Recently, the conditions of observers that can estimate directly Kx（t） for an arbitrarily given K, and that are free of the effect of time delayed states of the system, are formulated. This paper points out that, be...Recently, the conditions of observers that can estimate directly Kx（t） for an arbitrarily given K, and that are free of the effect of time delayed states of the system, are formulated. This paper points out that, because of the equivalence in formulation, the existing conditions for unknown input observers can be used to establish directly a new set of sufficient conditions for that recent observer. This new set of sufficient conditions is much simpler, and therefore much more useful and significant, than the sufficient conditions derived in that recent paper. This new set of sufficient conditions also reveals some basic mistakes of that recent paper. In addition, this paper reveals the severe restrictiveness of this new set of conditions and proposes a fundamentally new observer design formulation that can relax that set of conditions significantly.展开更多
Presents a systematic design method of reduced order dynamical compensator via the parametric representations of eigenstructure assignment for linear system, which provides maximum degree of freedom, and can be easily...Presents a systematic design method of reduced order dynamical compensator via the parametric representations of eigenstructure assignment for linear system, which provides maximum degree of freedom, and can be easily used for the design of a linear system with unknown inputs under some conditions. Even when these conditions are not satisfied, the lower order dynamical compensator can also be designed under some relaxed conditions. Some examples illustrate that the method is neat, simple and effective.展开更多
This paper investigates the problem of state estimation for discrete-time stochastic linear systems, where additional knowledge on the unknown inputs is available at an aggregate level and the knowledge on the missing...This paper investigates the problem of state estimation for discrete-time stochastic linear systems, where additional knowledge on the unknown inputs is available at an aggregate level and the knowledge on the missing measurements can be described by a known stochastic distribution. Firstly, the available knowledge on the unknown inputs and the state equation is used to form the prior distribution of the state vector at each time step. Secondly, to obtain an analytically tractable likelihood function, the effect of missing measurements is broken down into a systematic part and a random part, and the latter is modeled as part of the observation noise. Then, a recursive filter is obtained based on Bayesian inference. Finally, a numerical example is provided to evaluate the performance of the proposed methods.展开更多
This paper deals with the simultaneous estimation of states and unknown inputs for a class of Lipschitz nonlinear systems using only the measured outputs. The system is assumed to have bounded uncertainties that appea...This paper deals with the simultaneous estimation of states and unknown inputs for a class of Lipschitz nonlinear systems using only the measured outputs. The system is assumed to have bounded uncertainties that appear on both the state and output matrices. The observer design problem is formulated as a set of linear constraints which can be easily solved using linear matrix inequalities (LMI) technique. An application based on manipulator arm actuated by a direct current (DC) motor is presented to evaluate the performance of the proposed observer. The observer is applied to estimate both state and faults.展开更多
文摘Recently, the conditions of observers that can estimate directly Kx（t） for an arbitrarily given K, and that are free of the effect of time delayed states of the system, are formulated. This paper points out that, because of the equivalence in formulation, the existing conditions for unknown input observers can be used to establish directly a new set of sufficient conditions for that recent observer. This new set of sufficient conditions is much simpler, and therefore much more useful and significant, than the sufficient conditions derived in that recent paper. This new set of sufficient conditions also reveals some basic mistakes of that recent paper. In addition, this paper reveals the severe restrictiveness of this new set of conditions and proposes a fundamentally new observer design formulation that can relax that set of conditions significantly.
文摘Presents a systematic design method of reduced order dynamical compensator via the parametric representations of eigenstructure assignment for linear system, which provides maximum degree of freedom, and can be easily used for the design of a linear system with unknown inputs under some conditions. Even when these conditions are not satisfied, the lower order dynamical compensator can also be designed under some relaxed conditions. Some examples illustrate that the method is neat, simple and effective.
基金jointly funded by UK Engineering and Physical Sciences Research Council(EPSRC)and BAE System(EP/H501401/1)
文摘This paper investigates the problem of state estimation for discrete-time stochastic linear systems, where additional knowledge on the unknown inputs is available at an aggregate level and the knowledge on the missing measurements can be described by a known stochastic distribution. Firstly, the available knowledge on the unknown inputs and the state equation is used to form the prior distribution of the state vector at each time step. Secondly, to obtain an analytically tractable likelihood function, the effect of missing measurements is broken down into a systematic part and a random part, and the latter is modeled as part of the observation noise. Then, a recursive filter is obtained based on Bayesian inference. Finally, a numerical example is provided to evaluate the performance of the proposed methods.
文摘This paper deals with the simultaneous estimation of states and unknown inputs for a class of Lipschitz nonlinear systems using only the measured outputs. The system is assumed to have bounded uncertainties that appear on both the state and output matrices. The observer design problem is formulated as a set of linear constraints which can be easily solved using linear matrix inequalities (LMI) technique. An application based on manipulator arm actuated by a direct current (DC) motor is presented to evaluate the performance of the proposed observer. The observer is applied to estimate both state and faults.
