针对开关磁阻电机(switched reluctance motor,SRM)传统滑模控制方法响应速度慢、抖振大且鲁棒性差的问题,该文提出一种基于双滑模控制器的开关磁阻电机调速策略。首先,设计全局积分滑模速度控制器(global integral sliding model speed...针对开关磁阻电机(switched reluctance motor,SRM)传统滑模控制方法响应速度慢、抖振大且鲁棒性差的问题,该文提出一种基于双滑模控制器的开关磁阻电机调速策略。首先,设计全局积分滑模速度控制器(global integral sliding model speed controller,GISMSC),消除系统到达滑模面的过程,提高响应速度和鲁棒性,并通过改进趋近律来减小滑模抖振;其次,设计扰动滑模观测器(disturbance sliding mode observer,DSMO),对负载和未知扰动进行观测,并前馈补偿至全局积分滑模速度控制器中,进而复合构成双滑模速度控制器,并将其作为速度外环与模型预测控制(model predictive control,MPC)相结合,减小转矩脉动的同时提升其调速性能;最后,仿真和实验考虑到转速和负载突变以及电机参数失配等情况,结果表明,所提方法不仅提高了系统调速性能,减小了转矩脉动,而且克服了电机内部参数变化和外部扰动的影响,使系统具备更强鲁棒性。展开更多
Deep neural networks(DNN)are widely used in image recognition,image classification,and other fields.However,as the model size increases,the DNN hardware accelerators face the challenge of higher area overhead and ener...Deep neural networks(DNN)are widely used in image recognition,image classification,and other fields.However,as the model size increases,the DNN hardware accelerators face the challenge of higher area overhead and energy consumption.In recent years,stochastic computing(SC)has been considered a way to realize deep neural networks and reduce hardware consumption.A probabilistic compensation algorithm is proposed to solve the accuracy problem of stochastic calculation,and a fully parallel neural network accelerator based on a deterministic method is designed.The software simulation results show that the accuracy of the probability compensation algorithm on the CIFAR-10 data set is 95.32%,which is 14.98%higher than that of the traditional SC algorithm.The accuracy of the deterministic algorithm on the CIFAR-10 dataset is 95.06%,which is 14.72%higher than that of the traditional SC algorithm.The results of Very Large Scale Integration Circuit(VLSI)hardware tests show that the normalized energy efficiency of the fully parallel neural network accelerator based on the deterministic method is improved by 31%compared with the circuit based on binary computing.展开更多
文摘Deep neural networks(DNN)are widely used in image recognition,image classification,and other fields.However,as the model size increases,the DNN hardware accelerators face the challenge of higher area overhead and energy consumption.In recent years,stochastic computing(SC)has been considered a way to realize deep neural networks and reduce hardware consumption.A probabilistic compensation algorithm is proposed to solve the accuracy problem of stochastic calculation,and a fully parallel neural network accelerator based on a deterministic method is designed.The software simulation results show that the accuracy of the probability compensation algorithm on the CIFAR-10 data set is 95.32%,which is 14.98%higher than that of the traditional SC algorithm.The accuracy of the deterministic algorithm on the CIFAR-10 dataset is 95.06%,which is 14.72%higher than that of the traditional SC algorithm.The results of Very Large Scale Integration Circuit(VLSI)hardware tests show that the normalized energy efficiency of the fully parallel neural network accelerator based on the deterministic method is improved by 31%compared with the circuit based on binary computing.
