Improved African Vulture Optimization Algorithm for Parameter Optimization of Mars Rotorcraft Attitude Controllers

To achieve Mars rotorcraft attitude control with improved control accuracy and immunity, an improved African vulture optimization algorithm (IAVOA) is proposed and applied to the attitude controller parameter adjustment. Considering the coupling effect of control inputs and the uncertainty of the Mars environment, the attitude is controlled using an active disturbance rejection controller after decoupling the model using state feedback. In order to obtain the optimal parameters of the controller, the optimization algorithm is improved by using tent mapping initialization, opposite learning strategy, memory search strategy, weight time-varying mechanism, Cauchy Mutation, and attraction mechanism for the problem that the AVOA (African vulture optimization algorithm algorithm) is prone to fall into local optimal solutions and slow convergence of the algorithm. The simulation is verified with the ITAE (Integral of Time-weighted Absolute Error) index as the optimization target and compared with PSO (Particle Swarm Optimization), CSA (Crow Search Algorithm), and AVOA. The results demonstrate that the IAVOA converges faster, finds better parameters, and improves the control accuracy and immunity to disturbance.