Aguiar, A.P., Dacic, D.B., Hespanha, J.P., Kokotovic, P.: Path-following or reference-tracking? an answer relaxing the limits to performance. In: Proceedings of the IFAC/EURON Symposium on Intelligent Autonomous Vehicles, Lisbon, Portugal, pp. 1–6 (2004)
Alexis, K., Nikolakopoulos, G., Tzes, A.: Switching model predictive attitude control for a quadrotor helicopter subject to atmospheric disturbances. Control. Eng. Pract. 19(10), 1195–1207 (2011). https://doi.org/10.1016/j.conengprac.2011.06.010
Article
Google Scholar
Altmannshofer, S.: Fast suboptimal nonlinear model predictive control of an inverted pendulum. IFAC Proc. Vol. 45(17), 442–447 (2012). https://doi.org/10.3182/20120823-5-NL-3013.00051. 4th IFAC Conference on Nonlinear Model Predictive Control
Article
Google Scholar
Aoki, Y., Asano, Y., Honda, A., Motooka, N., Ohtsuka, T.: Nonlinear model predictive control of position and attitude in a Hexacopter with three failed rotors*. IFAC-PapersOnLine 51(20), 228–233 (2018). https://doi.org/10.1016/j.ifacol.2018.11.018
Article
Google Scholar
Azevedo, D.S., Costa, L.F.S., Brito, A.V., Nascimento, T.P.: Analysis of prediction models for multi-robot system nmpfc. In: 2014 Joint Conference on Robotics: SBR-LARS Robotics Symposium and Robocontrol, pp. 19–24. https://doi.org/10.1109/SBR.LARS.Robocontrol.2014.12 (2014)
Baca, T., Loianno, G.: M.Saska: Embedded model predictive control of unmanned micro aerial vehicles. In: 2016 IEEE International Conference on Methods and Models in Automation and Robotics (MMAR), pp. 992–997. https://doi.org/10.1109/MMAR.2016.7575273 (2016)
Bouffard, P., Aswani, A., Tomlin, C.: Learning-based model predictive control on a quadrotor: Onboard implementation and experimental results. In: Proceedings - IEEE International Conference on Robotics and Automation. https://doi.org/10.1109/ICRA.2012.6225035, pp 279–284. IEEE (2012)
Bounemeur, A., Chemachema, M., Essounbouli, N.: Indirect adaptive fuzzy fault-tolerant tracking control for MIMO nonlinear systems with actuator and sensor failures. ISA Trans. 79(September 2017), 45–61 (2018). https://doi.org/10.1016/j.isatra.2018.04.014
Article
Google Scholar
Camacho, E.F., Bordons, C.: Model Predictive Control. Springer, London (2004)
MATH
Google Scholar
Findeisen, R., Allgöwer, F.: An introduction to nonlinear model predictive control. In: 21st Benelux Meeting on Systems and Control, Veldhoven, The Netherlands, pp. 1–23 (2002)
Kamel, M., Burri, M., Siegwart, R.: Linear vs Nonlinear MPC for trajectory tracking applied to rotary wing micro aerial vehicles. IFAC-PapersOnLine 50 (1), 3463–3469 (2017). https://doi.org/10.1016/j.ifacol.2017.08.849
Article
Google Scholar
Kamel, M.A., Zhang, Y., Yu, X.: Fault-tolerant cooperative control of multiple wheeled mobile robots under actuator faults, vol. 48. https://doi.org/10.1016/j.ifacol.2015.09.682. 9th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes SAFEPROCESS 2015 (2015)
Kanjanawanushkul, K., Zell, A.: Path following for and omnidirectional mobile robot based on model predictive control. In: 2009 IEEE International Conference on Robotics and Automation, Piscataway, NJ, USA, pp. 3341–3346 (2009)
L’Afflitto, A.: A Mathematical Perspective on Flight Dynamics and Control. Springer, London (2017)
Book
Google Scholar
L’Afflitto, A., Anderson, R.B., Mohammadi, K.: An Introduction to Nonlinear Robust Control for Unmanned Quadrotor Aircraft: How to Design Control Algorithms for Quadrotors Using Sliding Mode Control and Adaptive Control Techniques. IEEE Control. Syst. 38(3), 102–121 (2018). https://doi.org/10.1109/MCS.2018.2810559
MathSciNet
Article
Google Scholar
Lee, H., Kim, H.J.: Trajectory tracking control of multirotors from modelling to experiments: A survey. Int. J. Control Autom. Syst. 15(1), 281–292 (2017). https://doi.org/10.1007/s12555-015-0289-3
Article
Google Scholar
Lima, P.U., Ahmad, A., Dias, A., Conceição, A.G., Moreira, A.P., Silva, E., Almeida, L., Oliveira, L., Nascimento, T.P.: Formation control driven by cooperative object tracking. Robot. Auton. Syst. 63, 68–79 (2015). https://doi.org/10.1016/j.robot.2014.08.018
Article
Google Scholar
Liu, Y., Rajappa, S., Montenbruck, J. M., Stegagno, P., Bülthoff, H., Allgöwer, F., Zell, A.: Robust nonlinear control approach to nontrivial maneuvers and obstacle avoidance for quadrotor UAV under disturbances. Robot. Auton. Syst. 98, 317–332 (2017). https://doi.org/10.1016/j.robot.2017.08.011
Article
Google Scholar
Liu, Z., Hedrick, K.: Dynamic surface control techniques applied to horizontal position control of a quadrotor. In: 2016 20th International Conference on System Theory, Control and Computing (ICSTCC), pp. 138–144. https://doi.org/10.1109/ICSTCC.2016.7790655 (2016)
