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 overcom...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.展开更多
This paper introduces a recursive identification methods toolbox(called RIM) running under Matlab environment for dynamic system identification from available data. The RIM includes many methods which are generally us...This paper introduces a recursive identification methods toolbox(called RIM) running under Matlab environment for dynamic system identification from available data. The RIM includes many methods which are generally used. The RIM helps users to validate the theoretical results and to carry out comparison between identifications methods without the need of algorithms programming. Furthermore, the RIM can be used as an education platform to study the identification parameters effect on model validity and results accuracy. To show its performance and capability, the RIM is evaluated through many application examples.展开更多
Hybrid systems based on renewable energies for the electrification of remote sites controlled by power management systems(PMSs)aim to reduce fossil fuels and increase the efficiency of renewable energy sources to mini...Hybrid systems based on renewable energies for the electrification of remote sites controlled by power management systems(PMSs)aim to reduce fossil fuels and increase the efficiency of renewable energy sources to minimize greenhouse gas emissions.The influential role of the PMS contributes to improving the efficiency and effectiveness of these systems by ensuring a balance between the different sources and loads in all operating modes.However,the abrupt transitions between the various operational modes selected by the PMS generate power loss and imbalance.To handle this issue,a fuzzy logic controller(FLC)-based PMS controlling a photovoltaic(PV)and diesel hybrid system with a battery storage element connected to a DC bus is proposed in this paper.The proposed PMS is wholly based on FLC to ensure a smooth transition between the different modes of the system.The success of using the suggested PMS lies in how well the FLC parameters are chosen before the system is processed.For this purpose,the particle swarm optimization algorithm is adapted to tune the FLC parameters.The resulting optimal intelligent PMS is tested and compared with a classical one using comprehensive simulations performed in a Simscape ElectricalTM MATLAB®environment.The obtained results show an overshoot attenuation at the DC-bus voltage of 2%when changing the mode and an improvement in the PV generator efficiency by 99.5%.展开更多
文摘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.
文摘This paper introduces a recursive identification methods toolbox(called RIM) running under Matlab environment for dynamic system identification from available data. The RIM includes many methods which are generally used. The RIM helps users to validate the theoretical results and to carry out comparison between identifications methods without the need of algorithms programming. Furthermore, the RIM can be used as an education platform to study the identification parameters effect on model validity and results accuracy. To show its performance and capability, the RIM is evaluated through many application examples.
文摘Hybrid systems based on renewable energies for the electrification of remote sites controlled by power management systems(PMSs)aim to reduce fossil fuels and increase the efficiency of renewable energy sources to minimize greenhouse gas emissions.The influential role of the PMS contributes to improving the efficiency and effectiveness of these systems by ensuring a balance between the different sources and loads in all operating modes.However,the abrupt transitions between the various operational modes selected by the PMS generate power loss and imbalance.To handle this issue,a fuzzy logic controller(FLC)-based PMS controlling a photovoltaic(PV)and diesel hybrid system with a battery storage element connected to a DC bus is proposed in this paper.The proposed PMS is wholly based on FLC to ensure a smooth transition between the different modes of the system.The success of using the suggested PMS lies in how well the FLC parameters are chosen before the system is processed.For this purpose,the particle swarm optimization algorithm is adapted to tune the FLC parameters.The resulting optimal intelligent PMS is tested and compared with a classical one using comprehensive simulations performed in a Simscape ElectricalTM MATLAB®environment.The obtained results show an overshoot attenuation at the DC-bus voltage of 2%when changing the mode and an improvement in the PV generator efficiency by 99.5%.
