HGAPSO-Based Third Order-SMC, ST-SMC, and SMC Strategy for AAV Control: A Comparative Analysis

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Abstract
The study of the control of quadrotors has been addressed extensively in different UAV research areas. In this work, the nonlinear dynamic model of the quadrotor has been developed using the Newton–Euler technique. By considering the system's under-actuated and strongly coupled characteristics, the controllers have been then designed. As a main objective, design and comparative analysis of conventional sliding mode controller (SMC), supper twisting sliding mode controller (ST-SMC), and third order sliding mode controller (TO-SMC) schemes are realized for a nonlinear model of quadrotor. The controller's performance is highly dependent on the sliding surface coefficients and the controller constants. Therefore, a hybrid type of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), HGAPSO, has been formulated to find the best controllers’ parameters. The effectiveness of GA, PSO, and HGAPSO are investigated with TO-SMC in terms of trajectory tracking and ro-bustness. The control algorithms are focused on the position, altitude, and attitude states manipulated by four control inputs. The performance comparison of the controllers is evaluated under the effect of wind disturbance. Model development and simulation of the proposed system are carried out using MATLAB Simulink and a set of simulations are conducted to illustrate the states and analysis the performance of the proposed controllers. Finally, the HGAPSO tuned TO-SMC has performance supremacy in trajectory tracking, chattering elimination, settling time, rise time, ITAE minimization, and robustness over HGAPSO tuned ST-SMC and SMC. In addition, the HGAPSO reveals attractive performance over PSO and GA alone.
Funding Information
  • Bahir Dar Institute of Technology

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