Loianno, G., Brunner, C., McGrath, G., Kumar, V.: Estimation, control, and planning for aggressive flight with a small quadrotor with a single camera and IMU. IEEE Robot. Autom. Lett. 2(2), 404–411 (2017). https://doi.org/10.1177/00957984880151008
Article
Google Scholar
Mehrez, M.W., Mann, G.K.I., Gosine, R.G.: Comparison of stabilizing nmpc designs for wheeled mobile robots: An experimental study. In: Moratuwa Engineering Research Conference (MERCon), 2015, pp. 130–135. https://doi.org/10.1109/MERCon.2015.7112333 (2015)
Mohammadi, K., L’Afflitto, A. In: Tuan, L.A. (ed.) : Adaptive Robust Control and Its Applications. Croatia, InTech (2017)
Nascimento, T.P., Conceição, A.G.S., Moreira, A.P.: Iterative weighted tuning for a nonlinear model predictive formation control. In: 2014 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), pp. 2–7 (2014)
Nascimento, T.P., Costa, L.F.S., Conceição, A.G.S., Moreira, A.P.: Nonlinear model predictive formation control: An iterative weighted tuning approach. J. Intell. Robot. Syst. 80(3), 441–454 (2015). https://doi.org/10.1007/s10846-015-0183-5
Article
Google Scholar
Nascimento, T.P., Dórea, C.E.T., Gonçalves, L.M.G.: Nonholonomic mobile robots’ trajectory tracking model predictive control: a survey. Robotica 36(5), 676–696 (2018). https://doi.org/10.1017/S0263574717000637
Article
Google Scholar
Nascimento, T.P., Moreira, A.P., Conceição, A.G.S., Bonarini, A.: Intelligent state changing applied to multi-robot systems. Robot. Auton. Syst. 61(2), 115–124 (2013). https://doi.org/10.1016/j.robot.2012.10.011
Article
Google Scholar
Nascimento, T.P., Saska, M.: Position and attitude control of multi-rotor aerial vehicles: A survey. Annual Reviews in Control. https://doi.org/10.1016/j.arcontrol.2019.08.004 (2019)
Ostafew, C.J., Schoellig, A.P., Barfoot, T.D.: Robust constrained learning-based nmpc enabling reliable mobile robot path tracking. The Int. J. Robot. Res. 1, 1–17 (2016). https://doi.org/10.1177/0278364916645661
Google Scholar
Özbek, N.S., Önkol, M., Efe, M.O.̈: Feedback control strategies for quadrotor-type aerial robots: A survey. Trans. Inst. Meas. Control. 38(5), 529–554 (2015). https://doi.org/10.1177/0142331215608427
Article
Google Scholar
Pan, Y., Wang, J.: A neurodynamic optimization approach to nonlinear model predictive control. In: Systems Man and Cybernetics (SMC), 2010 IEEE International Conference on, pp. 1597–1602. https://doi.org/10.1109/ICSMC.2010.5642367 (2010)
Pounds, P., Mahony, R., Corke, P.: Modelling and control of a large quadrotor robot. Control. Eng. Pract. 18(7), 691–699 (2010). https://doi.org/10.1016/j.conengprac.2010.02.008
Article
Google Scholar
Raffo, G.V., Ortega, M.G., Rubio, F.R.: An integral predictive/nonlinear H-inf control structure for a quadrotor helicopter. Automatica 46(1), 29–39 (2010). https://doi.org/10.1016/j.automatica.2009.10.018
MathSciNet
Article
Google Scholar
Raha, A., Chakrabarty, A., Raghunathan, V., Buzzard, G.T.: Embedding approximate nonlinear model predictive control at ultrahigh speed and extremely low power. IEEE Trans. Control Syst. Technol., 1–8. https://doi.org/10.1109/TCST.2019.2898835 (2019)
Riedmiller, M., Braun, H.: A direct adaptive method for faster backpropagation learning: the RPROP algorithm. In: IEEE International Conference on Neural Networks, San Francisco, CA, USA, pp. 586–591 (1993)
Ryll, M., Bicego, D., Franchi, A.: Modeling and control of FAST-Hex: A fully-actuated by synchronized-tilting hexarotor. In: IEEE International Conference on Intelligent Robots and Systems. https://doi.org/10.1109/IROS.2016.7759271, vol. 2016-Novem, pp 1689–1694. IEEE (2016)
Ryll, M., Bülthoff, H.H., Giordano, P.R.: A novel overactuated quadrotor unmanned aerial vehicle: Modeling, control, and experimental validation. IEEE Trans. Control Syst. Technol. 23(2), 540–556 (2015). https://doi.org/10.1109/TCST.2014.2330999
Article
Google Scholar
Shim, D.H., Kim, H.J., Sastry, S.: A flight control system for aerial robots: Algorithms and experiments. IFAC Proc. Vol. (IFAC-PapersOnline) 15(1), 241–246 (2002). https://doi.org/10.1016/S0967-0661(03)00100-X
Article
Google Scholar
Shraim, H., Awada, A., Youness, R.: A survey on quadrotors: Configurations, modeling and identification, control, collision avoidance, fault diagnosis and tolerant control. IEEE Aerosp. Electron. Syst. Mag. 33 (7), 14–33 (2018). https://doi.org/10.1109/MAES.2018.160246
Article
Google Scholar
Vougioukas, S.G.: Reactive trajectory tracking for mobile robots based on non linear model predictive control. In: Proceedings 2007 IEEE International Conference on Robotics and Automation, pp. 3074–3079, https://doi.org/10.1109/ROBOT.2007.363939 (2007)