Abstract
In the presence of unknown dynamics and input saturation, a finite-time predictor line-of-sight–based adaptive neural network scheme is presented for the path following of unmanned surface vessels. The proposed scheme merges with the guidance and the control subsystem of unmanned surface vessels together. A finite-time predictor–based line-of-sight guidance law is developed to ensure unmanned surface vessels effectively converging to and following the referenced path. Then, the path-following control laws are designed by combining neural network-based minimal learning parameter technique with backstepping method, where minimal learning parameter is applied to account for system nonparametric uncertainties. The key features of this scheme, first, the finite-time predictor errors are guaranteed; second, designed controllers are independent of the system model; and third, only required two parameters update online for each control law. The rigorous theory analysis verifies that all signals in the path-following guidance-control system are semi-globally uniformly ultimately bounded via Lyapunov stability theory. Simulation results illustrate the effectiveness and performance of the presented scheme.
Highlights
Path following means the output maneuvering problem that involves geometric and dynamic tasks of an unmanned surface vessels (USVs)’s closed-loop system, where the former task can be solved by a guidance law for steering and the latter task needs a kinetics controller such that it satisfies desired dynamic behaviors.[8,9]
It will lose stability in the presence of significant external disturbances.[14]. In this context, Ning Wang et al developed a finite-time sideslip observer to exactly estimate timevarying large sideslip angle in a short time.[7]. Along this line of consideration, an extended result was presented by Miao et al.[5] and wherein a compound line-of-sight (CLOS) method was developed to handle the situation that sideslip angles are produced by relative sway velocity and ocean currents separately and can still work when the former relative sideslip angle is approximately more than 20
To illustrate the effectiveness and performance of the presented finite-time predictor lineof-sight–based adaptive neural network (FPANN) scheme, we conduct simulation studies with an USV whose dynamic is given by equations (1) and (2) and its parameters can be found in the work by Lefeber et al.[58]
Summary
In the past decades, unmanned surface vessels (USVs) attract ubiquitous attention in rescue, exploration, military, and commerce fields, and its path following problem has been widely investigated.[1,2,3,4,5] High-accuracy path following control acts a pivotal role for an USV to successfully complete its task that reaches and stays a predefined parametric path automatically without time information.[6,7] path following means the output maneuvering problem that involves geometric and dynamic tasks of an USV’s closed-loop system, where the former task can be solved by a guidance law for steering and the latter task needs a kinetics controller such that it satisfies desired dynamic behaviors.[8,9]. Sideslip angle may be large in practical appliances, whereas the aforementioned references only satisfy the state of steady navigation of the USV with small sideslip angle.[2,9,10,13] it will lose stability in the presence of significant external disturbances.[14] In this context, Ning Wang et al developed a finite-time sideslip observer to exactly estimate timevarying large sideslip angle in a short time.[7] Along this line of consideration, an extended result was presented by Miao et al.[5] and wherein a compound line-of-sight (CLOS) method was developed to handle the situation that sideslip angles are produced by relative sway velocity and ocean currents separately and can still work when the former relative sideslip angle is approximately more than 20 All these aforementioned guidance laws cannot ensure the stability of the guidance signals in a finite time except the one proposed by Wang et al.[7] In the study by Jin,[15] a finite-time convergence of LOS was solved by time-varying tan-type barrier Lyapunov functions. Rm is the m-dimensional Euclidean space. ðÁÞ T denotes the transpose of a matrix dean norm and jj Á jj[2] ð1⁄4ÁÞ.Pjj Ái;jjfj grei2;pj.rðeÁsÞei;nj tds etnheotEesucthlieelement of ðÁÞ in row i and column j. j Á j represents the absolute value of a scalar. ð^Þ denotes the estimated value, and ðeÞ describes the error of approximation. ðÁÞ max represents the maximum value
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