The authors address the problem of controlling rigid robot manipulators subject to bounded torque input disturbances and to joint velocity measurements bounded noise. The advantages of the control scheme presented are mainly that global convergence is guaranteed without prior knowledge of the bounds on the plant parameters and disturbances. If these bounds are known a priori, they can be used to improve the control scheme performance. Disturbances on the plant input as well as on the plant output are considered. The cases discussed are for when the disturbances are bounded and the upperbounds may be known or unknown. and for when the disturbances are linear with respect to some unknown parameters.>

To provide frequency estimation, adaptive notch filters implemented in a cascade of second-order cells can be used, along with strategies to track time-varying parameters. The estimated frequencies are then used to provide the estimation of amplitudes and phases in a recursive manner. The proposed RML (recursive maximum-likelihood) algorithm consists of two steps. The first step involves a maximum-likelihood algorithm to adapt the cascaded filter parameters which will provide the frequency estimates. The second step uses these estimates, and the amplitudes and phases are estimated by a recursive-least-squares algorithm The RML algorithm is asymptotically consistent and robust with respect to the neglected dynamics. In the case of time-varying signals its tracking capabilities ensure the goodness of the estimations.>

Utilizing the fact that a manipulator composed of rigid links is a passive system, an approach based on the properties of interconnected passive systems is proposed for the analysis and synthesis of fixed and adaptive controllers for robot manipulators. This approach clarifies the rationale behind the various fixed and adaptive control laws synthesized using Lyapunov functions.>

This paper presents several structures for Adaptive Notch Filters and implementation aspects of constrained ARMA models. These ANF suit very well to evolutionary spectrum analysis for sinusoidal signals in additive noise. colored or white. Several estimation algorithms may be applied with these structures (ELS,RML,RIV) and some special procedures may be imployed to increase the convergence rate, enhance the estimation consistency and reduce tha algorithm complexity. Lastly, non-stationary ANF are considered and an estimation procedure implemented.

A survey of a design approach through an equivalent feedback representation (EFR) is presented for adaptive filters. Several results for stability analysis in a deterministic environment and for convergence analysis in a stochastic environment are discussed. These results can be straightforwardly applied to various adaptive filters configurations. Current trends in using EFR for designing adaptive filters are mentioned.

Several problems in signal processing can be approached as recursive identification problems for AR, ARMA or ARMAX models. The output error with extended estimation model adaptive algorithm is proposed and evaluated for real-time estimation of the parameters of the signal model. The applications of these techniques for the adaptive signal processing are presented. The theoretical aspects related to the convergence properties of this algorithm are also discussed.

This correspondence presents a procedure to recognize handprinted alphanumeric characters written on a graphic tablet. After preprocessing, the input character is segmented into a polygon using a simple segmentation procedure. A feature vector is formed by the parameters which describe the segments of the polygon. Classification is done in two steps, the first one based on structural information extracted from the feature vector and the second based on statistical decision rule using parameters of the segments. A recursive learning procedure is introduced in the statistical classifier. The evaluation includes the measurement of recognition rates using several statistical classifiers, the validity test on the hypothesis concerning the distribution of feature vectors and the possibility of further simplification using principal axis analysis. Databases were created and used for the evaluation.

Presently, for provable global stability and unbiasedness in the presence of zero-mean, colored output measurement noise, adaptive infinite impulse response (IIR) filters (or adaptive output error identifiers) require a restrictive SPR condition on the transfer function dividing the error smoothing coefficient polynomial by the unknown desired denominator. This paper considers two attempts to bypass this restriction by incorporating error smoothing coefficient adaption. This paper clearly identifies the possible stall/failure mechanism of one of these candidate algorithms and proves the global stability of the other, but notes the equation error type source of the second's biased-ness in the presence of output measurement noise.