Speed sensorless nonlinear adaptive control of induction motor using combined speed and perturbation observer

High performance induction motors (IM) require a robust and reliable speed controller to maintain the speed tracking performance under various uncertainties and disturbances. This paper presents a sensorless speed controller for IM based on speed and perturbation estimation and compensation. By defining a lumped perturbation term to include all unmodeled nonlinear dynamics and external disturbances, two state and perturbation observers are designed with combining the model reference adaptive system (MRAS) based speed observer to estimate the flux and speed states and the flux- and speed-loop related lumped perturbation terms. The estimated flux, speed and perturbation terms are used to design an output feedback, speed sensorless nonlinear adaptive controller (SSNAC) for IM. The stability of the closed-loop system is addressed in Lyapunov theory. Effectiveness of the SSNAC is verified via simulation and experiment tests. Comparing with the standard vector control plus MRAS speed observer (VC-MRAS), the proposed SSNAC reduces the speed tracking error by 20% to 30% on average under model uncertainties and unknown load disturbance due to the estimation and compensation of perturbation terms. The combined observer can estimate the real rotor speed under speed varying and load changes and thus makes SSNAC achieve high performance robust speed drive without using speed sensors

Advanced Computational-Effective Control and Observation Schemes for Constrained Nonlinear Systems

Constraints are one of the most common challenges that must be faced in control systems design. The sources of constraints in engineering applications are several, ranging from actuator saturations to safety restrictions, from imposed operating conditions to trajectory limitations. Their presence cannot be avoided, and their importance grows even more in high performance or hazardous applications. As a consequence, a common strategy to mitigate their negative effect is to oversize the components. This conservative choice could be largely avoided if the controller was designed taking all limitations into account. Similarly, neglecting the constraints in system estimation often leads to suboptimal solutions, which in turn may negatively affect the control effectiveness. Therefore, with the idea of taking a step further towards reliable and sustainable engineering solutions, based on more conscious use of the plants' dynamics, we decide to address in this thesis two fundamental challenges related to constrained control and observation. In the first part of this work, we consider the control of uncertain nonlinear systems with input and state constraints, for which a general approach remains elusive. In this context, we propose a novel closed-form solution based on Explicit Reference Governors and Barrier Lyapunov Functions. Notably, it is shown that adaptive strategies can be embedded in the constrained controller design, thus handling parametric uncertainties that often hinder the resulting performance of constraint-aware techniques. The second part of the thesis deals with the global observation of dynamical systems subject to topological constraints, such as those evolving on Lie groups or homogeneous spaces. Here, general observability analysis tools are overviewed, and the problem of sensorless control of permanent magnets electrical machines is presented as a case of study. Through simulation and experimental results, we demonstrate that the proposed formalism leads to high control performance and simple implementation in embedded digital controllers

Semiglobal Uniform Asymptotic Stability of an Easy-to-Implement PLL-Like Sensorless Observer for Induction Motors

In this work, stability properties of a novel and easy-to-implement sensorless observer for induction motors are investigated. The considered solution is inspired by phase-locked-loop strategies and it takes advantage from the rotor flux derivatives to define a rotating vector to align with, even if such variables are not directly measured. Input-to-State Stability properties are proven for suitably defined electrical and magneto-mechanical observation-error subsystems. Time scale separation is, then, exploited to invoke Singular Perturbation arguments and guarantee boundedness of the observation errors trajectories. Finally, the Small Gain Theorem is adopted to prove semiglobal uniform asymptotic stability of the presented scheme. Some additional interesting properties are discussed, beside the 'main stream' of the proposed analysis. Simulations indicate the observer can be profitably exploited for induction machines speed sensorless control applications