In this paper, we propose a new adaptive control system design using internal model principle (IMP) for a bounded polynomial parameters. In this method, we regard timevarying parameters as variable disturbance and de...
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In this paper, we propose a new adaptive control system design using internal model principle (IMP) for a bounded polynomial parameters. In this method, we regard timevarying parameters as variable disturbance and design an estimating law used the internal model of the disturbance so that the law is able to rejected the effectness of the disturbance. Our method has the features that the tracking error can converge to sere. Furthermore, we give a sufficient condition for the stability based on a small-gain theorem. The condition shows that our proposed method relax the stability condition more than the conventional methods based on a passivity theorem. Finally, we contain a numerical simulation to show an effect of our system.
In this paper, a novel continuous sampled-data observer for lineartime-varyingsystems is presented. The proposed observer is based on two different impulsive observers. The state estimates of these impulsive observe...
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In this paper, a novel continuous sampled-data observer for lineartime-varyingsystems is presented. The proposed observer is based on two different impulsive observers. The state estimates of these impulsive observers are fused in a manner such that continuous state estimation is achieved. Another significant contribution is comprehensive stability analysis of the proposed observer. The analysis establishes conditions that guarantee exponential convergence of observers. Contrary to the common understanding, it is revealed that the convergence of associated discrete-time equation for impulsive observers is not a sufficient condition for overall convergence. Contributions are illustrated by simulations.
This study addresses the identification of linear time varying systems. The identification is based on the expansion of all time functions in the state equations by Haar wavelets. The unknown time function can thus be...
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This study addresses the identification of linear time varying systems. The identification is based on the expansion of all time functions in the state equations by Haar wavelets. The unknown time function can thus be identified in terms of Haar wavelets. A Haar wavelet is a set of complete, orthogonal basis and is easy to use computations. Several good properties of Haar wavelets are utilized in the algorithm. Both numerical and experimental results verify the analysis.
A new method to design observers for lineartime-varyingsystems is presented in this paper. The method begins by constructing two layers. The first layer is made up of multiple observers, while the second establishes...
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A new method to design observers for lineartime-varyingsystems is presented in this paper. The method begins by constructing two layers. The first layer is made up of multiple observers, while the second establishes a relationship between observers via a weighted estimation state. The primary challenge was to find a new feedback process that would determine the second layer weights. The multiple observers of the first layer were investigated to determine a general observation law. The resulting multilayer structure significantly improves the transient characteristics of the observation process, which leads to a more efficient control system.
In this paper, a new iterative learning control based on the double differential of the error is proposed for the linear time varying system having relative degree greater than one. The convergence criterion of the pr...
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In this paper, a new iterative learning control based on the double differential of the error is proposed for the linear time varying system having relative degree greater than one. The convergence criterion of the proposed method is proved. Furthermore, it is shown by simulations that convergence of error can be increased considerably by using our proposed controller as compared to the iterative learning controller using error or single differential of the error for the modification of the control input without increasing the learning gain.
For the purpose of recursive joint estimation of state and parameters in continuous-time state space systems, the algorithm proposed in this paper improves the consistency of a recently developed adaptive observer for...
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ISBN:
(纸本)0780370619
For the purpose of recursive joint estimation of state and parameters in continuous-time state space systems, the algorithm proposed in this paper improves the consistency of a recently developed adaptive observer for multi-input-multi-output (MIMO) lineartimevarying (LTV) systems. The new algorithm makes use of a timevarying gain matrix for parameter estimation, instead of the constant gain matrix used by the previously reported algorithm. It is exponentially stable, converges in the mean for both state and parameter estimations. The covariance matrix of the parameter estimation error can be made arbitrarily small by choosing a sufficiently small forgetting factor.
A method for sensor fault estimation in multiple-input multiple-output linear time varying systems is proposed in this paper. It is based on a new adaptive observer for joint estimation of states and sensor faults in ...
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A method for sensor fault estimation in multiple-input multiple-output linear time varying systems is proposed in this paper. It is based on a new adaptive observer for joint estimation of states and sensor faults in a state-space formulation of the monitored system. The exponential convergence of the algorithm is proved under some persistent excitation condition.
For joint state-parameter estimation in discrote time stochastic multiple-input multiple-output linear time varying systems, an efficient adaptive observer is proposed in this paper. In the noise-free case, the global...
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For joint state-parameter estimation in discrote time stochastic multiple-input multiple-output linear time varying systems, an efficient adaptive observer is proposed in this paper. In the noise-free case, the global exponential convergence of the adaptive observer is first established. It is then proved that, in the noise-corrupted case, the state and parameter estimation errors remain bounded if the noises are bounded, and moreover, the estimation errors converge in the mean to zero if the noises have zero means.
A new method for fault detection and isolation (FDI) in stochastic lineartimevarying (LTV) systems is proposed in this paper. It allows to completely isolate any number of faults regardless of the number of output s...
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A new method for fault detection and isolation (FDI) in stochastic lineartimevarying (LTV) systems is proposed in this paper. It allows to completely isolate any number of faults regardless of the number of output sensors, thanks to an appropriate assumption on the fault profiles and to some persistent excitation condition. In contrast, most existing methods enabling complete fault isolation have been developed for lineartime invariant (LTI) systems and require a strong condition on the number of sensors. The method proposed in this paper is based on a recent development for the design of adaptive observers. Its performance is illustrated by a numerical example.
A novel approach is developed to investigate xenon oscillations within a two-group two-region coupled core reactor model incorporating thermal feedback and poison effects. Group-wise neutronic coupling coefficients be...
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A novel approach is developed to investigate xenon oscillations within a two-group two-region coupled core reactor model incorporating thermal feedback and poison effects. Group-wise neutronic coupling coefficients between the core regions are calculated applying the associated fundamental and first mode eigenvalue separation values. The resultant nonlinear state space representation of the core behavior is quite suitable for evaluation of reactivity induced power transients such as load following operation. The model however comprises a multi-physics coupling of sub-systems with extremely distant relaxation times whose stiffness treatment inquire costly multistep implicit numerical methods. An adiabatic treatment of the sluggish poison dynamics is therefore proposed as a way out. The approach helps further investigate the nonlinearsystem within a lineartimevarying (LW) framework whereby a semi analytical framework is established. This scheme incorporates a matrix exponential analytical solution of the perturbed system as a quite efficient tool to study load following operation and control purposes. Poison dynamics are updated within larger intervals which exclude the need for specific numerical schemes of stiff systems. Simulation results of the axial offset conducted on a WER-1000 reactor at the beginning (BOC) and the end of cycle (EOC) display quite acceptable results compared with available benchmarks. The LTV reactor model is further investigated within a stability analysis of the associated timevaryingsystems at these two stages employing the concept of Lyapunov exponent (C) 2017 Elsevier Ltd. All rights reserved.
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