Hybrid Genetic Algorithm fuzzy rule based guidance and control for launch vehicle

This paper presents a methodology of designing guidance and control law for four stage launch vehicle. Design of Guidance and Control system for nonlinear dynamic systems is an arduous task and various approaches have been attempted in the past to address coupled system dynamics and nonlinearities. In this paper, the approach of Guidance and Control is exclusively based on fuzzy rule-based mechanism. Since fuzzy logics are more identical to human decision making so, this strategy enhances robustness and reliability in guidance and control mechanism and meet the flight objectives tactfully and manage vehicle energy. For inner loop, fuzzy rule based autopilot is designed which precisely follows the reference pitch attitude profile. The reference pitch profile is constantly reshaped online by a fuzzy rule based guidance to achieve desired altitude. Since reshaping attitude has no great influence on velocity; therefore, an engine shutoff mechanism has been implemented depending on the magnitude of semi major axis during the 3rd and final stage to gain the required orbital velocity. To design optimal fuzzy rule based control and guidance algorithm, the distribution of the membership functions of fuzzy inputs and output are obtained by solving constraint optimization problem using Genetic Algorithms (GA). To get nominal trajectory profiles for proposed scheme, offline trajectory optimization is performed primarily. To acquire optimal AOA profile, Optimization problem is solved by using Genetic Algorithm. To analyze the flight path of the vehicle, a point mass trajectory model is developed. In this model constant thrust and mass flow rate are assumed while the aerodynamic coefficients are calculated by DATCOM. For performance evaluation and validation of proposed guidance and control algorithm, a Six Degree of Freedom software is developed and simulated in SIMULINK. Numerous simulations are conducted to test the proposed scheme for a variety of disturbances and modeling u- certainties.