Multi-Parameter Optimization of Sliding-Mode Controller for Quadcopter Application

For many years, quadcopters are quite popular in the academic field because of its structural simplicity. However, this property comes out the problem of designing an effective controller. Designing a controller for quadcopter is rather complicated because tuning of the controller parameters of multi-rotor structure to achieve a desired performance for agility, flying efficiency and immediate reaction is a challenging problem. To deal with such a difficulty, Ant Colony Optimization (ACO), Invasive Weed Optimization (IWO) and Firefly Optimization (FO) algorithms are used to obtain optimal parameters of Sliding Mode Controller (SMC). SMC is used for both attitude and position control of the quadcopter. By taking into consideration all six variables with different number of parameters (total number of parameters to be optimized are nineteen). This makes it a complicated tuning problem. In this numerical study, performance results of optimization algorithms are compared with respect to convergence rate and cost function