Search results for "Control theory"
showing 10 items of 1333 documents
LPV models: Identification for gain scheduling control
2001
In this paper the use of discrete-time Linear Parameter Varying (LPV) models for the gain scheduling control and identification methods for non-linear or time-varying system is considered. We report an overview on the existing literature on LPV systems for gain scheduling control and identification. Moreover, assuming that inputs, outputs and the scheduling parameters are measured, and a form of the functional dependence of the coefficients on the parameters is known, we show how the identification problem can be reduced to a linear regression so that a Least Mean Square and Recursive Least Square identification algorithm can be reformulated. Our methodology is applied for the identificatio…
A new modified Inc-Cond MPPT technique and its testing in a whole PV simulator under PSC
2015
In this paper a new modified Inc-Cond MPPT technique able to lead PV plants to work in the real MPP (Maximum Power Point) under PSC (Partial Shading Condition) is proposed. The new Inc-Cond MPPT technique working principle is fully explained, discussed and then successfully tested in a whole and reliable PV simulator conceived and set-up by the Authors in order to test and compare several new MPPT algorithms. The simulation results show that the proposed Inc-Cond MPPT technique is able to successfully lead the PV plant to work in the real MPP both in the cases of instantaneous and gradual changing of shading and of temperature conditions.
Velocity sensorless control of a PMSM actuator directly driven an uncertain two-mass system using RKF tuned with an evolutionary algorithm
2010
This paper proposes a solution to tune an observer keeping robust closed loop performances for the sensorless motion control of an uncertain mechanical load directly driven by a PMSM through a flexible axis. An evolutionary algorithm optimizes the observers degrees of freedom. Experiments show that performances are effectively maintained.
Fuzzy predictive controller design using ant colony optimization algorithm
2014
In this paper, an approach for designing an adaptive fuzzy model predictive control (AFMPC) based on the Ant Colony Optimization (ACO) is studied. On-line adaptive fuzzy identification is used to identify the system parameters. These parameters are used to calculate the objective function based on predictive approach and structure of RST control. The optimization problem is solved based on an ACO algorithm, used at the optimization process in AFMPC to calculate a sequence of future RST control actions. The obtained simulation results show that proposed approach provides better results compared with Proportional Integral-Ant Colony Optimization (PI-ACO) controller and adaptive fuzzy model pr…
Mathematical modelling of an inverter-based distributed generator
2016
In this paper, a modeling approach of a three phase power inverter based on an electrostatic synchronous machine is presented. By the proposed approach, any inverter-based distributed generator in a microgrid can be replaced by an equivalent electrostatic machine. This paper aims to promote the use of the proposed modeling approach in the analysis of microgrid stability. Thanks to the proposed modeling approach, transient performances of on-grid operation of converter-based distributed generators could be easily analyzed by multi-machine models. Parameters of the equivalent synchronous electrostatic machine model are derived to achieve equivalence with the inverter model. Small signal model…
Optimization model predictions for postural coordination modes
2003
International audience; This paper examines the ability of the dynamic optimization model to predict changes between in-phase and anti-phase postural modes of coordination and to evaluate influence of two particular environmental and intentional constraints on postural strategy. The task studied was based on an experimental paradigm that consisted in tracking a target motion with the head. An original optimal procedure was developed for cyclic problems to calculate hip and ankle angular trajectories during postural sway with a minimum torque change criterion. Optimization results give a good description of the sudden bifurcation phase between in-phase and anti-phase postural coordination mo…
NARX Models of an Industrial Power Plant Gas Turbine
2005
This brief reports the experience with the identification of a nonlinear autoregressive with exogenous inputs (NARX) model for the PGT10B1 power plant gas turbine manufactured by General Electric-Nuovo Pignone. Two operating conditions of the turbine are considered: isolated mode and nonisolated mode. The NARX model parameters are estimated iteratively with a Gram-Schmidt procedure, exploiting both forward and stepwise regression. Many indexes have been evaluated and compared in order to perform subset selection in the functional basis set and determine the structure of the nonlinear model. Various input signals (from narrow to broadband) for identification and validation have been consider…
Robust adaptive backstepping control design for a Nonlinear Hydraulic-Mechanical System
2009
The complex dynamics that characterize hydraulic systems make it difficult for the control design to achieve prescribed goals in an efficient manner. In this paper, we present the design and analysis of a robust nonlinear controller for a Nonlinear Hydraulic-Mechanical (NHM) System. The system consists of an electrohydraulic servo valve and two hydraulic cylinders. Specifically, by considering a part of the dynamics of the NHM system as a norm-bounded uncertainty, two adaptive controllers are developed based on the backstepping technique that ensure the tracking error signals asymptotically converge to zero despite the uncertainties in the system according to the Barbalat lemma. The resulti…
MRAS speed observer for high performance linear induction motor drives based on linear neural networks
2011
This paper proposes a Neural Network (NN) MRAS (Model Reference Adaptive System) speed observer suited for linear induction motor (LIM) drives. The voltage and current models of the LIM in the stationary reference frame, taking into consideration the end effects, have been obtained. Then, equations of the induced part have been discretized and rearranged so as to be represented by a linear neural network the TLS EXIN neuron, which has been used to compute the machine linear speed on-line and in recursive form. The proposed NN MRAS observer has been tested experimentally on a suitably developed test setup. Its performance has been also compared to the classic MRAS speed observer.
ROTOR FLUX OPTIMAL ESTIMATION FOR INDUCTION MOTOR CONTROL
2005
Abstract The aim of this paper is to analyze and design reduced order observers of the rotor flux of induction motors. The design requirements are: a) the convergence rate of the rotor flux estimation error; b) a low sensitivity to stator and rotor resistance variations; c) a low sensitivity to errors due to the implementation of the observers on microprocessor-based systems. It is shown that, in order to satisfy the requirements a)-c), it is sufficient to solve a constrained optimization problem according to a criterion in which these requirements appear explicitly. The implementation of the observer is discussed. The observer is tested by simulation and experiments.