Cristiano Maria Verrelli

Induction motor control design

Nonlinear and Adaptive Control Design for Induction Motors is a unified exposition of the most important steps and concerns in the design of estimation and control algorithms for induction motors. A single notation and modern nonlinear control terminology is used to make the book accessible to readers who are not experts in electric motors at the same time as giving a more theoretical control viewpoint to those who are. In order to increase readability, the book concentrates on the induction motor, eschewing the much more complex and less-well-understood control of asynchronous motors. The concepts of stability and nonlinear control theory are presented in appendices. Important features of the book include: i) thorough coverage of speed sensorless control, important for applications; ii) a wide-ranging discussion of nonlinear adaptive controls containing parameter estimation algorithms; iii) coverage of the design of adaptive observers and parameter estimators which can complement state feedback design techniques; iv) comparative simulations of different control algorithms on the same motor to clarify the advantages and drawbacks of each. The content is organized in a pedagogical, progressive exposition starting from basic assumptions, structural properties, modelling, state feedback control and estimation algorithms, and moving on to more complex output feedback control algorithms and modelling for speed sensorless control. Adaptive output feedback controls are based on stator current measurements, both alone and in connection with rotor speed. The induction motor exhibits many typical and unavoidable nonlinear features and so the material presented in this volume will be of value to engineers engaged in the control of electric motors and to a broader audience interested in nonlinear control design.

A global tracking control for speed-sensorless induction motors

The problem of controlling an induction motor without rotor speed measurements is addressed. Arbitrary smooth reference signals for rotor speed and rotor flux modulus are required to be tracked globally (i.e. from any initial condition). A global second-order tracking control is obtained, which is based on a novel rotor speed observer. Simulation results are provided which illustrate the controller performance.

Brief paper: An adaptive tracking control from current measurements for induction motors with uncertain load torque and rotor resistance

The problem of controlling sensorless induction motors with uncertain constant load torque and rotor resistance on the basis of stator current measurements only is addressed. A new eighth-order dynamic nonlinear adaptive control algorithm is designed, which relies on a closed loop adaptive observer for the unmeasured state variables (rotor speed and fluxes) and for the uncertain parameters and is not based on non-robust open loop integration of flux dynamics. Local exponential stability of the closed loop tracking and estimation error dynamics is achieved under persistency of excitation conditions which restrict the reference signals and may be interpreted in terms of motor observability and rotor resistance identifiability.

A nonlinear tracking control for sensorless induction motors

We consider the tracking control problem for induction motors in which only stator currents and voltages are available for feedback. Local exponential rotor speed and rotor flux tracking is achieved for any initial condition belonging to an explicitly computable domain of attraction. Simulation results are reported.

Learning control for nonlinear systems in output feedback form

Abstract The class of single-input, single-output, minimum phase, nonlinear, time-invariant systems with unknown output-dependent nonlinearities, unknown parameters and known relative degree ρ is considered. The output regulation problem is addressed and solved in the presence of unknown periodic reference and/or disturbance signals of known common period. A simple learning control algorithm is designed which guarantees asymptotic output tracking for any initial condition belonging to any given connected compact set. It can be interpreted as a generalization of the classical PID ρ − 1 control which solves the regulator problem when reference and disturbance signals are constant. As far as linear systems are concerned, global results are achieved.

Fourier series expansion for synchronization of permanent magnet electric motors

Abstract Learning control techniques, based on Fourier approximation theory, are used to solve the tracking control problem via state (rotor position and speed and stator currents) feedback for permanent magnet synchronous motors performing repetitive tasks: smooth rotor position and speed reference signals, whose foreknowledge is not assumed and which are periodic of uncertain period, are required to be tracked for any motor initial condition. The effectiveness of the proposed solution is illustrated by its successfully application to a master – slave synchronization problem. The presented result illustrates the high potentiality of merging together functional approximation theory and advanced nonlinear control techniques.

Adaptive Field-oriented Control of Synchronous Motors with Damping Windings

Two different nonlinear dynamic control algorithms are presented for synchronous motors with damping windings: (i) an adaptive speed-sensorless controller for rotor position tracking in the presence of unknown constant load torque, on the basis of rotor angle, stator and field windings currents measurements; (ii) an adaptive control law for rotor speed tracking in the presence of uncertain constant load torque and motor inertia, which is based on measurements of mechanical variables (rotor angle and speed) and stator windings currents but does not require field current. As in classical field oriented control, the three voltage inputs are designed so that the direct axis component of the stator current vector is driven to zero; the controllers generate, as an intermediate step, the reference signals for the field current and for the quadrature axis component of the stator current vector, which respectively determine the direct axis component of the damping winding flux vector and the electromagnetic torque. Simulation results are provided for a 20-KW synchronous machine, which show the effectiveness of the two proposed control algorithms.

Nonlinear tracking control for sensorless induction motors

We consider the tracking control problem for induction motors in which only stator currents and voltages are available for feedback. Local exponential rotor speed and rotor flux tracking is achieved for any initial condition belonging to an explicitly computable domain of attraction. Simulation results are reported.

Establishing improved convergence and robustness properties for the repetitive learning control

Abstract A novel learning control scheme is designed for a class of nonlinear systems. Not only global asymptotic tracking is achieved but also sufficient conditions for the asymptotic “input learning” are derived. The robustness with respect to a finite memory implementation of the control algorithm (which is based on the piecewise linear approximation theory) is guaranteed in the closed loop. The proposed approach allows for the solution of global output tracking problems: (i) for relative degree one systems with output dependent uncertainties; (ii) for nonlinear systems with matching uncertainties.

Adaptive learning control design for robotic manipulators driven by permanent magnet synchronous motors

On the basis of the ideas recently presented in Tomei and Verrelli (Tomei, P., and Verrelli, C.M. (2010), ‘Learning Control for Induction Motor Servo Drives with Uncertain Rotor Resistance’, International Journal of Control, 83, 1515–1528) and Marino et al. (Marino, R., Tomei, P., and Verrelli, C.M. (2011), ‘Robust Adaptive Learning Control for Nonlinear Systems with Extended Matching Unstructured Uncertainties’, International Journal of Robust and Nonlinear Control, Early View, doi: 10.1002/rnc.1720), we briefly show how the adaptive learning control design proposed in Liuzzo and Tomei (Liuzzo, S., and Tomei, P. (2009), Global Adaptive Learning Control of Robotic Manipulators by Output Error Feedback, International Journal of Adaptive Control and Signal Processing, 23, 97–109) can be extended to robotic manipulators driven by nonsalient-pole (surface) permanent magnet synchronous motors. Unstructured uncertain dynamics (that is no parameterisation is available for the uncertainties) of the rigid robot with rotational joints are considered as well as uncertainties in stator resistances of the synchronous motors are taken into account. Two solutions with clear stability proofs are presented: a global decentralised control via state feedback and a semi-global control via output feedback. Output tracking of known periodic reference signals and learning of corresponding uncertain input reference signals are achieved. Available results in the literature are thus improved since no simplification concerning negligible electrical motor dynamics is used.