Intelligent Control Method Research For Attitude Control Of MAV

MAV (Micro Aircraft Vehicle) has the advantages of low cost, convenient,lightness, small noise, strong concealment, easier maintenance, and so on. MAVcontrol system was divided into two parts: inner loop and outer loop usually, theinner loop represents the attitude stable control; the outer loop represents the positionnavigation control. This paper focuses on the inner control loop (attitude stablecontrol) of MAV control system, that is to say, this paper takes MAV attitude controlsystem as the research object. But, the MAV attitude control system is influenced byvarious kinds of outside interference and uncertainty, which is caused by variousparameters change, modeling error and other factors. So this paper takes adaptivecontrol technology, the sliding mode control technology, and fuzzy neural networktheory as a research tool to design stable and robust attitude control system of MAV.The main works in this dissertation are arranged as follows:Firstly, the inertial characteristics were analysized, the effects of dynamic forcesand moments on airframe, the dynamical and kinematical equations of centroidmovement and encircling the centroid movement of MAV were also studied, themathematical model of MAV attitude was presented for control.Secondly, the structure and learning algorithms of Interval Type II Fuzzy NeuralNetwork were studied, which has higher precision than Type I Fuzzy NeuralNetwork for dynamic system identification. So, this paper based on the Type II Fuzzy Neural Network theory and MAV attitude model cognitive degree, threecontrollers are studied here for ensuring the robustness and stability of MAV attitudecontrol system:The first kind of controller:robust adaptive controller based on Interval Type IIFuzzy Neural Network. This controller was composed of traditional PD controllerand Interval Type II Fuzzy Neural Network controller, which was constituted by twoInterval Type II Fuzzy Neural Networks, one of Interval Type II Fuzzy NeuralNetworks was used to study the inverse model of MAV attitude, the other one wasthe copy of the first one, tuned online and used to compensate tracking error in thereal-time.The second kind of controller:adaptive sliding mode controller based onInterval Type II Fuzzy Neural Network. A weighting factor, which can be adjustedbased on the trade-off between plant knowledge and control knowledge, wasincluded when combining the control efforts of the indirect adaptive Interval Type IIFuzzy Neural Network controller and the direct adaptive Interval Type II FuzzyNeural Network controller. The free parameters of which can be tuned on-line by anoutput feedback control law and adaptive lawsThe third kind of controller:Indirect adaptive controller based on Interval TypeII Fuzzy Neural Network. This controller makes full use of the nominal model ofMAV attitude. The uncertainties of system were approached by Interval Type IIFuzzy Neural Network online, the approximation error of Interval Type II FuzzyNeural Network and external interference were compensated by robust compensator.Finally, the performances of three controllers were compared and analyzed inthe simulation. The performances of three controllers could meet the performancerequirements of the MAV attitude control nomatter whether under the considerationof system uncertainties and external interferences. However, the indirect adaptivecontroller based on Interval Type II Fuzzy Neural Network is the optimal controlscheme relative to the other two controllers from control accuracy aspect andresponse speed aspect.