Aerial Robot Arms (ARAs) enable aerial drones to interact and influence objects in various environments. Traditional ARA controllers need the availability of a high-precision model to avoid high control chattering. Furthermore, in practical applications of aerial object manipulation, the payloads that ARAs can handle vary, depending on the nature of the task. The high uncertainties due to modeling errors and an unknown payload are inversely proportional to the stability of ARAs. To address the issue of stability, a new adaptive robust controller, based on the Radial Basis Function (RBF) neural network, is proposed. A three-tier approach is also followed. Firstly, a detailed new model for the ARA is derived using the Lagrange–d’Alembert principle. Secondly, an adaptive robust controller, based on a sliding mode, is designed to manipulate the problem of uncertainties, including modeling errors. Last, a higher stability controller, based on the RBF neural network, is implemented with the adaptive robust controller to stabilize the ARAs, avoiding modeling errors and unknown payload issues. The novelty of the proposed design is that it takes into account high nonlinearities, coupling control loops, high modeling errors, and disturbances due to payloads and environmental conditions. The model was evaluated by the simulation of a case study that includes the two proposed controllers and ARA trajectory tracking. The simulation results show the validation and notability of the presented control algorithm.
In this paper, wavelets were used to study the multivariate fractional Brownian motion through the deviations of the random process to find an efficient estimation of Hurst exponent. The results of simulations experiments were shown that the performance of the proposed estimator was efficient. The estimation process was made by taking advantage of the detail coefficients stationarity from the wavelet transform, as the variance of this coefficient showed the power-low behavior. We use two wavelet filters (Haar and db5) to manage minimizing the mean square error of the model.
Most of the medical datasets suffer from missing data, due to the expense of some tests or human faults while recording these tests. This issue affects the performance of the machine learning models because the values of some features will be missing. Therefore, there is a need for a specific type of methods for imputing these missing data. In this research, the salp swarm algorithm (SSA) is used for generating and imputing the missing values in the pain in my ass (also known Pima) Indian diabetes disease (PIDD) dataset, the proposed algorithm is called (ISSA). The obtained results showed that the classification performance of three different classifiers which are support vector machine (SVM), K-nearest neighbour (KNN), and Naïve B
... Show MoreThe thermal and electrical performance of different designs of air based hybrid photovoltaic/thermal collectors is investigated experimentally and theoretically. The circulating air is used to cool PV panels and to collect the absorbed energy to improve their performance. Four different collectors have been designed, manufactured and instrumented namely; double PV panels without cooling (model I), single duct double pass collector (model II), double duct single pass (model III), and single duct single pass (model IV) . Each collector consists of: channel duct, glass cover, axial fan to circulate air and two PV panel in parallel connection. The temperature of the upper and
... Show MoreIn this work the design and construction of optical pumping system was presented. The parameters of the pumping source to obtain discharge current density sufficient to shift the flash lamp spectrum towards uv portion of spectrum were measured.The current density was supplied to the flash lamp must be greater than 4000Amp./cm2 to obtain the spectral range wavelength lies between 0.2 and 0.35?m. The current density was obtained by a capacitor 50?F, at 7KV discharge voltage. The applied electrical energy to the flash lamp was more than 1200 J, and the current density was around 5000 Amp./cm2.The electrical parameters of the flash lamp were calculated. The impedance parameters(K0) from the voltage and the peak current pulse was measured in ran
... Show MoreIn this work the design and construction of a flash photolysis pulsed HCl laser was presented. The parameters of the pumping source and discharge current density was obtained, which sufficient to shift the flash lamp spectrum towards uv portion of spectrum. The maximum pulse laser energy parameters was measured. Total pressure and ratio of active gases to optimized the output pulse energy were measured , where at 125 mbar of total pressure and 1:7:14 Cl2:H2: He ratio, the laser energy was measured to be 200 mJ at pumping four flash lamps energy in the order of 6400J .The resonator consists of copper a near hemispherical mirror with the radius of curvature 3m coated by gold and reflectivity 98%,the output coupler sapphire mirror of
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Design and construction of video extractor circuit require an understanding of several parameters, which include: Selector circuit, extracting circuit which contains sampling signal and multiplexing. At each radar pulse, the video signal is fed to one of the selector. The fast filter has a pass –band from 190 Hz to 1800 Hz. These frequencies correspond to targets having radial velocities laying between and 10 Kph and 200 Kph.Slow filter: 60 Hz to 230 Hz for radial velocities laying between 3.5 and 13 Kph.The video- extractor is organized in four PCB CG (A-B-C-D) each one having 16 selector. The sampling signal (ADS) (1-2-3-4) control the 4-line-to-16-line decoders. 8 multiplexers of 8 inputs each, are required for the multiplexing of the
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