Parameters Optimization and Adaptive Control for UAV by Quantum Bacteria Foraging Algorithms and Local Refinement Strategy

The nonlinear model of UAV is more complex than that of classical under-actuated systems, among many control design methods, a kind of optimal fuzzy controller that is designed based on tensor product distributed compensation method with local extrema refinement strategy is proposed in the work [11], the proposed local extrema refinement strategy based tensor product distributed compensation controller presents many design parameters, to name a few, the maximum length of the sampling intervals, decaying rate, the upper bound of control effort and the maximum value of initial state, etc. To design an optimized fuzzy controller, the quantum inspired bacterial foraging algorithm that was proposed by us is employed to find the optimized the parameters of the controller, the quantum inspired bacterial foraging algorithm facilitates the design steps that is often optimized via linear matrix inequalities, and the combination of the heuristic algorithm and linear matrix inequalities performance better than the local extrema refinement strategy based tensor product distributed compensation controller.