Showing papers on "Observer (quantum physics) published in 2005"

Second-order sliding-mode observer for mechanical systems

Abstract: The super-twisting second-order sliding-mode algorithm is modified in order to design a velocity observer for uncertain mechanical systems. The finite time convergence of the observer is proved. Thus, the observer can be designed independently of the controller. A discrete version of the observer is considered and the corresponding accuracy is estimated.

Disturbance attenuation and rejection for systems with nonlinearity via DOBC approach

Abstract: In this paper the disturbance attenuation and rejection problem is investigated for a class of MIMO nonlinear systems in the disturbance-observer-based control (DOBC) framework. The unknown external disturbances are supposed to be generated by an exogenous system, where some classic assumptions on disturbances can be removed. Two kinds of nonlinear dynamics in the plants are considered, respectively, which correspond to the known and unknown functions. Design schemes are presented for both the full-order and reduced-order disturbance observers via LMI-based algorithms. For the plants with known nonlinearity, it is shown that the full-order observer can be constructed by augmenting the estimation of disturbances into the full-state estimation, and the reduced-order ones can be designed by using of the separation principle. For the uncertain nonlinearity, the problem can be reduced to a robust observer design problem. By integrating the disturbance observers with conventional control laws, the disturbances can be rejected and the desired dynamic performances can be guaranteed. If the disturbance also has perturbations, it is shown that the proposed approaches are infeasible and further research is required in the future. Finally, simulations for a flight control system is given to demonstrate the effectiveness of the results. Copyright © 2005 John Wiley & Sons, Ltd.

An improved deadbeat control for UPS using disturbance observers

Abstract: A digital control technique for the inverter stage of uninterruptible power supplies is proposed, which is based on a predictive regulator on both output voltage and inductor current. Its aim is to achieve a deadbeat dynamic response for the controlled variables (output voltage and inverter current). Besides the linear state feedback which allocates system poles at the origin so as to achieve deadbeat response for all state variables, the use of a disturbance observer for the estimation of the load current and of any other source of errors (such as dead-times, parameter, and model mismatches) is investigated. The proposed solution is able to guarantee a fast dynamic response and also a precise compensation of any source of unpredictable disturbance. Moreover, with a proper design of observer parameters, it is possible to reduce control sensitivity to model uncertainties, parameter mismatches, and noise on sensed variables, which usually characterizes existing deadbeat control techniques. Finally, the control algorithm is quite simple and requires only the measurements of the output voltage and inductor current. Experimental results on a single-phase 2 kVA prototype show the effectiveness of the proposed approach.

MRAS observer for sensorless control of standalone doubly fed induction generators

Abstract: This paper presents an analysis of a model reference adaptive system (MRAS) observer for the sensorless control of a standalone doubly fed induction generator (DFIG). The analysis allows the formal design of the MRAS observer of given dynamics and further allows the prediction of rotor position estimation errors under parameter mismatch. The MRAS observer analysis is experimentally implemented for the vector control of a standalone DFIG feeding a load at constant voltage and frequency. Experimental results, including speed catching of an already spinning machine, are presented and extensively discussed. Although the method is validated for a standalone generator, the proposed MRAS observer can be extended to other applications of the doubly fed induction machine.

Unknown input proportional multiple-integral observer design for linear descriptor systems: application to state and fault estimation

Abstract: In this note, the problem of observer design for linear descriptor systems with faults and unknown inputs is considered. First, it is considered that the fault vector function f is s~ times piecewise continuously differentiable. If the s~th time derivative of f is null, then s~ integral actions are included into a Luenberger observer, which is designed such that it estimates simultaneously the state, the fault, and its finite derivatives face to unknown inputs. Second, when the fault is not time piecewise continuously differentiable but bounded (like actuator noise) or s~th time derivative of fault is not null but bounded too, a high gain observer is derived to attenuate the fault impact in estimation errors. The considered faults may be unbounded, may not be determinist, and faults and unknown inputs may affect the state dynamic and plant outputs. Sufficient conditions for the existence of such observer are given. Results are illustrated with a differential algebraic power system.

