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Extended Kalman Filters (EKFs), Phase Locked Loops (PLLs), and Stator Flux Observers (SFOs) are widely used for sensorless control of Permanent Magnet Synchronous Motors (PMSMs) drives. Their use (in their most advanced version) is here extended, on the basis of model analogies and suitably-guaranteed closed loop stability properties, to the sensorless speed regulation control of Hybrid Stepper Motors (HSMs), in which position and speed sensors are not employed to reduce costs and increase robustness with respect to high temperature and high-vibration environments. Both realistic simulations and experimental results demonstrate the feasibility of the proposed methods in terms of closed-loop performance and robustness to parameters mismatch.

The paper poses the basis for the application of the Pade-based-repetitive learning control to power converters, in all the scenarios where a periodic reference signal is to be tracked. Compared to standard repetitive schemes, the new relevant feature of the proposed control is that the learning scheme is reduced to a simple intrinsically stable transfer function, whose poles lie on the left-hand-side of the complex plane. Hence, storing a considerable amount of samples is no longer required, while resetting procedures are no longer involved. The Pade-based-repetitive control has a simple structure, while being able to automatically compensate for any periodic disturbance signals coming from external generators and dead-time. A stability analysis is reported, providing a useful guidance to tune the control parameters. Simulation results are presented to illustrate the effectiveness of the proposed algorithm.

Autonomous vehicles, that are equipped with an artificial vision system, are considered in this paper. A new space-learning control is proposed for the tracking of planar curves, whose uncertain curvature is $L$-periodic in the curvilinear abscissa $s$. Differently from the related results in the literature, the new control does not rely on the time derivative of $s.$ Experimental results illustrate the effectiveness of the proposed approach.

1Simple adaptations of the latest design ideas in [2] suggest the existence of a new observer to be used for sensorless speed control of (nonsalient-pole surface) permanent magnets synchronous machines. The rotor speed measurement-assumed to be measured in [2] by a low-resolution transducer providing a single pulse per revolution — is here replaced by the output of a PLL (Phase Locked Loop) block that is based on a third-order Steady-State Linear Kalman Filter (SSLKF). Experimental results concerning the new observer are provided.

In this paper we show that, when the rotor position is not measured in (nonsalient-pole surface) PMSMs with unknown constant load torque and stator resistance (with the rotor speed being available along with the stator currents), it is possible to guarantee exponential rotor position tracking and — mainly — regulation without resorting to persistency of excitation conditions which involve a non-definitely zero rotor speed reference. A new output feedback control is accordingly designed, which relies on a novel adaptive observer. The only price to be paid is constituted by a non-constant reference for the stator current vector d-component.

We present the use of [m, m]-Pade approximants in the implementation of repetitive learning controls for the asymptotic link position tracking in robotic manipulators with uncertain dynamics and periodic position reference signals (with known period). The resulting linear learning controllers are described by transfer functions exhibiting all their poles with negative real part. Analyses from both theoretical and experimental points of view are carried out.

An innovative cruise control algorithm for electric vehicles (trains, cars) powered by induction motors has been recently proposed in [12]. It achieves vehicle speed regulation to a given constant reference by incorporating a suitable rotor speed generator into a highly efficient adaptive indirect field oriented control. The aim of this paper is to solve an analogous regulation problem in which automatic, feedback-based variations in the vehicle speed reference are required to be on-line performed by the control algorithm in order to take into account constraints on the tire longitudinal slip. CarSim simulations illustrate the effectiveness of the proposed approach in performing challenging manoeuvres, even in the presence of complex vehicle dynamics that have been neglected in the control design steps.

We prove the existence of a P-type (proportional-type) space-learning control, which, on the basis of a kinematic third order nonlinear model of an autonomous nonholonomic vehicle and by a proper choice of the proportional control gain, guarantees asymptotic tracking of planar curves whose uncertain curvature is L-periodic in the curvilinear abscissa. The behavior of a human driver, who repetitively learns the correct action from the past experience in the space, is mathematically reproduced. A stability analysis is presented while simulation results demonstrate the effectiveness of the presented approach.

An observer-based indirect field oriented control scheme is proposed for the speed and traction control of electric vehicles powered by induction motors with uncertain constant load torque and stator and rotor resistances. The novel control architecture transforms the vehicle speed reference into a suitable motor speed reference and then guarantees the motor speed regulation to the corresponding reference on the basis of the motor speed regulation error. The motor speed measurement, which is typically available in electric vehicles, is used not only for traction control but for regulation purposes as well. The closed loop on-line identification of the three critical uncertain parameters allows for on-line estimating and imposing the motor flux modulus reference value which minimizes power losses and improves power efficiency.

