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This paper proposes a path planning and intelligent control of mobile robot in unknown environment with static/dynamic obstacles and fixed target. A radial basis function (RBF) network approach is proposed in this work for obtaining optimized path to reach the goal without collision. The competency of the proposed approach is verified by means of simulation results using MATLAB where robot moves in a variety of environments with obstacles of different shapes and sizes.

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The increasing use of power electronics devices as well as the integration of renewable source-based microgrids (MG) has seriously affects the power quality (PQ) of the three-phase power system. Therefore, for the improvement of PQ, it is required to reduce the total harmonics distortion (THD) in the utility network. In this work, the improvement of PQ is discussed in a photovoltaic (PV) based MG integrated three-phase system using a three-level H-bridge (3LHB) multilevel inverter (MI). The MI is used for compensating the source current harmonics and reducing the THD by meeting the IEEE standard guidelines. Besides, the proposed model helps manage the reactive power with control of DC link bus voltage through the PV system. The proposed model is helpful not only in reducing the harmonics but also in providing additional active power to the load if any electrical disturbances occur on the grid side. The maximum power point tracking (MPPT) technique employed in PV is of an improved form of Perturb and Observe (P&O). Further, the reference current generation is derived using the direct current control (DCC) and indirect current control (ICC) techniques. The MG integrated MI is investigated in both DCC and ICC method using three different DC bus voltage controllers; proportional-integral (PI), fuzzy logic controller (FLC), and fuzzy sliding mode control (FSMC). The proposed microgrid integrated system is analyzed with the MATLAB/Simulink tool.

An autonomous mobile robot is a robot which can move and act autonomously without the help of human assistance. Navigation problem of mobile robot in unknown environment is an interesting research area. This is a problem of deducing a path for the robot from its initial position to a given goal position without collision with the obstacles. Different methods such as fuzzy logic, neural networks etc. are used to find collision free path for mobile robot. This paper examines behavior of path planning of mobile robot using three activation functions of wavelet neural network i.e. Mexican Hat, Gaussian and Morlet wavelet functions by MATLAB. The simulation result shows that WNN has faster learning speed with respect to traditional artificial neural network.

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