144 research outputs found
Comments on “Repetitive learning control for a class of partially linearizable uncertain nonlinear systems”, [Automatica, 85 (2017) 397–404]
No full textThis correspondence points out connections between the results achieved in Verrelli (2016) and the ones obtained in Chen and Liu (2017). Problem formulation, control structure, and stability proof in Chen and Liu (2017) can be revisited and recast to reveal similarities that provide a new interpretation of such a paper in the light of Verrelli (2016). (C) 2019 Elsevier Ltd. All rights reserved
Nonlinear tracking control for sensorless permanent magnet synchronous motors with uncertainties
No full textThe recent advanced solution in Marino, Tomei, and Verrelli (2013) to the tracking control problem for sensorless IMs with parameter uncertainties is translated on the basis of letter swap connections between the models of (nonsalient-pole surface) permanent magnet synchronous motors (PMSMs) and induction ones (IMs). The (stability proof-based) nonlinear adaptive position/speed tracking control for sensorless PMSMs (with simultaneous estimation of uncertain constant load torque and stator resistance), which is accordingly obtained by exploring and decoding the design paths in Marino et al. (2013) and which surprisingly represents a simple generalization of the controller in Tomei and Verrelli (2011), constitutes an innovative solution to the related open problem. Illustrative experimental results are included
Linear Repetitive Learning Controls for Robotic Manipulators by Padé Approximants
No full textThe aim of this brief is to present the use of [m, m]-Pade approximants in the implementation of repetitive learning controls for the asymptotic joint position tracking of robotic manipulators with uncertain dynamics and periodic position reference signals (with known period). The resulting linear learning controls, which are derived through a detailed stability proof (involving the use of a suitable Lyapunov-like function), are described by transfer functions exhibiting all their poles with a negative real part while allowing of experimental improvements in the output tracking errors as the approximation order m increases. Analyses from both theoretical and experimental points of view are included. Such control laws are good candidates to be implemented in industrial robot control units for repetitive tasks in place of classical proportional-integral-derivative (PID) controls
Robust output feedback learning control for induction motor servo drives
No full textThis 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
Two-age-structured covid-19 epidemic model: Estimation of virulence parameters to interpret effects of national and regional feedback interventions and vaccination
No full textThe COVID-19 epidemic has recently led in Italy to the implementation of different external strategies in order to limit the spread of the disease in response to its transmission rate: strict national lockdown rules, followed first by a weakening of the social distancing and contact reduction feedback interventions and finally the implementation of coordinated intermittent regional actions, up to the application, in this last context, of an age-stratified vaccine prioritization strategy. This paper originally aims at identifying, starting from the available age-structured real data at the national level during the specific aforementioned scenarios, external-scenario-dependent sets of virulence parameters for a two-age-structured COVID-19 epidemic compartmental model, in order to provide an interpretation of how each external scenario modifies the age-dependent patterns of social contacts and the spread of COVID-19
Repetitive learning control design and period uncertainties
No full textThe aim of this brief is to show how stability proofs in the time-domain involving suitable quadratic-integral Lyapunov-like functions can be derived in the repetitive control design scenario in the case of uncertain period for the reference signals/disturbances to be tracked/rejected. Even though the presented arguments are rather general, we apply them to the generalization of the proportional-integral-derivative (PID)-like learning control that has been recently designed. The use of the presented results in multi-link robot synchronization tasks provides simple and intuitive solutions to as yet unsolved problems
AC motors: Letter swap potentialities
No full textThis technical communique illustrates the potentialities of the recently found letter swap connections between the models of AC induction motors (IMs) and (nonsalient-pole surface) permanent magnet synchronous motors (PMSMs): observability and observer design issues for PMSMs are immediately addressed by directly exploring and decoding the logical paths characterizing the recent corresponding analysis for IMs, without re-performing the related analysis/design from the beginning. (C) 2019 Elsevier Ltd. All rights reserved
Virtual control strategy and conditioning technique for tracking and learning controls under input restrictions
No full textThe virtual control strategy for mechanical systems has been recently proposed (Gnucci and Marino, 2021) in the context of under-actuated mechanical systems. Such a strategy views and represents an under-actuated mechanical system as a fully actuated system with virtually added inputs and outputs having to satisfy, through a suitable choice of the virtual output reference signals, the virtual input zero-equality constraint: the related adaptive tracking control problem is then solved through standard design techniques. This paper exhibits a twofold aim. The first one is: to enlarge the concept of zero-input constraint and thus naturally adapt the virtual control approach to the case in which an actuator fault can occur. The second aim is: to show how the application and transposition of such an adaptation to two well-known classes of nonlinear systems (special systems in multi-variable tracking form with two inputs and outputs under actuator faults; one-relative-degree, single-input, single-output systems in output feedback form under input saturation) not only own strong connections with the conditioning technique, originally conceived in the context of anti-windup problems under input constraints, but they also gain original results
A new spatial learning control for autonomous vehicles: experimental results
No full textAutonomous 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
Pacejka-Like Curve-Based Speed Reference Generators for Electric Vehicles Powered by in-Wheel Motors
No full textRecently, motor speed reference generators have been designed for the cruise control of electric vehicles with either centralized electric motors (straight manoeuvres) or in-wheel motors (bend manoeuvres with sufficiently small constant steering angles). Steady-state operation at a safe (though conservative) tire longitudinal slip can be achieved, with no a priori knowledge regarding the occurrence of a specific external condition. Here, an innovative solution is presented. It is based on an ingenious use of Pacejka-like curves - representing the torque current-slip characteristics of the vehicle - to design a new contraction-mapping-based automatic tuning procedure for the longitudinal velocity of the vehicle. Such an innovative procedure overcomes the highly conservative nature of the previous approach in terms of unduly small longitudinal velocities (and yaw rates) under relatively favourable external conditions while guaranteeing a safe operating condition close to the maximum of the torque-slip characteristics with no knowledge of the tire-road adhesion coefficient. Comparative CarSim simulations illustrate the effectiveness of the proposed approach, in the presence of uncertain parameters and complex vehicle dynamics that are neglected at the control design stage
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