Platoon Formation Control With Prescribed Performance Guarantees for USVs

Dai, Shi‐Lu; He, Shude; Lin, Hai; Wang, Cong

doi:10.1109/tie.2017.2758743

Cited by 267 publications

(93 citation statements)

References 33 publications

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“…17 Using the PPC methodology, adaptive tracking control with prescribed performance was proposed for a group of fully actuated marine vehicles. 19,20 In the work of Bechlioulis et al, 21 the PPC methodology was applied to solve the trajectory tracking control problem for torpedo-like and unicycle-like vehicles with prescribed performance. For a group of underactuated surface vehicles, 22 considering symmetric constraints on tracking errors of the line-of-sight ranges and angles, adaptive formation control laws were designed by employing novel barrier Lyapunov functions.…”

Section: Feedback Control With Output/state Constraintsmentioning

confidence: 99%

“…anḋ1,̇2 3 are available for controller implementation because every term in the first time derivative of i in (22)-(24) are computable withṗ −1 i = −ṗ i ∕p 2 i , i = 1, 2, 3 and p i defined in (19). It is clear for system (27) that the designed control laws are closely related to the selection of Q according to feedback linearization technique.…”

Section: Control Design For Known Vehicle Modelmentioning

confidence: 99%

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Transverse function control with prescribed performance guarantees for underactuated marine surface vehicles

Dai

Lin

2019

Intl J Robust & Nonlinear

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This paper studies the problem of stabilizing reference trajectories (also called as the trajectory tracking problem) for underactuated marine vehicles under predefined tracking error constraints. The boundary functions of the predefined constraints are asymmetric and time-varying. The time-varying boundary functions allow us to quantify prescribed performance of tracking errors on both transient and steady-state stages. To overcome difficulties raised by underactuation and nonzero off-diagonal terms in the system matrices, we develop a novel transverse function control approach to introduce an additional control input in backstepping procedure. This approach provides practical stabilization of any smooth reference trajectory, whether this trajectory is feasible or not. By practical stabilization, we mean that the tracking errors of vehicle position and orientation converge to a small neighborhood of zero. With the introduction of an error transformation function, we construct an inverse-hyperbolic-tangent-like barrier Lyapunov function to show practical stability of the closed-loop systems with prescribed transient and steady-state performances. To deal with unmodeled dynamic uncertainties and external disturbances, we employ neural network (NN) approximators to estimate uncertain dynamics and present disturbance observers to estimate unknown disturbances. Subsequently, we develop adaptive control, based on NN approximators and disturbance estimates, that guarantees the prescribed performance of tracking errors during the transient stage of on-line NN weight adaptations and disturbance estimates. Simulation results show the performance of the proposed tracking control.

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Section: Feedback Control With Output/state Constraintsmentioning

confidence: 99%

Section: Control Design For Known Vehicle Modelmentioning

confidence: 99%

Transverse function control with prescribed performance guarantees for underactuated marine surface vehicles

Dai

Lin

2019

Intl J Robust & Nonlinear

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“…Remark Note that when the constraint requirement functions Ω bH and Ω bL are selected, such that

\begin{matrix} alignleft \\ align-1 Ω_{bH} (t) & align-2 = a_{1} + a_{2} e^{- a_{3} t}, a_{1} > 0, a_{2} > 0, a_{3} > 0, \\ align-1 Ω_{bL} (t) & align-2 = b_{1} + b_{2} e^{- b_{3} t}, b_{1} > 0, b_{2} > 0, b_{3} > 0 . \end{matrix}

By ensuring the barrier function is bounded in the closed‐loop analysis, it can guarantee that the output tracking error will be always bounded by the exponentially decaying constraint functions Ω bH and Ω bL . This is one of the key ideas behind the prescribed transient performance analysis in the literature, making it a special case of our discussion on system output constraint requirements, which is more general in practice for different control objectives (constrained system output and/or prescribed system performance).…”

Section: Universal Barrier Functionmentioning

confidence: 99%

Nonrepetitive trajectory tracking for nonlinear autonomous agents with asymmetric output constraints using parametric iterative learning control

Jin

2019

Intl J Robust & Nonlinear

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Summary In this paper, we present a novel parametric iterative learning control (ILC) algorithm to deal with trajectory tracking problems for a class of nonlinear autonomous agents that are subject to actuator faults. Unlike most of the ILC literature, the desired trajectories in this work can be iteration dependent, and the initial position of the agent in each iteration can be random. Both parametric and nonparametric system unknowns and uncertainties, in particular the control input gain functions that are not fully known, are considered. A new type of universal barrier functions is proposed to guarantee the satisfaction of asymmetric constraint requirements, feasibility of the controller, and prescribed tracking performance. We show that under the proposed algorithm, the distance and angle tracking errors can uniformly converge to an arbitrarily small positive number and zero, respectively, over the iteration domain, beyond a small user‐prescribed initial time interval in each iteration. A numerical simulation is presented in the end to demonstrate the efficacy of the proposed algorithm.

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“…Then, the tracking error can be guaranteed within a given prescribed boundary. The PPC method has been widely applied in practice such as vehicle active suspension systems [45,46], unmanned surface vehicle systems [47], nonlinear systems [48,49], fault-tolerant control [50], and robot manipulators [51]. On the other hand, the barrier Lyapunov function (BLF) is used as a constraint control which is utilized to transform the error into a new error without constraint [52].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Barrier Control for Nonlinear Servomechanisms with Friction Compensation

Wang

Gao

et al. 2018

Complexity

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This paper proposes an adaptive barrier controller for servomechanisms with friction compensation. A modified LuGre model is used to capture friction dynamics of servomechanisms. This model is incorporated into an augmented neural network (NN) to account for the unknown nonlinearities. Moreover, a barrier Lyapunov function (BLF) is utilized to each step in a backstepping design procedure. Then, a novel adaptive control method is well suggested to ensure that the full-state constraints are within the given boundary. The stability of the closed-loop control system is proved using Lyapunov stability theory. Comparative experiments on a turntable servomechanism confirm the effectiveness of the devised control method.

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Platoon Formation Control With Prescribed Performance Guarantees for USVs

Cited by 267 publications

References 33 publications

Transverse function control with prescribed performance guarantees for underactuated marine surface vehicles

Transverse function control with prescribed performance guarantees for underactuated marine surface vehicles

Nonrepetitive trajectory tracking for nonlinear autonomous agents with asymmetric output constraints using parametric iterative learning control

Adaptive Barrier Control for Nonlinear Servomechanisms with Friction Compensation

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