Robust backstepping control for a class of time delayed systems

Abstract: In this note, the problem of robust output feedback control for a class of nonlinear time delayed systems is considered. The systems considered are in strict-feedback form. State observer is first designed, then based on the observed states the controller is designed via backstepping method. Both the designed observer and controller are independent of the time delays. Based on Lyapunov stability theory, we prove that the constructed controller can render the closed-loop system asymptotically stable. Simulation results further verify the effectiveness of the proposed approach.

Observer-based direct adaptive fuzzy-neural control for nonaffine nonlinear systems

Abstract: In this paper, an observer-based direct adaptive fuzzy-neural control scheme is presented for nonaffine nonlinear systems in the presence of unknown structure of nonlinearities. A direct adaptive fuzzy-neural controller and a class of generalized nonlinear systems, which are called nonaffine nonlinear systems, are instead of the indirect one and affine nonlinear systems given by Leu et al. By using implicit function theorem and Taylor series expansion, the observer-based control law and the weight update law of the fuzzy-neural controller are derived for the nonaffine nonlinear systems. Based on strictly-positive-real (SPR) Lyapunov theory, the stability of the closed-loop system can be verified. Moreover, the overall adaptive scheme guarantees that all signals involved are bounded and the output of the closed-loop system will asymptotically track the desired output trajectory. To demonstrate the effectiveness of the proposed method, simulation results are illustrated in this paper.

Optimal Estimation with Limited Measurements

Abstract: We consider a sequential estimation problem with two decision makers, or agents, who work as members of a team. One of the agents sits at an observation post, and makes sequential observations about the state of an underlying stochastic process for a fixed period of time. The observer agent upon observing the process makes a decision as to whether to disclose some information about the process to the other agent who acts as an estimator. The estimator agent sequentially estimates the state of the process. The agents have the common objective of minimizing a performance criterion with the constraint that the observer agent may only act a limited number of times.

Observer design for systems with unknown inputs

Abstract: Design procedures are proposed for two different classes of observers for systems withunknown inputs In thefirst approach, the state of the observed system is decomposed into known and unknown components The unknown component is a projection, not necessarily orthogonal, of the whole state along the subspace in which the available state component resides Then, a dynamical system to estimate the unknown component is constructed Combining the output of the dynamical system, which estimates the unknown state component, with the available state information results in an observer that estimates the whole state It is shown that some previously proposed observer architectures can be obtained using the projection operator approach presented in this paper The second approach combines sliding modes and the second method of Lyapunov resulting in a nonlinear observer The nonlinear component of the sliding mode observer forces the observation error into the sliding mode along a manifold in the observation error space Design algorithms are given for both types of observers

Fault detection for Markovian jump systems

Abstract: The paper deals with the robust fault detection problem for a class of discrete-time linear Markovian jump systems with an unknown input. By using a general observer-based fault detection filter as residual generator, the robust fault detection filter design is formulated as an H∞-filtering problem, in which the filter matrices are dependent on the system mode, i.e. the residual generator is a Markovian jump linear system as well. The main objective is to make the error between residual and fault (or, more generally, weighted fault) as small as possible. A sufficient condition to solve this problem is established in terms of the feasibility of certain linear matrix inequalities (LMI), which can be solved with the aid of Matlab LMI Toolbox. A numerical example is given to illustrate the effectiveness of the proposed techniques.

Global finite-time stabilization by output feedback for planar systems without observable linearization

Abstract: This note considers the problem of global finite-time stabilization by output feedback for a class of planar systems without controllable/observable linearization. A sufficient condition for the solvability of the problem is established. By developing a nonsmooth observer and modifying the adding a power integrator technique, we show that an output feedback controller can be explicitly constructed to globally stabilize the systems in finite time. As a direct application of the main result, global output feedback finite-time stabilization is achieved for the double linear integrator systems perturbed by some nonlinear functions which are not necessarily homogeneous.

