Similar articles

Abstract: Designing reliable flight control for an autonomous helicopter requires a high performance dynamics model. In this paper, a nonlinear autoregressive with exogenous inputs (NLARX) model is selected as the mathematical structure for identifying and controlling the flight of a small-scale helicopter. A neural network learning algorithm is combined with the NLARX model to identify the dynamic component of the rotorcraft unmanned aerial vehicle (RUAV). This identification process is based on the well-known gradient descent learning algorithm. As a case study, the multiple-input multiple-output (MIMO) model predictive control (MPC) is applied to control the pitch motion of the helicopter. Results of the neural network output model are closely match with the real flight data. The MPC also shows good performance under various conditions.

[1]Amidi, O., Kanade, T., Fujita, K., 1999. A visual odometer for autonomous helicopter flight. Robotics and Autonomous Systems, 28(2-3):185-193.

[2]Budiyono, A., Sudiyanto, T., Lesmana, H., 2007. First Principle Approach to Modeling of Small Scale Helicopter. International Conference on Intelligent Unmanned System, Bali, Indonesia, p.100-110.

[3]Chen, W., Liu, M., Zhu, M., Yu, Y., Ge, Y., 2006. Configuration of Sensors on Small-Scale Autonomous Helicopter. IEEE International Conference on Information Acquisition, Shandong, China, p.851-855.

[4]Coates, A., Abbeel, P., Ng, A.Y., 2008. Learning for Control from Multiple Demonstrations. Proceedings of the 25th International Conference on Machine learning, Helsinki, Finland, p.144-151.

[5]Constantin, N., 2003. Adaptive neural predictive techniques for nonlinear control. Studied in Informatics and Control, 12(4):285-291.

[6]Cutler, C.R., Ramaker, B.L., 1979. Dynamic Matrix Control: A Computer Control Algorithm. AICHE National Meeting, Houston, TX, USA.

[7]Dharmayanda, H.R., Kang, T., Lee, Y.J., Sung, S., 2007. Motion Stability of Small Scale Helicopter Using State Feedback. International Conference on Control, Automation and Systems, p.1439-1444.

[8]Kadmiry, B., Driankov, D., 2004. A fuzzy fight controller combining linguistic and model-based fuzzy control. Fuzzy Sets and Systems, 146(3):313-347.

[9]Kim, H.J., Shim, D.H., 2003. A flight control system for aerial robots: algorithm and experiments. Control Engineering Practice, 11(12):1389-1400.

[10]Mettler, B., Tischler, M.B., Kanade, T., 1999. System Identification of Small-size Unmanned Helicopter Dynamics. American Helicopter Society 55th Forum, Montreal, Quebec, Canada.

[11]Narendra, K.S., Parthasarathy, K., 1990. Identification and control of dynamical systems using neural network. IEEE Transactions on Neural Networks, 1(1):4-27.

[12]Nugroho, G., Taha, Z., 2005. Dynamics Modelling and Attitude Control for a Model Scale Helicopter. International Conference on Manufacturing and Information System, Kuala Lumpur, Malaysia.

[13]Richalet, J., Rault, A., Testud, J.L., Papon, J., 1978. Model predictive heuristic control: applications to industrial processes. Automatica, 14(5):413-428.

[14]Sanchez, E.N., Becerra, H.M., Velez, C.M., 2007. Combining fuzzy, PID and regulation control for an autonomous mini-helicopter. Information Sciences, 177(10):1999-2022.

[15]Saripalli, S., Montgomery, J.F., Sukhatme, G.S., 2003. Visually guided landing of an unmanned aerial helicopter. IEEE Transactions on Robotics and Automation, 19(3):371-380.

[16]Shim, D.H., Kin, H.J., Sastry, S., 2000. Control Design for Rotorcraft-based Unmanned Aerial Vehicles Using Time-Domain System Identification. Proceeding of the IEEE, International Conference on Control Application. Alaska, p.808-813.

[17]Tahersima, H., Fatehi, A., 2007. Nonlinear Identification of Model Helicopter Using Genetic Algorithm. Proceedings of the 17th IFAC World Congress, p.1-4.

[18]Zhang, Q., Ljung, L., 2004. Multiple Steps Prediction with Nonlinear ARX Models. Proceedings of the 6th International Federation of Automatic Control: Nonlinear Control Systems, p.309-314.