Towards Robust Adaptive Least-Squares Parameter Estimation with Internal Feedback
Abstract The new concepts of the ‘covariance matrix normalization’ and the ‘cascade’ structure of the adaptive least-squares estimator are shown to generalize and extend the use of internal information feedback in various robustness/alertness-oriented modifications to the standard ALS estimation algorithm. In the cascade estimation structure it is possible to ‘naturally’ stabilize, rather than maximize, the information matrix so that the covariance windup and blowup are effectively eliminated and the celebrated square root update of the covariance matrix is no longer needed Consequently, a new, ‘single-loop/cascade’ ALS MIMO estimation algorithm, enabling to effectively track both slow and …
Linear parameter estimation and predictive constrained control of wiener/hammerstein systems
Abstract A new, analytical, orthonormal basis functions (OBF)-based design methodology for adaptive predictive constrained control of open-loop stable, possibly nonminimum phase, time-varying Wiener and Hammerstein systems is presented. A linear adaptive least-squares parameter estimation algorithm is applied both to a nonlinear static part and a linear dynamic, OBF-modeled factor of the Wiener/Hammerstein system. A notion of inverse systems is crucial for linear estimation of both Wiener and Hammerstein systems, with in verses of the nonlinear or linear parts respectively involved. The adaptive estimator is coupled with a simple but robust, predictive control strategy called Extended Horiz…
Constrained minimum variance control of nonsquare LTI MIMO systems
Constrained minimum variance control is offered for nonsquare LTI MIMO systems. A constrained control design takes advantage of the so-called control zeros. The new control strategy is compared with familiar generalized minimum variance control and possible application areas of the two are discussed.
2D harmonic analysis of the cogging torque in synchronous permanent magnet machines
Presents an approach to determine sources of cogging torque harmonics in permanent magnet electrical machines on the basis of variations of air‐gap magnetic flux density with time and space. The magnetic flux density is determined from the two‐dimensional (2D) finite element model and decomposed into the double Fourier series through the 2D fast Fourier transform (FFT). The real trigonometric form of the Fourier series is used for the purpose to identify those space and time harmonics of magnetic flux density whose involvement in the cogging torque is the greatest relative contribution. Carries out calculations for a symmetric permanent magnet brushless machine for several rotor eccentricit…
Control zeros and nonminimum phase LTI mimo systems
Abstract The paper presents a new, general, inverse-model/output-zeroing approach to zeros of LTI discrete-time multivariable, possibly nonsquare systems. It is shown on simple examples that the existing definitions of multivariable zeros fail to detect certain important zeros which contribute to zeroing the system output. As a result, a concept of ‘control zeros’ is introduced, followed by a general redefinition of minimum/nonminimum phase systems, both new contributions being based on the notion of (generalized) inverse systems. Output-zeroing/inverse-model/minimum-variance control-related justifications of the new approach are presented.
Adaptive estimation of Laguerre models with time-varying delay
Abstract An Orthonormal Basis Functions (OBF) approach is effectively used in adaptive parameter estimation of linear(ized) open-loop stable, possibly nonminimum phase plants with time-varying delay. In particular, discrete Laguerre models are considered in detail. A special attention is paid to the numerical conditioning issue in case of ’poor’ excitation of a plant under control, where OBF models are of particular value. Closed-loop predictive control simulations confirm the usefulness of adaptive OBF modelling, especially for systems with time-varying delays.
Extended Horizon Adaptive Model Algorithmic Control
Abstract A new, original, robust adaptive control strategy termed Extended Horizon Adaptive Model Algorithmic Control is presented. In EHAMAC, a new, combined, ’single-loop’/’cascade’ adaptive least-squares parameter estimator is coupled with a new, simple but powerful Extended Horizon Model Algorithmic Control so that open-loop stable non-minimum phase systems can be effectively controlled in the time-varying environment. In the new, cascade structure of the ALS estimator, the covariance windup and blowup are totally eliminated. Moreover, the sacramental square-root update of the covariance matrix is no longer needed On the other hand, employing EHMAC facilitates robustness design so that …