This article presents a new cascaded extended state observer (CESO)-based sliding-mode control (SMC) for an underactuated flexible joint robot (FJR). The control of the FJR has many challenges, including coupling, underactuation, nonlinearity, uncertainties and external disturbances, and the noise amplification especially in the high-order systems. The proposed control integrates the CESO and SMC, in which the CESO estimates the states and disturbances, and the SMC provides the system robustness to the uncertainty and disturbance estimation errors. First, a dynamic model of the FJR is derived and converted from an underactuated form to a canonical form via the Olfati transformation and a flatness approach, which reduces the complexity of th

An Intelligent Internet of Things network based on an Artificial Intelligent System, can substantially control and reduce the congestion effects in the network. In this paper, an artificial intelligent system is proposed for eliminating the congestion effects in traffic load in an Intelligent Internet of Things network based on a deep learning Convolutional Recurrent Neural Network with a modified Element-wise Attention Gate. The invisible layer of the modified Element-wise Attention Gate structure has self-feedback to increase its long short-term memory. The artificial intelligent system is implemented for next step ahead traffic estimation and clustering the network. In the proposed architecture, each sensing node is adaptive and able to

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’A

Thermal conductivity measurement was done for specimens of Polystyrene/ titanium dioxide, Polycarbonate/ titanium dioxide and Polymethylmetha acrylate/ titanium dioxide composites for weight ratio of 1.9/ 0.1 and 1.8/ 0.2 wt% for different thickness of the samples. The experimental results show that the thermal conductivity is increased with the increasing of thickness of layers and with the weight ratio of TiO2

Thin films of pure polycarbonate (PC) with anthracene doping PC films for different doping ratios (10, 20, 30, 40, 50 and 60 ml) were prepared by using a casting method. The influence of anthracene doping ratio on photo-fries rearrangement of polycarbonate was systematic investigated. Furthermore, pure PC and anthracene doping PC films were irradiated via UV light at a wavelength (254 nm) for different periods (5, 240, 288, and 360 hrs). The photo-fries rearrangement occurring in pure PC and anthracene doping PC films were monitored using UV and FTIR spectroscopies. The photo-fries rearrangement leads to scission the carbonate linkage and formation phenylsalicylate and dihydroxybenzophenes. The result of the UV spectrum confirms disappea

A freshwater bivalve plays a crucial function in aquatic habitats as the filtered water and burrowing mussels mix the sediment, thus increasing oxygen content and making the ecosystem healthier. The aim of the study is to see how chlorpyrifos affects biochemical markers in freshwater mussel Unio tigridis. About 180 individuals per taxon and water samples were collected from the Qandil water resource on the Greater Zab River, Erbil Province, Iraq. Once arrived at the lab, the individuals were kept in aquaria with river water and an air-conditioned room Temperature: 25±2 and Light: 12h/12h and acclimatized to laboratory conditions for seven days in aged tap water. The mussel's identification molecularly and the DNA sequence of t

It is well known that drilling fluid is a key parameter for optimizing drilling operations, cleaning the hole, and managing the rig hydraulics and margins of surge and swab pressures. Although the experimental works represent valid and reliable results, they are expensive and time consuming. In contrast, continuous and regular determination of the rheological fluid properties can perform its essential functions during good construction. The aim of this study is to develop empirical models to estimate the drilling mud rheological properties of water-based fluids with less need for lab measurements. This study provides two predictive techniques, multiple regression analysis and artificial neural networks, to determine the rheological