Near-space aerospace vehicles attitude control based on adaptive dynamic programming and sliding mode control

In this paper, coordinated sliding mode control (SMC) and adaptive dynamic programming (ADP) strategy is proposed for near-space aerospace vehicle (NSASV) adaptive attitude tracking control. In this design, the NSASV attitude angle control is implemented as classical cascade control scheme with two control loops in the model. The outer one is a slow control loop for the attitude angle tracking, and the inner one is a fast control loop for the attitude angular rate tracking. Both of these two control loops are designed by using SMC, which can provide exact control performance near the operating point. To improve the control performance and robustness under parameter variations and external disturbances, ADP based supplementary control is introduced and incorporated into the inner fast control loop to provide adaptive compensation for the reference signal. Simulation study is carried out in Matlab/Simulink environment, and the results demonstrate that the proposed cooperative control could provide quite satisfied tracking performance in terms of overshoot and oscillation.

[1]  Haibo He,et al.  Intelligent load frequency controller using GrADP for island smart grid with electric vehicles and renewable resources , 2015, Neurocomputing.

[2]  Michel van Tooren,et al.  Review of uncertainty-based multidisciplinary design optimization methods for aerospace vehicles , 2011 .

[3]  Hai Lin,et al.  A smooth hybrid symbolic control for the formation of UAVs over a partitioned space , 2013, 2013 American Control Conference.

[4]  Yufei Tang,et al.  Observer-based sliding mode frequency control for micro-grid with photovoltaic energy integration , 2016, 2016 IEEE Power and Energy Society General Meeting (PESGM).

[5]  Changyin Sun,et al.  An Event-Triggered Approach for Load Frequency Control With Supplementary ADP , 2017, IEEE Transactions on Power Systems.

[6]  Haibo He,et al.  Reactive power control of grid-connected wind farm based on adaptive dynamic programming , 2014, Neurocomputing.

[7]  D. Liu,et al.  Adaptive Dynamic Programming for Finite-Horizon Optimal Control of Discrete-Time Nonlinear Systems With $\varepsilon$-Error Bound , 2011, IEEE Transactions on Neural Networks.

[8]  Huaguang Zhang,et al.  Adaptive Dynamic Programming: An Introduction , 2009, IEEE Computational Intelligence Magazine.

[9]  John Villasenor.  "Drones" and the Future of Domestic Aviation , 2014, Proc. IEEE.

[10]  Xiangdong Liu,et al.  Distributed attitude coordinated tracking control for multi-group spacecrafts based on input normalized adaptive neural network , 2014, 2014 American Control Conference.

[11]  Jinyu Wen,et al.  Energy-Storage-Based Low-Frequency Oscillation Damping Control Using Particle Swarm Optimization and Heuristic Dynamic Programming , 2014, IEEE Transactions on Power Systems.

[12]  Wei Xu,et al.  Continuous sliding mode controller with disturbance observer for hypersonic vehicles , 2015, IEEE/CAA Journal of Automatica Sinica.

[13]  Yufei Xu,et al.  Adaptive Fault-Tolerant Tracking Control of Near-Space Vehicle Using Takagi–Sugeno Fuzzy Models , 2010, IEEE Transactions on Fuzzy Systems.

[14]  Jennie Si,et al.  Helicopter trimming and tracking control using direct neural dynamic programming , 2003, IEEE Trans. Neural Networks.

[15]  Yufei Tang,et al.  SMES-Based Damping Controller Design Using Fuzzy-GrHDP Considering Transmission Delay , 2016, IEEE Transactions on Applied Superconductivity.

[16]  Chaoxu Mu,et al.  Intelligent Critic Control with Disturbance Attenuation for a Micro-Grid System , 2019 .

[17]  Derek Kingston,et al.  UAV navigation using predictive vector field control , 2014, 2014 American Control Conference.

[18]  Jie Wang,et al.  Nonlinear robust control for reusable launch vehicles in reentry phase based on time-varying high order sliding mode , 2013, J. Frankl. Inst..

[19]  Zhong-Ping Jiang,et al.  Global Adaptive Dynamic Programming for Continuous-Time Nonlinear Systems , 2013, IEEE Transactions on Automatic Control.

[20]  Manoj Kumar,et al.  Reinforcement learning based controller synthesis for flexible aircraft wings , 2014, IEEE/CAA Journal of Automatica Sinica.

[21]  Jennie Si,et al.  Online learning control by association and reinforcement , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

[22]  Changyin Sun,et al.  Adaptive nonsingular terminal sliding mode control design for near space hypersonic vehicles , 2014, IEEE/CAA Journal of Automatica Sinica.

[23]  Haibo He,et al.  Power System Stability Control for a Wind Farm Based on Adaptive Dynamic Programming , 2015, IEEE Transactions on Smart Grid.

[24]  Changyin Sun,et al.  Fast sliding mode control on air-breathing hypersonic vehicles with transient response analysis , 2016, J. Syst. Control. Eng..

[25]  Jianying Yang,et al.  Adaptive fault-tolerant control with control allocation for flight systems with severe actuator failures and input saturation , 2013, 2013 American Control Conference.

[26]  Thomas Stastny,et al.  Robust and Adaptive Nonlinear Model Predictive Controller for Unsteady and Highly Nonlinear Unmanned Aircraft , 2015, IEEE Transactions on Control Systems Technology.

[27]  Haibo He,et al.  Fuzzy-Based Goal Representation Adaptive Dynamic Programming , 2016, IEEE Transactions on Fuzzy Systems.

[28]  Changyin Sun,et al.  Adaptive sliding mode control for re-entry attitude of near space hypersonic vehicle based on backstepping design , 2015, IEEE/CAA Journal of Automatica Sinica.

[29]  Bálint Vanek,et al.  Supervisory fault tolerant control of the NASA AirStar aircraft , 2014, 2014 American Control Conference.

[30]  Nhan T. Nguyen,et al.  Fuzzy model-based pitch stabilization and wing vibration suppression of flexible aircraft , 2014, 2014 American Control Conference.

[31]  Andrea Serrani,et al.  Modeling and linear control of a flapping-wing MAV with split-amplitude and phase-modulated wingbeat , 2013, 2013 American Control Conference.

[32]  Ron J. Patton,et al.  Reconfigurable Fault Tolerant Control for nonlinear aircraft based on concurrent SMC-NN adaptor , 2014, 2014 American Control Conference.

[33]  Haibo He,et al.  Air-Breathing Hypersonic Vehicle Tracking Control Based on Adaptive Dynamic Programming , 2017, IEEE Transactions on Neural Networks and Learning Systems.