Enhancing performance and stability of gain-scheduling control system using evolutionary algorithms: A case study on transport aircraft

Elkhatem A. S., ENGİN Ş. N.

EXPERT SYSTEMS WITH APPLICATIONS, vol.213, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 213
  • Publication Date: 2022
  • Doi Number: 10.1016/j.eswa.2022.118859
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Computer & Applied Sciences, INSPEC, Metadex, Public Affairs Index, Civil Engineering Abstracts
  • Keywords: Smooth adaptation mechanism, Gain scheduling flight control, Evolutionary algorithms, Non -dominated optimal solutions, Longitudinal motion of B747, TAKAGI-SUGENO, LPV CONTROL, IDENTIFICATION, OPTIMIZATION
  • Yıldız Technical University Affiliated: Yes


An enhanced gain-scheduling control strategy was presented to establish a smooth adaptation mechanism for the longitudinal motion of B747 between different flight envelopes. The focal objective is to accomplish a smooth and non-conservative gain-scheduling control system that has high performance and stability characteristics for different flight conditions and transitions between them. The proposed approach ensured a successful gain scheduling control system by allocating a specific error performance index that should be minimized for all flight conditions. Then all required performance indices were cohered according to the flight envelope in the form of a multi-objective optimization problem that could be solved using evolutionary algorithms. The performance and stability of the transition points between different flight conditions were preserved through the sets of feasible solutions obtained by an evolutionary algorithm which are known as a set of Pareto optimal solutions. The proposed gain-scheduling system was built based on 2DoF-PID (Two-degree-of-freedom Proportional Integral Derivative) controller. An auto-decision-maker was developed to adjust the parameters of the 2DoF-PID gainscheduling control system with the current flight condition. The effectiveness of the proposed gain-scheduling control structure was studied for two different multiobjective evolutionary-based optimizers known as fast sorting and elite multi-objective genetic algorithm.II (NSGA.II), and sub-population genetic algorithm.II (SPGA. II). The proposed methodology was evaluated by simulation results according to the quality of the pareto-front and transient response characteristics of the proposed gain-scheduling controller for the chosen flight conditions in normal flight conditions and 50% loss of actuator effectiveness.