Nonlinear observer control for full-state projective synchronization in chaotic continuous-time systems

Abstract: Projective synchronization, characterized by a scaling factor that two coupled systems synchronize proportionally, is usually observable in a class of nonlinear dynamical systems with partial-linearity. We show that, by using an observer-based control, the synchronization could be realized in a general class of chaotic systems regardless of partial-linearity. In addition, this technique overcomes some limitations in previous work, capable to achieve a full-state synchronization with a specified scaling factor, and adjust the scaling factor arbitrarily in due course of control without degrading the controllability. Feasibility of the technique is illustrated for a chaotic circuit converter and the Chen’s attractor.

A State Bounding Observer for Uncertain Non-linear Continuous-time Systems based on Zonotopes

Abstract: A state bounding observer aims at computing some domains which are guaranteed to contain the set of states that are consistent both with the uncertain model and with the uncertain measurements. In this paper, the estimated domains are represented by zonotopes. A zonotope is a particular polytope defined as the linear image of a unit interval vector (i.e. unit hypercube). Some results about the validated integration of ordinary differential equations are used to guarantee the inclusion of sampling errors. The main loop of the observation algorithm consists of a one step prediction with a limitation of the domain complexity and a correction using the measurements. The observer is applied to a Lotka-Volterra predator-prey model.

A complete solution to underwater navigation in the presence of unknown currents based on range measurements from a single location

Abstract: An underwater navigation algorithm is considered that enables an underwater vehicle to compute its trajectory and unknown currents by utilizing range measurements from a single known location By assessing local observability about potential vehicle trajectories, we characterize those trajectories that cannot be asymptotically estimated Analysis is also presented to clarify the relationship between finite time observability, a theme of this work, and existence of an observer with exponentially decaying observation dynamics The navigation algorithm is illustrated using a hardware experiment

Path-following control of mobile robots in presence of uncertainties

Abstract: This paper presents, in detail, the implementation of a new control strategy, Kalman-based active observer controller (AOB), for the path following of wheeled mobile robots (WMRs) subject to nonholonomic constraints. This control strategy presents some particularities as being used in discrete mode, and being robust against uncertainties and disturbances such as the ones due to the use of the input-output feedback-linearization method in discrete mode, while it was developed to be used in continuous mode. The performance of the proposed control algorithm is verified via computer simulation, and is compared with other control strategies, such as pole placement controller (PPC) and PPC with a Kalman filter observer (CKF).

An MRAS-based sensorless high-performance induction motor drive with a predictive adaptive model

Abstract: This paper presents a new model reference adaptive system (MRAS) speed observer for high-performance field-oriented control induction motor drives based on adaptive linear neural networks. It is an evolution and an improvement of an MRAS observer presented in the literature. This new MRAS speed observer uses the current model as an adaptive model discretized with the modified Euler integration method. A linear neural network has been then designed and trained online by means of an ordinary least-squares (OLS) algorithm, differently from that in the literature which employs a nonlinear backpropagation network (BPN) algorithm. Moreover, the neural adaptive model is employed here in prediction mode, and not in simulation mode, as is usually the case in the literature, with a consequent quicker convergence of the speed estimation, no need of filtering the estimated speed, higher bandwidth of the speed loop, lower estimation errors both in transient and steady-state operation, better behavior in zero-speed operation at no load, and stable behavior in field weakening. A theoretical analysis of some stability issues of the proposed observer has also been developed. The OLS MRAS observer has been verified in numerical simulation and experimentally, and in comparison with the BPN MRAS one presented in the literature.

