ABSTRACT
This research is aimed at the development of a modified fruit fly optimization algorithm (mFFOA) for the determination of optimized weighting matrices (Q which is a positive definite matrix that penalizes the states and R which is also a positive definite matrix that penalizes the control inputs) of the linear quadratic regulator (LQR) to be used for the aircraft pitch control system (PCS).The standard fruit fly optimization algorithm (FFOA) is an optimization algorithm inspired by intelligent smell and vision behaviour of flies towards fruit. The FFOA suffers from the problem of lack of balance between exploration and exploitation as it has a higher rate of exploitation than exploration leading to a high probability of it being trapped in some local optimal. The mFFOA was developed by modifying the iteration factor of the search radius to a decreasing function to improve the exploration capability of the algorithm and then by introducing a linearly decreasing inertial weight in order to provide an efficient balance between exploration and exploitation of the FFOA. The mFFOA was benchmarked against the FFOA using ten optimization test functions (Ackley, Alpine, Eggcrate, Griewank, Pathologic, Rastrigrin, Rosenbrock, Schaffer, Sphere, and Whitley) and showed a 20% improvement in its convergence to global optima. The optimized values of the Q and R weighting matrices are obtained for ten test runs using mFFOA within a time of 126.9538s when compared with the 140.7819s taken using the FFOA approach. The proposed method reduced the time taken by the FFOA by 13.8281s.These matrices used to determine the LQR controller for the PCS showed a settling time of4.4456s when compared to 4.4764s obtained using FFOA. This showed a convergence of the solution search space using the LQR (mFFOA) having a more optimal time-to-solution.