Development of position tracking of BLDC motor using adaptive fuzzy logic controller

The brushless DC (BLDC) motor has many advantages including simple to construct, high torque capability, small inertia, low noise and long life operation. Unfortunately, it is a non-linear system whose internal parameter values will change slightly with different input commands and environments. In this proposed controller, Takagi-Sugeno-Kang method is developed. In this project, a FLC for position tracking and BLDC motor are modeled and simulated in MATLAB/SIMULINK. In order to verify the performance of the proposed controller, various position tracking reference are tested. The simulation results show that the proposed FLC has better performance compare the conventional PID controller

Recent research in cooperative path planning algorithms for multi-agent using mixed- integer linear programming

Path planning is one of the issues to be handled in the development of autonomous systems. For a group of agents, cooperative path planning is crucial to ensure that a given mission is accomplished in the shortest time possible with optimal solution. Optimal means that the resulting path has minimal length hence the total consumed energy by the agents is the least. Cooperative path planning fuses information from all agents to plan an optimal path. There are a number of cooperative path planning methods available in the literature for multi-agent including Cell Decomposition, Roadmap and Potential Field to name but three. This paper will review and compare the performances of those existing methods that can find solution without graph search algorithm such as Mixed-Integer Linear Programming (MILP) techniques which exactly solves the problem and then propose four alternative MILP formulations which are computationally less intensive and suited for real-time purposes, but yield a theoretically guaranteed suboptimal solution

Efficient robotic path planning algorithm based on artificial potential field

Path planning is crucial for a robot to be able to reach a target point safely to accomplish a given mission. In path planning, three essential criteria have to be considered namely path length, computational complexity and completeness. Among established path planning methods are voronoi diagram (VD), cell decomposition (CD), probability roadmap (PRM), visibility graph (VG) and potential field (PF). The above-mentioned methods could not fulfill all three criteria simultaneously which limits their application in optimal and real-time path planning. This paper proposes a path PF-based planning algorithm called dynamic artificial PF (DAPF). The proposed algorithm is capable of eliminating the local minima that frequently occurs in the conventional PF while fulfilling the criterion of path planning. DAPF also integrates path pruning to shorten the planned path. In order to evaluate its performance, DAPF has been simulated and compared with VG in terms of path length and computational complexity. It is found that DAPF is consistent in generating paths with low computation time in obstacle-rich environments compared to VG. The paths produced also are nearly optimal with respect to VG

Energy efficient elliptical concave visibility graph algorithm for unmanned aerial vehicle in an obstacle-rich environment

This paper proposes a path planning algorithm for unmanned aerial vehicle (UAV) called Elliptical Concave Visibility Graph (ECoVG). The algorithm, which is based on visibility graph (VG), overcomes the limitations of VG computation time and hence, it can be applied in real-time and in obstacle-rich environments. An experimental investigation has been done to compare the performance between ECoVG and another VG based method namely Equilateral-Space Oriented VG (ESOVG) in terms of computational time and path length. The investigation was done in identical scenarios through simulation to show that the ECoVG has a better computation time than that of ESOVG for its efficient selection of a region in calculating the path. It is also found that the proposed algorithm is energy efficient and complete since it can find a path if one exists