A kind of adaptive sliding model control algorithm is developed to solve and improve the mathematical model dependency and un-modeled dynamics of a controlled system. The control strategy derived from a kind of data-d...A kind of adaptive sliding model control algorithm is developed to solve and improve the mathematical model dependency and un-modeled dynamics of a controlled system. The control strategy derived from a kind of data-driven control method in essence, thereby the input and output data are utilized by the controller with no information about the control system model. Theoretical analysis proves that this proposed control algorithm can improve the utilization of the estimated pseudo partial derivative information and accelerate the velocity of the convergence. The stability of the control system is further verified by rigorous mathematical analysis. This new discrete-time nonlinear systems model-free control algorithm obtained better control performance through the simulations for the linear motor position and the information tracking speed, which also achieved robust and accurate traceability.展开更多
A prediction framework based on the evolution of pattern motion probability density is proposed for the output prediction and estimation problem of non-Newtonian mechanical systems,assuming that the system satisfies t...A prediction framework based on the evolution of pattern motion probability density is proposed for the output prediction and estimation problem of non-Newtonian mechanical systems,assuming that the system satisfies the generalized Lipschitz condition.As a complex nonlinear system primarily governed by statistical laws rather than Newtonian mechanics,the output of non-Newtonian mechanics systems is difficult to describe through deterministic variables such as state variables,which poses difficulties in predicting and estimating the system’s output.In this article,the temporal variation of the system is described by constructing pattern category variables,which are non-deterministic variables.Since pattern category variables have statistical attributes but not operational attributes,operational attributes are assigned to them by posterior probability density,and a method for analyzing their motion laws using probability density evolution is proposed.Furthermore,a data-driven form of pattern motion probabilistic density evolution prediction method is designed by combining pseudo partial derivative(PPD),achieving prediction of the probability density satisfying the system’s output uncertainty.Based on this,the final prediction estimation of the system’s output value is realized by minimum variance unbiased estimation.Finally,a corresponding PPD estimation algorithm is designed using an extended state observer(ESO)to estimate the parameters to be estimated in the proposed prediction method.The effectiveness of the parameter estimation algorithm and prediction method is demonstrated through theoretical analysis,and the accuracy of the algorithm is verified by two numerical simulation examples.展开更多
In this paper,an improved high-order model-free adaptive iterative control(IHOMFAILC)method for a class of nonlinear discrete-time systems is proposed based on the compact format dynamic linearization method.This meth...In this paper,an improved high-order model-free adaptive iterative control(IHOMFAILC)method for a class of nonlinear discrete-time systems is proposed based on the compact format dynamic linearization method.This method adds the differential of tracking error in the criteria function to compensate for the effect of the random disturbance.Meanwhile,a high-order estimation algorithmis used to estimate the value of pseudo partial derivative(PPD),that is,the current value of PPD is updated by that of previous iterations.Thus the rapid convergence of the maximumtracking error is not limited by the initial value of PPD.The convergence of the maximumtracking error is deduced in detail.This method can track the desired output with enhanced convergence and improved tracking performance.Two examples are used to verify the convergence and effectiveness of the proposed method.展开更多
基金supported by Key Programs for Science and Technology Development of Henan Province(No.102102210197)the Opening Project of Key Laboratory of Mine Informatization,Henan Polytechnic University and the Doctoral Foundation of Henan Polytechnic University(No.B2010-23)
文摘A kind of adaptive sliding model control algorithm is developed to solve and improve the mathematical model dependency and un-modeled dynamics of a controlled system. The control strategy derived from a kind of data-driven control method in essence, thereby the input and output data are utilized by the controller with no information about the control system model. Theoretical analysis proves that this proposed control algorithm can improve the utilization of the estimated pseudo partial derivative information and accelerate the velocity of the convergence. The stability of the control system is further verified by rigorous mathematical analysis. This new discrete-time nonlinear systems model-free control algorithm obtained better control performance through the simulations for the linear motor position and the information tracking speed, which also achieved robust and accurate traceability.
文摘A prediction framework based on the evolution of pattern motion probability density is proposed for the output prediction and estimation problem of non-Newtonian mechanical systems,assuming that the system satisfies the generalized Lipschitz condition.As a complex nonlinear system primarily governed by statistical laws rather than Newtonian mechanics,the output of non-Newtonian mechanics systems is difficult to describe through deterministic variables such as state variables,which poses difficulties in predicting and estimating the system’s output.In this article,the temporal variation of the system is described by constructing pattern category variables,which are non-deterministic variables.Since pattern category variables have statistical attributes but not operational attributes,operational attributes are assigned to them by posterior probability density,and a method for analyzing their motion laws using probability density evolution is proposed.Furthermore,a data-driven form of pattern motion probabilistic density evolution prediction method is designed by combining pseudo partial derivative(PPD),achieving prediction of the probability density satisfying the system’s output uncertainty.Based on this,the final prediction estimation of the system’s output value is realized by minimum variance unbiased estimation.Finally,a corresponding PPD estimation algorithm is designed using an extended state observer(ESO)to estimate the parameters to be estimated in the proposed prediction method.The effectiveness of the parameter estimation algorithm and prediction method is demonstrated through theoretical analysis,and the accuracy of the algorithm is verified by two numerical simulation examples.
文摘In this paper,an improved high-order model-free adaptive iterative control(IHOMFAILC)method for a class of nonlinear discrete-time systems is proposed based on the compact format dynamic linearization method.This method adds the differential of tracking error in the criteria function to compensate for the effect of the random disturbance.Meanwhile,a high-order estimation algorithmis used to estimate the value of pseudo partial derivative(PPD),that is,the current value of PPD is updated by that of previous iterations.Thus the rapid convergence of the maximumtracking error is not limited by the initial value of PPD.The convergence of the maximumtracking error is deduced in detail.This method can track the desired output with enhanced convergence and improved tracking performance.Two examples are used to verify the convergence and effectiveness of the proposed method.
