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This paper introduces an adaptive backstepping control law for a two-wheel electric scooter (eScooter) with a nonlinear uncertain model. Adaptive backstepping control is integrated with feedback control that satisfies Lyapunov stability. By using the recursive structure to find the controlled function and estimate uncertain parameters, an adaptive backstepping method allows us to build a feedback control law that efficiently controls a self-balancing controller of the eScooter. Additionally, a controller area network (CAN bus) with high reliability is applied for communicating between the modules of the eScooter.Simulation and experimental results demonstrate the robustness and good performance of the proposed adaptive backstepping control.

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The Fe3O4/Talc nanocomposite was synthesized by the coprecipitation-ultrasonication method. The reaction was carried out under a inert gas environment. The nanoparticles were characterized by X-ray diffraction (XRD), field-emission scanning electron microscopy (FESEM), fourier-transform infrared spectroscopy (FT-IR) and vibrating sample magnetometry techniques (VSM), the surface area of the nanoparticles was determined to be 77.92 m2/g by Brunauer-Emmett-Teller method (BET). The kinetic data showed that the adsorption process fitted with the pseudo-second order model. Batch experiments were carried out to determine the adsorption kinetics and mechanisms of Cr(VI) by Fe3O4/Talc nanocomposite. The adsorption process was found to be highly pH-dependent, which made the material selectively adsorb these metals from aqueous solution. The isotherms of adsorption were also studied using Langmuir and Freundlich equations in linear forms. It is found that the Langmuir equation showed better linear correlation with the experimental data than the Freundlich. The thermodynamics of Cr(VI) adsorption onto the Fe3O4/Talc nanocomposite indicated that the adsorption was exothermic. The reusability study has proven that Fe3O4/Talc nanocomposite can be employed as a low-cost and easy to separate.

This manuscript introduces a new adaptive inverse neural (AIN) control method applied to precisely track the piezoelectric (PZT) actuator displacement.First, a 3-layer neural network optimized by the enhanced differential evolution technique which modifies a mutation scheme and provides suggestions for selecting mutant coefficient F, crossover coefficient CR, and population size NP, is used to identify the inverse nonlinearity hysteresis structure of the PZT actuator. Second, a feed-forward control based on the identified model is proposed to compensate for the PZT hysteresis effect. Third, the Lyapunov stability principle is used to design and implement an adaptive law-based neural sliding mode model plus the feed-forward compensator to ensure that the whole PZT plant is operated in asymptotical stability. The experiment results demonstrate the proposed AIN controller proves superiority in comparison with other advanced control methods. K E Y W O R D S adaptive inverse neural controller, back-propagation, enhanced differential evolution, hybrid adaptive inverse neural control, Lyapunov stability concept

A new enhanced adaptive fuzzy sliding mode control approach is proposed in this article with its good availability for application in control of a highly uncertain nonlinear two-link pneumatic artificial muscle manipulator. Stability demonstration of the robust convergence of the closed-loop pneumatic artificial muscle manipulator system based on a novel enhanced adaptive fuzzy sliding mode control is experimentally proved using Lyapunov stability theorem. Obtained result confirms that the new enhanced adaptive fuzzy sliding mode control method, applied to the two-link uncertain nonlinear pneumatic artificial muscle manipulator system, is fully investigated with better robustness and precision than the standard sliding mode control and fuzzy sliding mode control techniques.