Target Tracking Based on a Nonsingular Fast Terminal Sliding Mode Guidance Law by Fixed-Wing UAV

Abstract

This paper proposes a modified nonsingular fast terminal sliding mode (NFTSM) guidance law to solve the problem of ground moving target tracking for fixed-wing unmanned aerial vehicle (UAV) in a planar environment. Firstly, the loitering algorithm is analysed, which can steer the UAV to follow and circle around a ground moving target with the desired distance by heading angle control. Secondly, the effects of different parameters on the convergence time of sliding manifold is presented which is helpful for the designing of sliding manifold. Singularity can be avoided by using a modified saturation function which is obtained through a small range around the null point. Moreover, the NFTSM sliding manifold is used in the loitering algorithm. By using the Lyapunov theory, the finite-time convergence of the proposed method has been proved in the the reaching phase and the sliding phase. In order to verify the approach's feasibility and benefits, numerical simulations are performed by using a moving target with three different motion states in comparison with the conventional sliding model control method. Simulation results indicate that, under the proposed NFTSM guidance law, the UAV can reach the desired distance in a short time.

Performance Analysis and Design Strategy for a Second-Order, Fixed-Gain, Position-Velocity-Measured (α-β-η-θ) Tracking Filter

“…The other gains are expressed using tracking indices (see Equations (24)- (27) of [31]). However, this filter is not optimal for other models, such as the frequently-used random-velocity model [9] and the diagonal Q, which does not include correlations in process noise [1,2]. Other process noise can be incorporated using arbitrary process noise; see [4].…”

“…For example, substituting (a, b, c) = (qT 4 /4, qT 3 /2, qT 2 ) into Equation (33) gives the Q ra of (16); substituting (a, b, c) = (q v T 2 , q v T, q v ) (q v is the variance of the velocity noise) yields the random-velocity model [9]; and b = 0 leads to a diagonal Q, which is also a well-used setting in real applications [1,2]. The relationship between steady state Kalman gains and Q gen is derived as:…”

“…Adaptive tracking techniques such as Kalman and extended Kalman filters [1][2][3][4][5] and particle filters [6,7] are commonly used for this purpose because of their accuracy. An alternative option is fixed-gain tracking filters, which have also been studied and used extensively for two reasons [8][9][10][11][12]:…”

A Unified Approach to Design Robust Controllers for Nonlinear Uncertain Engineering Systems

“…Taking also into account that the computation time for feedback linearization or control signal generation by the inverse model or MPC techniques is non-negligible, these simplified hypotheses make the above mentioned techniques not always reliable (see [4][5][6]8,10,14,15,20,[23][24][25]29,31,33,37,40,41]. In some cases, the control system can even be unstable, as can be verified with simple examples (see also Appendix A).…”

Robust control design for path tracking of non-affine UAV

“…They are also applied for guarding the frontier and identification of natural disasters (Dupont, Chua, Tashrif, & Abbott, 2017). Different control methods have been studied for UAV control, like PID (Misra, Bhattacharjee, Goswami, & Ghosh, 2016;Pounds, Bersak, & Dollar, 2012;Sarhan & Qin, 2016), optimal control (Melnyk, Zhiteckii, Bogatyrov, & Pilchevsky, 2013;Nodland, Zargarzadeh, & Jagannathan, 2013), backsteping (Azinheira & Moutinho, 2008;Zheng, Zhen, & Gong, 2017), control Lyapunov function (Shafiei & Binazadeh, 2012), passivity based control (Chenarani & Binazadeh, 2017), H ∞ control (Jiao, Du, Wang, & Xie, 2010;Kerma, Mokhtari, Abdelaziz, & Orlov, 2012), sliding mode (Castañeda, Salas-Peña, & de León-Morales, 2017;Wu, Cai, Zhao, & Wang, 2017) and dynamic sliding mode (Tavakol & Binazadeh, 2015).…”