This article presents a tracking control design for two-link robot manipulators. To achieve robust tracking control performance, a super-twisting sliding mode control (STSMC) is derived. The stability of the system based on the proposed approach is proved based on the Lyapunov theorem. However, one problem with the designed STSMC is to properly set its parameters during the design. Therefore, it is proposed a social spider optimization (SSO) to tune these design parameters to improve the dynamic performance of the robot manipulator controlled considering STSMC. The performance of the STSMC approach based on SSO is compared to that based on particle swarming optimization (PSO) in terms of dynamic performance and robustness characteristics. The effectiveness of the proposed optimal controllers is verified by simulations within the MATLAB software. It is verified that the performance given by SSO-based STSMC outperforms that resulting from PSO-based STSMC. The experimental results are conducted based on LabVIEW 2019 software to validate the numerical simulation.
The surveillance and security of areas such as home, laboratory, office, factory, and airports, are important to prevent any threatening to human lives. Mobile robots are proven their effectiveness in a large number of applications, especially in hazardous areas where they can be remotely controlled by humans to accomplish certain tasks. This research paper presents a design and implementation of a mobile robot for surveillance and security applications. The main objective of the design is to lower the cost and the power consumption of the mobile robot which accomplish using low-cost open-source hardware such as Arduino and Raspberry Pi. The robot is connected wirelessly via a low-power ZigBee module to the control station to allow the operator for controlling the mobile robot motions and monitoring the physical events in the environment where the robot is used. Sensors such as camera, temperature, and range are embedded in the robot to sense and monitor human motion, the room temperature, and the distance of the surrounding obstacles. The testing of the implemented mobile robot shows that it can run continuously for approximately 6.5 hours at a motor shaft speed 25 rpm of unlit the need to recharge the battery.
This paper presents a comparison between two nonlinear PID controllers, the first is the Neural based controller and the second is the nonlinear fractional order PID controller (FOPID) for a trajectory tracking control of a non-holonomic two wheeled mobile robots (2-WMR). A modified particle swarm optimization (MPSO) has been proposed in this work to tune the parameters of the nonlinear FOPID controller to design the controller so that the 2-WMR follows exactly a predefined continuous track. The kinematic model of a differential drive 2-WMR has been derived to simulate the behavior of the 2-WMR and it is used in the design and simulations of the proposed FOPID controller. From simulation and results, it can be seen that the efficiency of the proposed nonlinear FOPID controller outperforms the nonlinear integer order PID controller; this is proved by the minimized tracking error and the speed control signals obtained.
This work presents proposed methodsfor short term power load forecasting (STPLF) for the governorate of Baghdad using two different models of Artificial Neural Networks (ANNs). The two models used in this work are the multi-layer perceptron (MLP) model trained with Levenberg-Marquardt Back Propagation (BP) algorithm and Radial Basis Function (RBF) neural network. Inputs to the ANN are thepast loadsvalues and the output of the ANN is the load forecast for the weekends of certain months for Baghdad governorate. The data is divided into two parts where half of them was used for training and the other half was used for testing the ANN. Simulations were achieved by MATLAB software with the aid of Neural networks toolbox, where the data obtained for the Iraqi national grid were rearranged and preprocessed. Finally, the simulations results showed that the forecasted load values for the Baghdad governorate by the proposed methods were very close to actual ones as compared with the traditional methods.
General TermsLoad demand, neural networks, load prediction, artificial intelligence, energy consumption.
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