A new way to design parameter estimators with enhanced performance is proposed in the paper. The procedure consists of two stages, first, the generation of new regression forms via the application of a dynamic operator to the original regression. Second, a suitable mix of these new regressors to obtain the final desired regression form. For classical linear regression forms the procedure yields a new parameter estimator whose convergence is established without the usual requirement of regressor persistency of excitation. The technique is also applied to nonlinear regressions with "partially" monotonic parameter dependence-giving rise again to estimators with enhanced performance. Simulation results illustrate the advantages of the proposed procedure in both scenarios.

We present some new results on the dynamic regressor extension and mixing parameter estimators for linear regression models recently proposed in the literature. This technique has proven instrumental in the solution of several open problems in system identification and adaptive control. The new results include: (i) a unified treatment of the continuous and the discrete-time cases; (ii) the proposal of two new extended regressor matrices, one which guarantees a quantifiable transient performance improvement, and the other exponential convergence under conditions that are strictly weaker than regressor persistence of excitation; and (iii) an alternative estimator ensuring convergence in finite-time whose adaptation gain, in contrast with the existing one, does not converge to zero. Simulations that illustrate our results are also presented.

No abstract

A novel approach to the problem of partial state estimation of nonlinear systems is proposed. The main idea is to translate the state estimation problem into one of estimation of constant, unknown parameters related to the systems initial conditions. The class of systems for which the method is applicable is identified via two assumptions related to the transformability of the system into a suitable cascaded form and our ability to estimate the unknown parameters. The first condition involves the solvability of a partial differential equation while the second one requires some persistency of excitation-like conditions. The proposed observer is shown to be applicable to position estimation of a class of electromechanical systems, for the reconstruction of the state of power converters and for speed observation of a class of mechanical systems.

No abstract

A sensorless algorithm is developed on the basis of a rotor flux observer in the stationary frame. In particular, it involves a parameter adaptive algorithm for an initial rotor flux like the observer, which was recently proposed by Bobtsov et al. (Automatica, vol 61, Nov, 2015). In the proposed method, the flux observer is linked to the parameter estimator via a compensating term which results from parameter error. This method has a robust property against dc bias errors, i.e., it cures the inherent weakness of the pure integrator (flux observer) to dc offsets which frequently occur in current measurements and voltage estimates. The robust performance is demonstrated through simulations and experimental results. Index terms: Sensorless, nonlinear observer, flux estimator, linear regression form, position observer, voltage offset. NOMENCLATURE α − β Stationary axis reference frame quantities. d − q Synchronous axis reference frame quantities. v, i Stator voltage and current. λ Stator flux. x Rotor flux. η Initial rotor flux. q Subtracted value initial rotor flux from rotor flux. R, L Resistance and inductance of stator winding. ψ m PM flux linkage constant.

No abstract

We would like to thank the Editor in Chief and the reviewers for their interest in our manuscript and also for providing many constructive comments and valuable suggestions. Their comments and suggestions have helped us to improve the quality of the paper, and have been included in the revised manuscript.