Considering the dynamic positioning(DP) problem of a surface ship with unknownmodel dynamic parameters and unknown environmental disturbances,this paper develops a data-mining-based fuzzy control *** the certain oper...
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Considering the dynamic positioning(DP) problem of a surface ship with unknownmodel dynamic parameters and unknown environmental disturbances,this paper develops a data-mining-based fuzzy control *** the certain operating and environmental conditions of a ship,the scientific and effective fuzzy rules for the DP of ships are extracted using the data-mining *** on the extracted fuzzy rules,the DP fuzzy control law is ***,the simulation studies on a supply ship Northern Clipper are carried out to verify the effectiveness of the designed DP fuzzy control law.
For the autoregressive(AR) signals with multisensor,unknown model parameters and unknown noise variances,using the recursive extended least square(RELS) and the correlation method,the strong consistent information fus...
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For the autoregressive(AR) signals with multisensor,unknown model parameters and unknown noise variances,using the recursive extended least square(RELS) and the correlation method,the strong consistent information fusion estimators of modelparameters and noise variances are presented,and then by substituting them into the optimal weighted measurement fusion Wiener filter based on the autoregressive moving average(ARMA) innovation model,a self-tuning weighted measurement fusion Wiener signal filter is ***,applying the dynamic error system analysis(DESA) method,it is proved that the self-tuning fused Wiener filter converges to the optimal fused Wiener filter in a realization,so that it has asymptotically global optimality.A simulation example shows its effectiveness.
For the autoregressive (AR) signals with multisensor, unknown model parameters and unknown noise variances, using the recursive extended least square (RELS) and the correlation method, the strong consistent informatio...
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ISBN:
(纸本)9781424451814
For the autoregressive (AR) signals with multisensor, unknown model parameters and unknown noise variances, using the recursive extended least square (RELS) and the correlation method, the strong consistent information fusion estimators of modelparameters and noise variances are presented, and then by substituting them into the optimal weighted measurement fusion Wiener filter based on the autoregressive moving average (ARMA) innovation model, a self-tuning weighted measurement fusion Wiener signal filter is presented. Further, applying the dynamic error system analysis (DESA) method, it is proved that the self-tuning fused Wiener filter converges to the optimal fused Wiener filter in a realization, so that it has asymptotically global optimality. A simulation example shows its effectiveness.
For the multisensor single channel autoregressive moving average（ARMA）signals with unknown model parameters and noise variances,using the recursive instrumental variable（RIV）and the correlated method,the strong co...
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For the multisensor single channel autoregressive moving average（ARMA）signals with unknown model parameters and noise variances,using the recursive instrumental variable（RIV）and the correlated method,the strong consistent information fusion estimators of modelparameters and noise variances are presented,and then substituting them into the optimal weighted measurement fusion Wiener signal filter,a self-tuning weighted measurement fusion Wiener signal filter is ***,applying the dynamic error system analysis（DESA）method,it is rigorously proved that the self-tuning fused Wiener filter converges to the optimal fused Wiener filter in a realization,so that it has asymptotically global optimality.A simulation example shows its effectiveness.
In this paper,a new self adaptive iterative learning PD control(ILC-PD) scheme is proposed for trajectory tracking of robot manipulators with unknownparameters and performing repetitive *** proposed control scheme is...
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In this paper,a new self adaptive iterative learning PD control(ILC-PD) scheme is proposed for trajectory tracking of robot manipulators with unknownparameters and performing repetitive *** proposed control scheme is based upon a proportional-derivative(PD) feedback structure, for which an iterative term is added to cope with the unknown model parameters and *** contrast to classical iterative learning schemes,ILC-PD method is very simple in the sense that the only requirement on the PD and learning gains are just two iterative variables and the bounds of the robot parameters are not required,which is an interesting fact from a practical point of ***,the ILC-PD method possesses both adaptive and learning capabilities with a simple control structure,and the asymptotical convergence is guaranteed based on the Lyapunov *** simulations are presented for a planner manipulator with two revolute degrees of *** results are provided to illustrate the effectiveness of the proposed controllers.
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