International audienceThis brief presents a set of experimental results concerning the sliding mode control of an electropneumatic system. Two discrete-time control strategies are considered: an explicit and an implicit (that is very easy to implement with a projection on the interval [−1, 1]) Euler discretizations. While the explicit implementation is known to generate numerical chattering , the implicit one is expected to significantly reduce chattering while keeping the accuracy. The experimental results reported in this brief remarkably confirm that the implicit discrete-time sliding mode supersedes the explicit ones, with several important features: chattering in the control input is almost eliminated (while the explicit and saturated controllers behave like high-frequency bang–bang inputs), the input magnitude depends only on the perturbation size and is independent of the controller gain and sampling time
This chapter presents a set of experimental results concerning the sliding mode control of an electropneumatic system. The controller is implemented via a microprocessor as a discrete-time input. Three discrete-time control strategies are considered for the implementation of the discontinuous part of the sliding mode controller: explicit discretizations with and without saturation, and an implicit discretization (that is very easy to implement as a projection on the interval [−1, 1]). While the explicit implementation is known to generate numerical chattering, the implicit one is expected to significantly reduce chattering while keeping the accuracy. The experimental results reported in this work remarkably confirm that the implicit discrete-time sliding mode supersedes the explicit ones, with several important features: chattering in the control input is almost eliminated (while the explicit and saturated controllers behave like high-frequency bang-bang inputs), the input magnitude depends only on the perturbation size and is "independent" of the controller gain and sampling time. On the contrary the explicit controller shows obvious chattering for all sampling times, its magnitude increases as the controller gain increases, and it does not reduce when the sampling period augments. The tracking errors are comparable for both methods, though the implicit method keeps the precision when the control gain increases, which is not the case for the explicit one. Introducing a saturation in the explicit controller does not allow to significantly improve the explicit controller behaviour if one does not take care of the saturation width.
A robust sliding mode controller for a grid-connected photovoltaic source is proposed in this paper. The objective of the presented control scheme is to force both the output voltage of the photovoltaic PV source and the power factor at the inverter output to follow a certain trajectory reference. The main idea is to apply the robust sliding mode controller directly to the nonlinear state model of the system composed of the PV source and the inverter with its input and output filters. In order to operate the PV system at the maximum power point and to satisfy the environmental factors, such as solar irradiance and temperature, we included a rigorous maximum power point tracker based on an artificial neural network. Simulation results are presented to illustrate the performance of the proposed control scheme. In addition, we show that the grid current satisfies the harmonic limits of the IEEE standard for interconnecting distributed energy sources with electric power systems. KEYWORDS artificial neural network, grid connection, harmonic distortion, maximum power point tracker, photovoltaic source, sliding mode control 1 | INTRODUCTIONRenewable energy sources are candidates for future electric energy due to their independence from fossil and nuclear fuels and given their low impact on the environment. Nowadays, photovoltaic (PV) sources contribute more and more in the generation of electric energy around the world. Photovoltaic power generation converts the sun's irradiation directly into electrical energy. To avoid the problems common in autonomous systems, such as costly and bulky batteries that require regular maintenance, it is necessary to operate the PV system in gridconnected mode; this allows injecting energy excess and covering for demand where production is not sufficient [1].Currently, grid-connected PV systems are the subject of many research works. The most important points of study are: the maximum power point tracking (MPPT) of solar arrays [2][3][4][5], the control of reactive energy injected in the network using suitable control techniques [2,4,5] and the reduction of harmonic pollution of the network by means of appropriate power electronic converters [4,5]. Commonly, grid connected PV sources use two cascaded power electronic converters: a DC-DC converter allows the control of the maximum power point (MPP) of the PV source and a DC-AC converter allows the control of the output voltage and the power factor as well as the reduction of the harmonic pollution of the output current [1,2,6].In [7], a basic proportional-integral (PI) controller is used to investigate the performance of a grid connected PV system by means of two cascaded DC-DC and DC-AC converters. The use of powerful control techniques ---
The aim of this paper is to develop a neuro-fuzzy-sliding mode controller (NFSMC) with a nonlinear sliding surface for a coupled tank system. The main purpose is to eliminate the chattering phenomenon and to overcome the problem of the equivalent control computation. A first-order nonlinear sliding surface is presented, on which the developed sliding mode controller (SMC) is based. Mathematical proof for the stability and convergence of the system is presented. In order to reduce the chattering in SMC, a fixed boundary layer around the switch surface is used. Within the boundary layer, where the fuzzy logic control is applied, the chattering phenomenon, which is inherent in a sliding mode control, is avoided by smoothing the switch signal. Outside the boundary, the sliding mode control is applied to drive the system states into the boundary layer. Moreover, to compute the equivalent controller, a feed-forward neural network (NN) is used. The weights of the net are updated such that the corrective control term of the NFSMC goes to zero. Then, this NN also alleviates the chattering phenomenon because a big gain in the corrective control term produces a more serious chattering than a small gain. Experimental studies carried out on a coupled tank system indicate that the proposed approach is good for control applications.
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