The classical D-type iterative learning control law depends crucially on the relative degreeof the controlled system, high order di?erential iterative learning law must be taken for systems withhigh order relative deg...The classical D-type iterative learning control law depends crucially on the relative degreeof the controlled system, high order di?erential iterative learning law must be taken for systems withhigh order relative degree. It is very di?cult to ascertain the relative degree of the controlled systemfor uncertain nonlinear systems. A first-order D-type iterative learning control design method ispresented for a class of nonlinear systems with unknown relative degree based on dummy model inthis paper. A dummy model with relative degree 1 is constructed for a class of nonlinear systemswith unknown relative degree. A first-order D-type iterative learning control law is designed basedon the dummy model, so that the dummy model can track the desired trajectory perfectly, and thecontrolled system can track the desired trajectory within a certain error. The simulation exampledemonstrates the feasibility and e?ectiveness of the presented method.展开更多
A robust repetitive control scheme is used to improve the rate smoothness of a brushless DC motor (BLDCM) driven test turntable. The method synthesizes variable structure control (VSC) laws and repetitive control (RC)...A robust repetitive control scheme is used to improve the rate smoothness of a brushless DC motor (BLDCM) driven test turntable. The method synthesizes variable structure control (VSC) laws and repetitive control (RC) laws in a complementary manner. The VSC strategy can stabilize the system and suppress uncertainties, such as the aperiodic disturbance and noises, while RC strategy can eliminate the periodic rate fluctuation in a steady state. The convergence of the repetitive learning process is also guaranteed by VSC. A general nonlinear system model is discussed. The model can be considered as an extension of BLDCMs. The stability and asymptotic position tracking performance are validated by using Lyapunov functions. Simulation results show the effectiveness of the proposed approach for improving the rate smoothness.展开更多
This paper presents an approach that is useful for the identification of a fuzzy model in SISO system. The initial values of cluster centers are identified by the Hough transformation, which considers the linearity an...This paper presents an approach that is useful for the identification of a fuzzy model in SISO system. The initial values of cluster centers are identified by the Hough transformation, which considers the linearity and continuity of given input-output data, respectively. For the premise parts parameters identification, we use fuzzy-C-means clustering method. The consequent parameters are identified based on recursive least square. This method not only makes approximation more accurate, but also let computation be simpler and the procedure is realized more easily. Finally, it is shown that this method is useful for the identification of a fuzzy model by simulation.展开更多
We consider the robust H 2/H ∞ filtering problem for linear perturbed systems with steadystate error variance assignment. The generalized inverse technique of matrix is introduced, and a new algorithm is developed....We consider the robust H 2/H ∞ filtering problem for linear perturbed systems with steadystate error variance assignment. The generalized inverse technique of matrix is introduced, and a new algorithm is developed. After two Riccati equations are solved, the filter can be obtained directly, and the following three performance requirements are simultaneously satisfied: The filtering process is asymptotically stable; the steadystate variance of the estimation error of each state is not more than the individual prespecified upper bound; the transfer function from exogenous noise inputs to error state outputs meets the prespecified H ∞ norm upper bound constraint. A numerical example is provided to demonstrate the flexibility of the proposed design approach.展开更多
文摘The classical D-type iterative learning control law depends crucially on the relative degreeof the controlled system, high order di?erential iterative learning law must be taken for systems withhigh order relative degree. It is very di?cult to ascertain the relative degree of the controlled systemfor uncertain nonlinear systems. A first-order D-type iterative learning control design method ispresented for a class of nonlinear systems with unknown relative degree based on dummy model inthis paper. A dummy model with relative degree 1 is constructed for a class of nonlinear systemswith unknown relative degree. A first-order D-type iterative learning control law is designed basedon the dummy model, so that the dummy model can track the desired trajectory perfectly, and thecontrolled system can track the desired trajectory within a certain error. The simulation exampledemonstrates the feasibility and e?ectiveness of the presented method.
文摘A robust repetitive control scheme is used to improve the rate smoothness of a brushless DC motor (BLDCM) driven test turntable. The method synthesizes variable structure control (VSC) laws and repetitive control (RC) laws in a complementary manner. The VSC strategy can stabilize the system and suppress uncertainties, such as the aperiodic disturbance and noises, while RC strategy can eliminate the periodic rate fluctuation in a steady state. The convergence of the repetitive learning process is also guaranteed by VSC. A general nonlinear system model is discussed. The model can be considered as an extension of BLDCMs. The stability and asymptotic position tracking performance are validated by using Lyapunov functions. Simulation results show the effectiveness of the proposed approach for improving the rate smoothness.
基金This project was supported by the Natural Science Foundation of Heilongjiang province and Doctor Foundation of Yanshan U-niversity.
文摘This paper presents an approach that is useful for the identification of a fuzzy model in SISO system. The initial values of cluster centers are identified by the Hough transformation, which considers the linearity and continuity of given input-output data, respectively. For the premise parts parameters identification, we use fuzzy-C-means clustering method. The consequent parameters are identified based on recursive least square. This method not only makes approximation more accurate, but also let computation be simpler and the procedure is realized more easily. Finally, it is shown that this method is useful for the identification of a fuzzy model by simulation.
文摘We consider the robust H 2/H ∞ filtering problem for linear perturbed systems with steadystate error variance assignment. The generalized inverse technique of matrix is introduced, and a new algorithm is developed. After two Riccati equations are solved, the filter can be obtained directly, and the following three performance requirements are simultaneously satisfied: The filtering process is asymptotically stable; the steadystate variance of the estimation error of each state is not more than the individual prespecified upper bound; the transfer function from exogenous noise inputs to error state outputs meets the prespecified H ∞ norm upper bound constraint. A numerical example is provided to demonstrate the flexibility of the proposed design approach.