A nonlinear adaptive control algorithm is proposed for induction machines. It is based on a two time-scale observer that allows the on-line identification of three critical uncertain parameters, i.e., load torque and motor stator and rotor resistances. In addition, the set-point of the rotor flux is adjusted in order to minimize the Joule losses and to improve the power efficiency. Experimental results illustrate the feasibility of the developed approach.

The output tracking problem of periodic reference signals (of known period) for single-input single-output observable minimum phase uncertain linear time-invariant systems with unitary relative degree is considered. A continuous global iterative learning control via output error feedback is designed which guarantees closed loop boundedness and asymptotic output tracking, thus improving the L 2 convergence achieved in [6]. Its closed loop performances are then compared with those obtained by using the adaptive learning control in [2] and the adaptive regulator in [5]: in particular the effects of finite memory implementation, measurement noise and uncertainties in the period of the output reference signal are explicitly taken into account in the simulations.

The output tracking control problem via state feedback is addressed for a class of single input-single output nonlinear systems which are affected by extended matching unstructured uncertainties. Under the assumption that the output reference signal is sufficiently smooth and periodic with known period, a robust adaptive learning control is designed, which learns the unstructured unknown periodic disturbance signals due to system uncertainties by identifying the Fourier coefficients of any truncated approximation while guaranteeing L2 and L? transient performances. For any initial condition of the system in an arbitrary given compact set, by properly setting the control parameters: i) the output tracking error exponentially converges to a residual set which may be arbitrarily reduced by increasing the number of terms in the truncated Fourier series expansions; ii) when the unknown periodic disturbances can be represented by finite Fourier series expansions, the output tracking error exponentially converges to zero.

We address the state feedback position tracking control problem for uncertain current-fed permanent magnet step motors with non-sinusoidal flux distribution. Under the assumption that the reference profile for the rotor angle is periodic with known period, a robust adaptive learning control algorithm is designed, which "learns" the non-structured unknown periodic disturbance signal due to system uncertainties by identifying the Fourier coefficients of any truncated approximation, while guaranteing L2 and Lx transient performances. It is shown that for any motor initial condition: i) the position tracking error exponentially converges to a residual set 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, it is exponentially reconstructed by the learning algorithm and exponential position tracking is achieved.

The problem of controlling sensorless induction motors with uncertain constant load torque and rotor resistance is addressed. A new eigth order dynamic nonlinear adaptive control algorithm is designed, which feeds back only stator current measurements and does not require open loop integration of flux dynamics. Local exponential rotor speed and flux modulus tracking of smooth bounded reference signals are guaranteed under persistency of excitation.

The output feedback tracking control problem for induction motor servo drives with mechanical uncertainties is addressed. Under the assumption that the reference profile for the rotor angle is periodic with known period, an adaptive learning control is designed, which "learns" the non-structured unknown periodic disturbance signal due to mechanical uncertainties by identifying the Fourier coefficients of any truncated approximation, while guaranteing L2 and Linfin transient performances. It is shown that, for any motor initial condition belonging to an arbitrary given compact set: i) the guaranteed output tracking precision improves 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, it is exponentially reconstructed by the learning algorithm and exponential output tracking is achieved. Simulation results for a digital implementation of the proposed controller are provided.

A nonlinear control for speed-sensorless synchronous motors with damping windings is designed. Assuming that flux initial values are known, exponential rotor angle tracking of arbitrary smooth bounded reference signals, rotor flux vector direct axis component tracking of suitable smooth bounded reference signals and stator current vector direct axis component regulation to zero (motor field orientation) are guaranteed for any initial condition and for any unknown constant load torque. In the case of non-exact knowledge of flux initial conditions, a local result holds.

The problem of designing a global output feedback tracking control for current-fed induction motor servo drives with mechanical uncertainties is addressed. Under the assumption that the reference profile for the rotor angle is periodic with known period, an adaptive learning control is designed which "learns" the non-structured unknown periodic disturbance signal due to mechanical uncertainties, by identifying the Fourier coefficients of any truncated approximation. It is shown that the output tracking precision improves by increasing the number of terms in the truncated Fourier series; when the unknown periodic disturbance can be represented by a finite Fourier series, it is asymptotically reconstructed by the learning algorithm and exponential output tracking is guaranteed. L2 and Linfin transient performances for the output tracking error are guaranteed in the learning phase

Assuming that only stator currents and voltages are available for feedback, a nonlinear output feedback adaptive speed tracking control strategy is proposed for a permanent magnet step motor with unknown 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 uniform 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 load torque. Numerical simulation results show the effectiveness of the proposed solution.