Genetic-algorithm-based control allocation for multi-surface aircrafts

In order to improve the reliability and viability, advanced aircraft is equipped with abundant multiple control surfaces. Control allocation is utilized to assign the virtual control torque to these redundant control surfaces. Due to physical and aerodynamic factors, there are some constraints impacting on each control surface, which makes the control allocation problem become more complex. In this paper, a weighted pseudo-inverse based control allocation algorithm is presented. Although the weighted pseudo-inverse method is simple and efficient, this control allocation algorithm cannot reach the optimal allocation achievement. For obtaining the maximum attainable moment set, genetic algorithm is employed to train the weighted matrices in this paper. In order to improve performances of the method, the space of the expected moment is divided into multiple parts, and the genetic algorithm is used to find the optimal weighted matrices for each part. Compared with a single weighted matrix, multiple weighted matrices can ensure that the proposed algorithm performs better in each part. In order to verify the validity of this algorithm, the direct allocation method is employed as a comparison. Simulations demonstrate and verify that performances of the new method is better than those of the conventional weighted pseudo-inverse method.