Internal Model Principle: extension to the switching case and applications

This thesis deals with a novel control approach based on the extension of the well-known Internal Model Principle to the case of periodic switched linear exosystems. This extension, inspired by power electronics applications, aims to provide an effective design method to robustly achieve the asymptotic tracking of periodic references with an infinite number of harmonics. In the first part of the thesis the basic components of the novel control scheme are described and preliminary results on stabilization are provided. In the second part, advanced control methods for two applications coming from the world high energy physics are presented.

Development of innovative robust stability enhancement algorithms for distribution systems containing distributed generators

This project was a step forward in improving the voltage profile of traditional low voltage distribution networks with high photovoltaic generation or high peak demand. As a practical and economical solution, the developed methods use a Dynamic Voltage Restorer or DVR, which is a series voltage compensator, for continuous and communication-less power quality enhancement. The placement of DVR in the network is optimised in order to minimise its power rating and cost. In addition, new approaches were developed for grid synchronisation and control of DVR which are integrated with the voltage quality improvement algorithm for stable operation.

Controle Adaptativo Robusto para Filtros Ativos de Potência Paralelo

Conventional control strategies used in shunt active power filters (SAPF) employs real-time instantaneous harmonic detection schemes which is usually implements with digital filters. This increase the number of current sensors on the filter structure which results in high costs. Furthermore, these detection schemes introduce time delays which can deteriorate the harmonic compensation performance. Differently from the conventional control schemes, this paper proposes a non-standard control strategy which indirectly regulates the phase currents of the power mains. The reference currents of system are generated by the dc-link voltage controller and is based on the active power balance of SAPF system. The reference currents are aligned to the phase angle of the power mains voltage vector which is obtained by using a dq phase locked loop (PLL) system. The current control strategy is implemented by an adaptive pole placement control strategy integrated to a variable structure control scheme (VS-APPC). In the VS-APPC, the internal model principle (IMP) of reference currents is used for achieving the zero steady state tracking error of the power system currents. This forces the phase current of the system mains to be sinusoidal with low harmonics content. Moreover, the current controllers are implemented on the stationary reference frame to avoid transformations to the mains voltage vector reference coordinates. This proposed current control strategy enhance the performance of SAPF with fast transient response and robustness to parametric uncertainties. Experimental results are showing for determining the effectiveness of SAPF proposed control system

Estratégia de controle robusto para filtro ativo paralelo sem detecção de harmônicos de correntes

Conventional control strategies used in shunt active power filters (SAPF) employs real-time instantaneous harmonic detection schemes which is usually implements with digital filters. This increase the number of current sensors on the filter structure which results in high costs. Furthermore, these detection schemes introduce time delays which can deteriorate the harmonic compensation performance. Differently from the conventional control schemes, this paper proposes a non-standard control strategy which indirectly regulates the phase currents of the power mains. The reference currents of system are generated by the dc-link voltage controller and is based on the active power balance of SAPF system. The reference currents are aligned to the phase angle of the power mains voltage vector which is obtained by using a dq phase locked loop (PLL) system. The current control strategy is implemented by an adaptive pole placement control strategy integrated to a variable structure control scheme (VS¡APPC). In the VS¡APPC, the internal model principle (IMP) of reference currents is used for achieving the zero steady state tracking error of the power system currents. This forces the phase current of the system mains to be sinusoidal with low harmonics content. Moreover, the current controllers are implemented on the stationary reference frame to avoid transformations to the mains voltage vector reference coordinates. This proposed current control strategy enhance the performance of SAPF with fast transient response and robustness to parametric uncertainties. Experimental results are showing for determining the effectiveness of SAPF proposed control system

Impedance control and internal model use during the initial stage of adaptation to novel dynamics in humans.

This study investigated the neuromuscular mechanisms underlying the initial stage of adaptation to novel dynamics. A destabilizing velocity-dependent force field (VF) was introduced for sets of three consecutive trials. Between sets a random number of 4-8 null field trials were interposed, where the VF was inactivated. This prevented subjects from learning the novel dynamics, making it possible to repeatedly recreate the initial adaptive response. We were able to investigate detailed changes in neural control between the first, second and third VF trials. We identified two feedforward control mechanisms, which were initiated on the second VF trial and resulted in a 50% reduction in the hand path error. Responses to disturbances encountered on the first VF trial were feedback in nature, i.e. reflexes and voluntary correction of errors. However, on the second VF trial, muscle activation patterns were modified in anticipation of the effects of the force field. Feedforward cocontraction of all muscles was used to increase the viscoelastic impedance of the arm. While stiffening the arm, subjects also exerted a lateral force to counteract the perturbing effect of the force field. These anticipatory actions indicate that the central nervous system responds rapidly to counteract hitherto unfamiliar disturbances by a combination of increased viscoelastic impedance and formation of a crude internal dynamics model.

Robust Learning Control for Manipulators with Uncertainty Disturbances Based on Internal Model

In this paper, a disturbance controller is designed for making robotic system behave as a decoupled linear system according to the concept of internal model. Based on the linear system, the paper presents an iterative learning control algorithm to robotic manipulators. A sufficient condition for convergence is provided. The selection of parameter values of the algorithm is simple and easy to meet the convergence condition. The simulation results demonstrate the effectiveness of the algorithm..

Internal model control for shape memory alloy actuators using fuzzy based Preisach model

This paper investigates a possible application of Preisach model to control shape memory alloy (SMA) actuators using an internal model control strategy. The developed strategy consists in including the Preisach hysteresis model of SMA actuator and the inverse Preisach model within the control structure. In this work, an extrema input hystory and a fuzzy inference is utilized to replace the classical Preisach model. This allows to reduce a large amount of experimental parameters and computation time of the classical Preisach model. To demonstrate the effectiveness of the proposed controller in improving control performance and hysteresis compensation of SMA actuators, experimental results from real time control are presented.

Inverting recurrent neural networks for internal model control of nonlinear systems

In this paper, we show how a set of recently derived theoretical results for recurrent neural networks can be applied to the production of an internal model control system for a nonlinear plant. The results include determination of the relative order of a recurrent neural network and invertibility of such a network. A closed loop controller is produced without the need to retrain the neural network plant model. Stability of the closed-loop controller is also demonstrated.