Research On Adaptive Tracking Control Method For Constrained UAVs

Unmanned aerial vehicles(UAVs)have obvious advantages of flexibility,strong adaptability,and easy to use,thus widely applied in numerous fields of military and civilian.There actually exist disadvantageous nonlinear characteristics such as dead-zone,saturation in the actuators of UAVs.Meanwhile,UAVs are not only sensitive to external disturbances,but also are significant influenced by uncertainties including modeling errors,parametric perturbations,and etc.To ensure flight safety,angle of attack(AOA),airspeed and other conditions need to be limited within the designed range.In some special applications,satisfactory dynamic performance and instruction tracking accuracy of control system are required to be guaranteed.Therefore,restricted by aforementioned factors,designing a high performance control system of UAV still faces great challenges.Considering above-mentioned issues comprehensively and proposing effective control strategies are of great significance for promoting the progress of flight control technology as well as improving the performance of UAVs.This dissertation focuses on the control law design of nonlinear longitudinal model of UAVs,and in-depth research of robust back-stepping,constrained input and output control,state constraint control and prescribed performance control(PPC)have been conducted.The research work and main contributions of this dissertation can be summarized as:1)Aiming at dealing with the problem of constrained tracking errors and control input saturation,based on Barrier-Lyapunov-function(BLF)technology,an adaptive neural network(NN)constrained control method is proposed.By designing the corresponding auxiliary compensation systems,the desired control laws are modified to ensure the stability of the control system when the actuators are instantaneously saturated.The BLF technology is used to constrain the system tracking errors,which ensures the tracking accuracy of the closed-loop system.2)By integrating BLF method and command filter technology,an adaptive fuzzy control algorithm with constrained angle of attack(AOA)is presented.Through introducing the saturation limiter to restrain the virtual control in the back-stepping method,and employing the BLF to constrain the virtual tracking error,the purpose of limiting the AOA is achieved,thus ensuring the safety of UAVs3)In order to ensure that the system has satisfactory transient performance,taking into account the influence of the dead-zone of the actuator,an indirect and a direct method are proposed for designing adaptive prescribed performance controllers.By combining the smooth dead-zone inverse model strategy with the prescribed performance technology,an indirect adaptive controller is designed;Integrating the prescribed performance method,the fuzzy logic system(FLS)technique and the Nussbaum-type function,a direct adaptive fuzzy controller is addressed.The simulation results show that both methods can achieve robust tracking of reference commands and can ensure tracking errors with satisfactory convergence speed and steady-state accuracy.4)Two concise adaptive NN control algorithms are constructed for the tracking control of UAV’s altitude systems.In the first method,by transforming the longitudinal model of UAVs into the integrated chain form,an adaptive controller is designed by minimum parameter learning(MPL)method,which reduces the number of online learning parameters of NN.In the second method,by passing the nonlinear uncertain function to the last step of back-stepping design,neural network is employed to approximate the total uncertainties.The main feature of these two algorithms is that only one neural network is required to approximate the lumped uncertainty of the model,which significantly reduces the amount of computation.5)Aiming at dealing with the tracking problem of UAV with parameter perturbations and external disturbances,a novel performance control method based on a new disturbance observer is proposed.In the controller design,the compound nonlinear uncertainties are estimated online on the basis of a nonlinear disturbance observer,and then the estimated disturbance values are actively compensated to the nominal prescribed performance controller,which effectively suppresses the influence of mismatched disturbances on the system and enhance the robustness of the control system.