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This paper presents a modified set theoretic framework for estimating the state of a linear dynamic system based on uncertain measurements. The measurement errors are assumed to be unknown but bounded by ellipsoidal sets. Based on this assumption, a recursive state estimator is (re-)derived in a tutorial fashion. It comprises both the prediction step (time update), i.e., propagation of a set of feasible...
The classification process of the Counter Propagation neural network (CPN) is investigated. The homogeneity distribution of the codebook vectors is a key element in the accuracy of the classification process. The paper defines an appropriate homogeneity measure that is strongly correlated with the optimal misclassification error. Based on this homogeneity value, the paper proposes three modification...
Adaptive control approaches can successfully handle system parametric uncertainties, but can hardly deal with unstructured uncertainties. In adaptive neural network and adaptive fuzzy network approaches, the unstructured uncertainties are converted into parametric ones using artificially selected neural or fuzzy basis functions. In this paper we introduce adaptive wavelet network working concurrently...
The problem of non-conservative minimisation of the maximum amplitude of error signals in the time domain for multivariable systems subject to uncertain inputs is considered. It is shown that this problem, which is usually tackled using L1 control theory, can also be addressed in the framework of H∞ theory, via the introduction of some new signal uncertainty sets, and a slight modification of the...
This paper considers the design of Transmit Pulse Shapes such that after transmission through a dispersive channel, the distorted pulse at the receiver fits in a prescribed template. The norm of the transmit pulse is minimized so as to reduce the effect of cross-talk at the receiver end. This design problem is formulated as a Quadratic Programming problem with affine functional inequality constraints...
Iterative controller design for planar Poiseuille flow by model unfalsification and controller redesign is the topic of the paper. The main contribution is to show that model-unfalsification-based iterative design can be useful in flow control problems. The a priori knowledge on the dynamics of the sampled system is obtained from the analytic approximation of the Navier-Stokes equations by a Galerkin...
The paper describes a design methodology which allows dynamic and parametric modelling uncertainty to be treated simultaneously. It combines the advantages of H∞ control techniques with those provided by robust design methods based on a polytopic representation of parametric uncertainty. The proposed method uses the Q-parameterization of all solutions of the H∞ control problem in addition to the robust...
The aim of this paper is to find a fixed structure transfer matrix, whose dynamics (i.e. whose poles) are not a priori fixed, and whose frequency response is the closest as possible to a target one. This non convex optimization problem is recast into a generalized μ problem. A generalized n lower bound is proposed, which provides an a priori suboptimal solution (i.e. a local minimum), namely a transfer...
This paper is concerned with the stability and control of linear systems with uncertain physical parameters. The case where the characteristic equations of the systems are polynomially dependent on uncertain parameters is studied. A new algorithm is presented for the calculation of stability margins in the parameter space in general / p-norms. The stability is defined with respect to a desired region...
This paper considers the problem of applying eigenstracture assignment to parameter dependant systems. The concept of modal sensitivity is introduced, and a recently presented method for the assignment of reduced sensitivity eigenstracture is presented in this context. A new proof for this method is presented, allowing greater insight into the sensitivity problem, and leading to a more flexible design...
This paper addresses the problem of stability robustness of minimum-phase nonlinear dynamical systems modeled with feedforward neural networks with bounded parametric uncertainties under IOF linearization. By means of an affine description of the feedforward neural network model which takes into account the parametric uncertainties, the Input-Output Feedback (IOF) linearization is performed and the...
For both continuous and discrete-time cases, this paper presents a simple solution to the robust H∞ unbiased functional reduced order filtering problem via LMI methods in the presence of norm-bounded time-varying uncertainties. Necessary and sufficient conditions for the existence and the stability of the unbiased filter are given in the nominal case; the filter differs from that of 11] (only nominal...
In this paper, a nonlinear robust controller is proposed for the goal of power stabilization in power systems. The here-proposed approach is based on both the use of a properly-chosen sliding surface and the derivation of a disturbance attenuation condition (nonlinear L2-gain). The controller design is then applied to a single-machine-infinite-bus (SMIB) system and some simulation results demonstrate...
In this paper, we present a fuzzy observer based fuzzy control scheme for nonlinear processes. The observer and the controller are built by "fuzzily interconnecting" local linear Lu-enberger observers and linear controllers. The approach uses techniques of robust control along with piecewise quadratic lyapunov functions, to show the global stability of observer + controller + process. In...
This paper presents a novel approach to synthesis of time-optimal control laws for the class of Takagi-Sugeno (TS) fuzzy systems. The idea is based on transforming the given TS system into a corresponding Piecewise Affine (PWA). The feedback strategy is derived by using the principles of Model Predictive Control (MPC). In order to cope with the state-dependent changes in process dynamics induced by...
Discrete-time affine control systems with parameter uncertainty and exogenous disturbances are considered in the paper. Such systems are examined, where there is an a priori constraint on the Euclidean norm of the control. The nominal free system is supposed to be exponentially stable, the parameter uncertainty is cone-bounded. A saturation type state-feedback control is proposed. A sufficient condition...
This paper considers linear systems with bounded disturbances, that are not necessarily matched. A static output feedback sliding mode controller is designed. The existence problem is solved by determining a sliding surface that minimizes the ultimate bound of the reduced-order dynamics in the presence of unmatched disturbances. Linear Matrix Inequalities (LMIs) are derived to compute the sliding...
This paper addresses robust H∞ static output feedback control problem for multimodel systems with time-varying normbounded uncertainties. Sufficient conditions for synthesis of a static output feedback controller are derived in terms of a set of linear matrix inequalities (LMIs). The effectiveness of the proposed design method is demonstrated by an example for the control of a multimodel constituted...
The method of sample-based minimax optimization is developed for the minimization problem with an uncertain quadratic objective function subject to linear constraints. Several examples based on confidence statistical estimation are considered to define the uncertainty set. Analytical and numerical techniques are proposed for finding the optimal robust strategy.
Experts should analyse systems in order to define would-be faults in systems. As a result of this analysis, there will be a set of priori known faults supporting off-line teaching of neural networks. Unfortunately, it is impossible to define all faults in the design phase. As a result, a priori unknown faults may appear in systems. A priori unknown faults modify the distribution of the input patterns...
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