Quadcopter pid controller design and path planning using bio-inspired meta-heuristic algorithms

The usage of Quadcopter in commercial fields has evolved significantly due to its phenomenal development. However, controlling the movements of a Quadcopter is a demanding task due to its complex dynamics. The usage of the Proportional-Integral-Derivative(PID) controller for stability control is quite challenging in regards to the complexity of Quadcopter’s nonlinear structure. Conventional methods like Ziegler-Nichols(ZN) for tuning the PID controller for a Quadcopter do not provide efficient performance and might also cause the system to be severely damaged. In this thesis, we are addressing the problem of the controlling a Quadcopter using Metaheuristic-based PID controller. Multi-Objective Fitness Function is proposed to reduce the overall time of the step response effectively. Path planning is one of the important concepts for a Quadcopter to move from one point to another point effectively. A novel Neighborhood Search Genetic Algorithm (NSGA) is presented for path planning by balancing the diversity inside the Genetic Algorithm using a Neighborhood Search to produce an efficient path. The performance of the NSGA has been compared to traditional A∗ , standard GA, and PSO. NSGA produced superior results in terms of cost.