Fault identification for robot manipulators

Abstract: Several factors must be considered for robotic task execution in the presence of a fault, including: detection, identification, and accommodation for the fault. In this paper, a nonlinear observer is used to identify a class of actuator faults once the fault has been detected by some other method. Advantages of the proposed fault-identification method are that it is based on the nonlinear dynamic model of a robot manipulator (and hence, can be extended to a number of general Euler Lagrange systems), it does not require acceleration measurements, and it is independent from the controller. A Lyapunov-based analysis is provided to prove that the developed fault observer converges to the actual fault. Experimental results are provided to illustrate the performance of the identification method.

Interval observers for biochemical processes with uncertain kinetics and inputs

Abstract: This paper concentrates on the state observation in bioprocesses when there is uncertainty on the process parameters and/or the process inputs. An interval observer is designed on the basis of the cooperativity properties of the model for a standard stirred tank bioreactor model with a single microbial growth and a kinetic model depending on the substrate concentration. Further assumptions are the (lower and upper) boundedness of the specific growth rate and the inlet substrate concentration. Mathematical analysis of the stability and convergence of the interval observer is performed both in absence and in presence of uncertainty on the measurements. It is shown in particular that when the process inputs are known, the static observation error on the unknown state is inversely proportional to one of the observer gains. The performance of the interval observer are also illustrated through numerical simulation.

Parameter fault detection and estimation of a class of nonlinear systems using observers

Abstract: The focus of this paper is on the detection and estimation of parameter faults in nonlinear systems with nonlinear fault distribution functions. The novelty of this contribution is that it handles the nonlinear fault distribution function; since such a fault distribution function depends not only on the inputs and outputs of the system but also on unmeasured states, it causes additional complexity in fault estimation. The proposed detection and estimation tool is based on the adaptive observer technique. Under the Lipschitz condition, a fault detection observer and adaptive diagnosis observer are proposed. Then, relaxation of the Lipschitz requirement is proposed and the necessary modification to the diagnostic tool is presented. Finally, the example of a one-wheel model with lumped friction is presented to illustrate the applicability of the proposed diagnosis method.

Observer Design for Nonlinear Systems: An Approach Based on the Differential Mean Value Theorem.

Abstract: In this note, observers design for a class of non linear dynamical systems has been investigated. The main contribution lies in the use of the differential mean value theorem (DMVT) to transform the nonlinear error dynamics into a LPV system. The stability analysis is, therefore, performed using a standard Lyapunov function that leads to the solvability of a set of Linear Matrix Inequalities (LMIs), easily tractable. Numerical examples are provided to show high performances of the proposed approach and the large class of nonlinear dynamical systems that are concerned.

Observer-based control of discrete-time Lipschitzian non-linear systems: application to one-link flexible joint robot

Abstract: The problem of designing asymptotic observers along with observer-based feedbacks for a class of discrete-time non-linear systems is considered. We assume that the system non-linearity is globally Lipschitz and the system is supposed to be stabilizable by a linear controller. Sufficient linear matrix inequality condition is derived to ensure the stability of the considered system under the action of feedback control based on the reconstructed states. A numerical example of a single-link flexible joint robot is presented to illustrate the efficacy of the theoretical developments.

de Sitter holography with a finite number of states

Abstract: We investigate the possibility that, in a combined theory of quantum mechanics and gravity, de Sitter space is described by finitely many states. The notion of observer complementarity, which states that each observer has complete but complementary information, implies that, for a single observer, the complete Hilbert space describes one side of the horizon. Observer complementarity is implemented by identifying antipodal states with outgoing states. The de Sitter group acts on S-matrix elements. Despite the fact that the de Sitter group has no nontrivial finite-dimensional unitary representations, we show that it is possible to construct an S-matrix that is finite-dimensional, unitary, and de Sitter-invariant. We present a class of examples that realize this idea holographically in terms of spinor fields on the boundary sphere. The finite dimensionality is due to Fermi statistics and an ``exclusion principle'' that truncates the orthonormal basis in which the spinor fields can be expanded.