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We show how the design of repetitive learning controls, which typically require perfect knowledge of the period T characterizing the periodic reference signals to be tracked, can be generalized, through the design of novel recursive add-on period identifiers, to face sufficiently small period uncertainties in synchronization problems. Exponential period identification is achieved so that the asymptotic convergence properties guaranteed by the classical learning controls are preserved. Simulation results concerning multi-link robot synchronization tasks are included. Copyright © 2015 John Wiley & Sons, Ltd.

Summary We discuss the use of [m,m]-Pade approximants in the implementation of repetitive learning controls solving the output tracking problem (via output error feedback) in the presence of uncertain periodic reference and/or disturbance signals with known common period. The aim is to address the stability issues concerning those approximants when a linear learning controller—designed through a detailed stability proof (involving the use of a suitable Lyapunov-like function) and described by a transfer function exhibiting all its poles with negative real part—is to be obtained as well as to evaluate the corresponding closed-loop performances: robustness (for instance with respect to additive disturbance noises due to unmodeled sensor dynamics) is consequently achieved with improvements in the output tracking errors appearing as the approximation order m increases. Even though the case of any relative degree may be explicitly addressed, in this paper, for the sake of clarity, we restrict our attention to the learning problem for the class of single-input, single-output, minimum phase, time-invariant systems with known relative degree ρ = 2, uncertain parameters and uncertain output-dependent nonlinearities. Numerical simulation results illustrate the theoretical derivations. Copyright © 2014 John Wiley & Sons, Ltd.

SUMMARY In this brief note, we consider the transient stabilization and voltage regulation problem for a synchronous generator connected to an infinite bus. We show how a much simpler output feedback controller, compared with previous results, can be obtained if a different stability analysis is carried out: a suitable change of coordinates is performed so that advantageous triangularity properties of the regulation error dynamics are highlighted. A novel first order estimator for the uncertain mechanical input power is designed: it is the only adaptation scheme used by the controller. The innovative crucial step in the presented control design relies on relaxing the constraint imposed by the typically (previously) adopted back-stepping techniques and constituted by the required availability of the regulation/tracking error variables: the converging estimate of the mechanical input power directly becomes the reference value for the electrical power and allows for the straightforward computation of the uncertain power angle constant value guaranteeing voltage regulation at steady state. Copyright © 2011 John Wiley & Sons, Ltd.

SUMMARY The exponential output tracking problem for a class of single-input, single-output uncertain nonlinear systems, including systems with extended matching unstructured uncertainties and without a well-defined global relative degree, is addressed. Conditions on the uncertain system dynamics are derived, which allow us to design a state-feedback learning control achieving semi-global exponential output tracking of sufficiently smooth and periodic reference signals of known period, while guaranteeing ℒ2 and ℒ∞ transient performances during the learning phase. The application of the proposed learning approach to the position tracking control problem for uncertain permanent magnet step motors with non-sinusoidal flux distribution and uncertain position-dependent load torque allows us to provide a solution to a yet unsolved problem. Copyright © 2011 John Wiley & Sons, Ltd.

This paper addresses the output feedback tracking control problem for induction motor servo drives with mechanical uncertainties: rotor angle, rotor speed and stator Currents are assumed to be available for feedback. A robust adaptive learning control is designed under the assumption that the reference profile for the rotor angle is periodic with known period: it 'learns' the periodic disturbance signal by identifying the Fourier coefficients of any truncated approximation; L-2 and L-infinity transient performances are guaranteed in the 'learning phase'. It is shown that, for any motor initial condition belonging to an arbitrary given compact set, by properly setting the control parameters: (i) the rotor position and flux modulus tracking errors, exponentially converge to residual sets, which may be arbitrarily reduced by increasing the number of terms in the truncated Fourier series; (ii) when the unknown periodic disturbance can be represented by a finite Fourier series, the rotor position and flux modulus tracking errors exponentially converge to zero. As in field oriented-control, the control algorithm generates references for the magnetizing flux component and for the torque component of the stator current leading to significant simplifications for current-fed motors. Copyright (C) 2008 John Wiley & Sons, Ltd.

We propose a novel nonlinear tracking control algorithm for induction motors with uncertain load torque in which only stator currents are used for feedback: rotor speed and flux sensors are not required; no open-loop integration of flux dynamics is used. For a class of reference signals, for sufficiently small computable time-varying load torque uncertainty and for any initial condition belonging to an explicitly computed region, closed-loop boundedness is guaranteed while, under persistency of excitation, local exponential rotor speed and flux modulus tracking are achieved in the case of constant load torque. Copyright © 2007 John Wiley & Sons, Ltd.

Assuming that only stator currents and voltages are available for feedback, a nonlinear adaptive speed tracking control algorithm is proposed for a permanent magnet step motor with unknown constant load torque. It relies on three new theoretical results: the ‘s-alignment’ and ‘c-alignment’ procedures, in which the motor is forced to reach certain known equilibrium points, and an output feedback controller which guarantees asymptotic speed tracking for every initial condition belonging to an explicitly computed domain of attraction (global exponential convergence is achieved in the case of known constant load torque). Numerical simulation results illustrate the effectiveness of the proposed solution. Copyright © 2007 John Wiley & Sons, Ltd.

The transient stabilization problem of a synchronous generator in a multimachine power system is addressed. A robust adaptive nonlinear feedback control algorithm is designed on the basis of a third order model of the synchronous machine in a multimachine power network which takes into account the effect of transfer conductances and of the remote network dynamics: it does not require the machine internal voltage measurement and relies on the knowledge of only three model parameters and of upper and lower bounds on the uncertain ones. Sufficient conditions to be satisfied by the remaining part of the network for guaranteeing L2 and L∞ disturbance attenuation and relative speed regulation are derived which are more general than those required by the single machine-infinite bus approximation.