power electronics of modern multi-axis systems often comprise coupled DC-links,enabling for internal exchange of recuperative brake energy.However,the motion sequences of manipulators are often commanded at maximum dynamics for minimum time motion,neglecting possible optimization potential,e.g.available idle time,leading to inefficient energy management.A robust trajectory optimization approach based on the particle swarm algorithm and well-established path planning methods is presented for the adaption of multi-axis positioning tasks with only two parameters per axis and positioning motion during system run-time.Experimental results prove that,depending on the positioning task and chosen optimization constraints,energy demands are distinctly reduced.The approach is applicable to diverse multi-axis configurations and enables for considerable energy savings without additional hardware invest.
