In this paper, a single link flexible joint robot is used to evaluate a tracking trajectory control and vibration reduction by a super-twisting integral sliding mode (ST-ISMC). Normally, the system with joint flexibility has inevitably some uncertainties and external disturbances. In conventional sliding mode control, the robustness property is not guaranteed during the reaching phase. This disadvantage is addressed by applying ISMC that eliminates a reaching phase to ensure the robustness from the beginning of a process. To design this controller, the linear quadratic regulator (LQR) controller is first designed as the nominal control to decide a desired performance for both tracking and vibration responses. Subsequently, discontinuous control was traditionally built by ISMC with a constant reaching law to reject the uncertainties and disturbances acted in the system. To avoid the chattering phenomenon that appears in the classical control law of ISMC, super-twisting is used here instead of constant reaching law. Finally, the comparative assessment is accomplished in order to confirm the superiorities of proposed method. Numerical simulation shows the effectiveness of STISMC over LQR and ISMC in terms of the tracking responses, robustness achievement and chattering reduction.
In this work, a new development of predictive voltage-tracking control algorithm for Proton Exchange Membrane Fuel Cell (PEMFCs) model, using a neural network technique based on-line auto-tuning intelligent algorithm was proposed. The aim of proposed robust feedback nonlinear neural predictive voltage controller is to find precisely and quickly the optimal hydrogen partial pressure action to control the stack terminal voltage of the (PEMFC) model for N-step ahead prediction. The Chaotic Particle Swarm Optimization (CPSO) implemented as a stable and robust on-line auto-tune algorithm to find the optimal weights for the proposed predictive neural network controller to improve system performance in terms of fast-tracking de
... Show MoreIn this research, a study is introduced on the effect of several environmental factors on the performance of an already constructed quality inspection system, which was designed using a transfer learning approach based on convolutional neural networks. The system comprised two sets of layers, transferred layers set from an already trained model (DenseNet121) and a custom classification layers set. It was designed to discriminate between damaged and undamaged helical gears according to the configuration of the gear regardless to its dimensions, and the model showed good performance discriminating between the two products at ideal conditions of high-resolution images. So, this study aimed at testing the system performance at poo
... Show MoreLafutidine (LAF) a newly developed histamine H2-receptor antagonist with absorption window makes it a good candidate to be prepared as floating drug delivery system. The current study involves formulation and in- Vitro evaluation of lafutidine as floating microspheres. Different formulation variables that affect the physicochemical properties of the prepared microspheres besides to the drug release behavior were investigated. Fourteen formulas were prepared by emulsion (o/w) solvent evaporation method using Ethyl cellulose (EC) as the polymeric matrix and tween 80 (TW80) as an emulsifying agent. The prepared formulas were evaluated for their percentage buoyancy (%), Percentage yield (%) and Entrapment efficiency (EE %). The results obt
... Show MoreIn this research, a study is introduced on the effect of several environmental factors on the performance of an already constructed quality inspection system, which was designed using a transfer learning approach based on convolutional neural networks. The system comprised two sets of layers, transferred layers set from an already trained model (DenseNet121) and a custom classification layers set. It was designed to discriminate between damaged and undamaged helical gears according to the configuration of the gear regardless to its dimensions, and the model showed good performance discriminating between the two products at ideal conditions of high-resolution images.
So, this study aimed at testing the system performance at poor s
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