In this paper, an adaptive neural networks(NNs)tracking controller is proposed for a class of single-input/singleoutput(SISO) non-affine pure-feedback non-linear systems with input saturation. In the proposed approach...In this paper, an adaptive neural networks(NNs)tracking controller is proposed for a class of single-input/singleoutput(SISO) non-affine pure-feedback non-linear systems with input saturation. In the proposed approach, the original input saturated nonlinear system is augmented by a low pass filter.Then, new system states are introduced to implement states transformation of the augmented model. The resulting new model in affine Brunovsky form permits direct and simpler controller design by avoiding back-stepping technique and its complexity growing as done in existing methods in the literature.In controller design of the proposed approach, a state observer,based on the strictly positive real(SPR) theory, is introduced and designed to estimate the new system states, and only two neural networks are used to approximate the uncertain nonlinearities and compensate for the saturation nonlinearity of actuator. The proposed approach can not only provide a simple and effective way for construction of the controller in adaptive neural networks control of non-affine systems with input saturation, but also guarantee the tracking performance and the boundedness of all the signals in the closed-loop system. The stability of the control system is investigated by using the Lyapunov theory. Simulation examples are presented to show the effectiveness of the proposed controller.展开更多
The paper deals with the problem of switched dynamical systems modeling especially in DC-DC converters case study consideration.It presents two approaches to describe accurately the behavior of this class of systems.T...The paper deals with the problem of switched dynamical systems modeling especially in DC-DC converters case study consideration.It presents two approaches to describe accurately the behavior of this class of systems.To clarify the paper's contribution,the proposed approaches are validated through simulations and experimental results.A comparative study,between the obtained results and those of other techniques from the literature,is given to evaluate the performances of the studied approaches.展开更多
This paper introduces a recursive identification methods toolbox(called RIM) running under Matlab environment for dynamic system identification from available data. The RIM includes many methods which are generally us...This paper introduces a recursive identification methods toolbox(called RIM) running under Matlab environment for dynamic system identification from available data. The RIM includes many methods which are generally used. The RIM helps users to validate the theoretical results and to carry out comparison between identifications methods without the need of algorithms programming. Furthermore, the RIM can be used as an education platform to study the identification parameters effect on model validity and results accuracy. To show its performance and capability, the RIM is evaluated through many application examples.展开更多
文摘In this paper, an adaptive neural networks(NNs)tracking controller is proposed for a class of single-input/singleoutput(SISO) non-affine pure-feedback non-linear systems with input saturation. In the proposed approach, the original input saturated nonlinear system is augmented by a low pass filter.Then, new system states are introduced to implement states transformation of the augmented model. The resulting new model in affine Brunovsky form permits direct and simpler controller design by avoiding back-stepping technique and its complexity growing as done in existing methods in the literature.In controller design of the proposed approach, a state observer,based on the strictly positive real(SPR) theory, is introduced and designed to estimate the new system states, and only two neural networks are used to approximate the uncertain nonlinearities and compensate for the saturation nonlinearity of actuator. The proposed approach can not only provide a simple and effective way for construction of the controller in adaptive neural networks control of non-affine systems with input saturation, but also guarantee the tracking performance and the boundedness of all the signals in the closed-loop system. The stability of the control system is investigated by using the Lyapunov theory. Simulation examples are presented to show the effectiveness of the proposed controller.
文摘The paper deals with the problem of switched dynamical systems modeling especially in DC-DC converters case study consideration.It presents two approaches to describe accurately the behavior of this class of systems.To clarify the paper's contribution,the proposed approaches are validated through simulations and experimental results.A comparative study,between the obtained results and those of other techniques from the literature,is given to evaluate the performances of the studied approaches.
文摘This paper introduces a recursive identification methods toolbox(called RIM) running under Matlab environment for dynamic system identification from available data. The RIM includes many methods which are generally used. The RIM helps users to validate the theoretical results and to carry out comparison between identifications methods without the need of algorithms programming. Furthermore, the RIM can be used as an education platform to study the identification parameters effect on model validity and results accuracy. To show its performance and capability, the RIM is evaluated through many application examples.
