A neural-network-based adaptive gain scheduling backstepping sliding mode control（NNAGS-BSMC） approach for a class of uncertain strict-feedback nonlinear system is proposed.First, the control problem of uncertain st...A neural-network-based adaptive gain scheduling backstepping sliding mode control（NNAGS-BSMC） approach for a class of uncertain strict-feedback nonlinear system is proposed.First, the control problem of uncertain strict-feedback nonlinear systems is formulated. Second, the detailed design of NNAGSBSMC is described. The sliding mode control（SMC） law is designed to track a referenced output via backstepping technique.To decrease chattering result from SMC, a radial basis function neural network（RBFNN） is employed to construct the NNAGSBSMC to facilitate adaptive gain scheduling, in which the gains are scheduled adaptively via neural network（NN）, with sliding surface and its differential as NN inputs and the gains as NN outputs. Finally, the verification example is given to show the effectiveness and robustness of the proposed approach. Contrasting simulation results indicate that the NNAGS-BSMC decreases the chattering effectively and has better control performance against the BSMC.展开更多
DSM(SVM) is a memory model which was proposed at the 1980s'. DSM solved the problem thatscalability was contradict with easy programming and merge their merits. The efficiency of DSM is still aproblem. The propose...DSM(SVM) is a memory model which was proposed at the 1980s'. DSM solved the problem thatscalability was contradict with easy programming and merge their merits. The efficiency of DSM is still aproblem. The proposed solutions include prefetching and adopting new coherence model, but neither ofthem is a perfect solution. Prefetch will be less efficient when there are too many false sharing. The weak, relaxed coherence model is quite different from sequential coherence which was assumed by mostprogrammers intuitively, so programming and debugging becomes very difficult. We have proposed StrictWeak Hybrid Coherence (SWHC) model which can eliminate false sharing and provide more temporal andspatial locality, we also provide a scheme to keep sequential coherent. But SWHC model is complicate, asimplified model for Pthread share memory parallel computing model is proposed in the paper.展开更多
基金supported by the National Natural Science Foundation of China(11502288)the Natural Science Foundation of Hunan Province(2016JJ3019)+1 种基金the Aeronautical Science Foundation of China(2017ZA88001)the Scientific Research Project of National University of Defense Technology(ZK17-03-32)
文摘A neural-network-based adaptive gain scheduling backstepping sliding mode control（NNAGS-BSMC） approach for a class of uncertain strict-feedback nonlinear system is proposed.First, the control problem of uncertain strict-feedback nonlinear systems is formulated. Second, the detailed design of NNAGSBSMC is described. The sliding mode control（SMC） law is designed to track a referenced output via backstepping technique.To decrease chattering result from SMC, a radial basis function neural network（RBFNN） is employed to construct the NNAGSBSMC to facilitate adaptive gain scheduling, in which the gains are scheduled adaptively via neural network（NN）, with sliding surface and its differential as NN inputs and the gains as NN outputs. Finally, the verification example is given to show the effectiveness and robustness of the proposed approach. Contrasting simulation results indicate that the NNAGS-BSMC decreases the chattering effectively and has better control performance against the BSMC.
文摘DSM(SVM) is a memory model which was proposed at the 1980s'. DSM solved the problem thatscalability was contradict with easy programming and merge their merits. The efficiency of DSM is still aproblem. The proposed solutions include prefetching and adopting new coherence model, but neither ofthem is a perfect solution. Prefetch will be less efficient when there are too many false sharing. The weak, relaxed coherence model is quite different from sequential coherence which was assumed by mostprogrammers intuitively, so programming and debugging becomes very difficult. We have proposed StrictWeak Hybrid Coherence (SWHC) model which can eliminate false sharing and provide more temporal andspatial locality, we also provide a scheme to keep sequential coherent. But SWHC model is complicate, asimplified model for Pthread share memory parallel computing model is proposed in the paper.
