Application Of RBF-ARX Model-based Predictive Control On Quad-Rotor Helicopter Simulator

As a MIMO nonlinear coupling system, the study of quad-rotor is meaningful in recent years. Focusing in this system, the researches and experiments of mathematical modeling and predictive control algorithm will be presented in this paper.Firstly, the mathematical modeling based on the quad-rotor is introduced. In the past, the traditional physical method was popular. The math models were established on the differential equations deduced by mechanism analysis, which has some disadvantages. The physical parameters are hard to be precisely measured, and some simplifications and linearizations are made in the process of modeling.The math models such as ARX model and RBF-ARX model are identification methods. The ARX model is a commonly used linear model. So as to represent the nonlinear object, the quad-rotor, the work space of the quad-rotor is linearly divided, and in each linear subspace there is an ARX model to represent its characteristics. The RBF-ARX model is a nonlinear time-varying model whose structure resembles the ARX model. Its independent variables are groups of signals indicating the nonlinear status of the system, and its model parameters can be promptly adjusted to the best by taking the advantages of the RBF neural network. Owing to the self-adjusting parameters, the RBF-ARX model not only has an outstanding approximation in local linear space, but also has superior global performance.Secondly, the predictive controller of the quad-rotor is designed. Generally speaking, the procedure of predictive control algorithm is divided into three parts, which are building model, rolling optimization and feedback correction. In this paper, the predictive controller is established based on the state space model. In order to calculate the best control input sequences, it adopts the quadratic programming method to optimize performance index function.Finally, the simulation and real-time control experiments have been carried out. The control results show that the RBF-ARX model-based predictive control strategy has Strong stability and robustness. Based on all different step function responses, it has a satisfied performance. However, through the comparison analysis, the RBF-ARX model-based predictive controller undoubtedly wins the best, which has a fast response, high precision and stable outputs.