Multi-objective genetic algorithm-based sliding mode control for assured crew reentry vehicle

Abstract

A reentry control system is proposed for an assured crew reentry vehicle (ACRV), where the control law is tuned using multi-objective genetic algorithm-based sliding mode controller. The controller designed guarantees the robustness properties with respect to parametric uncertainties and other disturbances. The system state remains in the neighborhood of a reference attitude and the control signal is close to a well-defined equivalent control. The amplitude of the sliding mode controller is tuned using an evolutionary optimization technique, i.e., Genetic algorithm. Multi-objective optimizer is used for the controller as it is to minimize the error in the Bank Angle (degree), Angle of Attack (degree), and Sideslip Angle (degree). The reference attitude is obtained in terms of the outputs given by the trajectory controller and the navigational system. A pulse width pulse frequency (PWPF) modulator is designed to modulate the attitude controller through the thrust torque developed. The simulation results show the effectiveness of the proposed method.