Input-Constrained Path Following for Autonomous Marine Vehicles with a Global Region of Attraction

Hùng, Nguyễn Tuấn; Rego, Francisco; Crasta, Naveena; Pascoal, António

doi:10.1016/j.ifacol.2018.09.499

Cited by 10 publications

(13 citation statements)

References 11 publications

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“…where Q=diag (1,1,2) and R=diag (2,20). The sampling interval is set to = 0.2 second and the prediction horizon is set to T p = 2 seconds.…”

Section: Simulation Examplesmentioning

confidence: 99%

“…This is the reason why we did not use LaSalle's invariance principle to conclude the stability in the proof. Note that this is different from the single path following studied in 20 where the speed of the vehicle depends only on the path parameter, which makes the path following error system autonomous; and therefore the proof of stability can be done using the invariance principle.…”

Section: (I) Stabilitymentioning

confidence: 99%

“…Step 1: From Lemma 1, and (20) we conclude that |e [i] (t)| ≤ [i] (t)∕v dmin for all t and all i ∈  . Letting e = [e [1] , e [2] , … , e [N] ] T , it follows that…”

Section: C2 Proof Of Theoremmentioning

confidence: 99%

See 2 more Smart Citations

Cooperative path following of constrained autonomous vehicles with model predictive control and event‐triggered communications

Hùng

Pascoal

Johansen

2020

Intl J Robust & Nonlinear

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We present a solution to the problem of multiple vehicle cooperative path following (CPF) that takes explicitly into account vehicle input constraints, the topology of the intervehicle communication network, and time-varying communication delays. The objective is to steer a group of vehicles along given spatial paths, at speeds that may be path dependent, while holding a feasible geometric formation. The solution involves decoupling the original CPF problem into two subproblems: (i) single path following of input-constrained vehicles and (ii) coordination of an input-constrained multiagent system. The first is solved by adopting a sampled-data model predictive control scheme, whereas the latter is tackled using a novel distributed control law with an event-triggered communication (ETC) mechanism. The proposed strategy yields a closed-loop CPF system that is input-to-state-stable with respect to the system's state (consisting of the path following error of all vehicles and their coordination errors) and the system's input, which includes triggering thresholds for ETC communications and communication delays. Furthermore, with the proposed ETC mechanism, the number of communications among the vehicles are significantly reduced. Simulation examples of multiple autonomous vehicles executing CPF maneuvers in 2D under different communication scenarios illustrate the efficacy of the CPF strategy proposed.

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“…where Q=diag (1,1,2) and R=diag (2,20). The sampling interval is set to = 0.2 second and the prediction horizon is set to T p = 2 seconds.…”

Section: Simulation Examplesmentioning

confidence: 99%

Section: (I) Stabilitymentioning

confidence: 99%

See 1 more Smart Citation

Cooperative path following of constrained autonomous vehicles with model predictive control and event‐triggered communications

Hùng

Pascoal

Johansen

2020

Intl J Robust & Nonlinear

Self Cite

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show abstract

“…Substituting the equation (12) in (11), the cost function can be expressed based on sliding function and its first derivative as follows:…”

Section: (4)mentioning

confidence: 99%

“…Some research projects concerning adopting the higher order sliding mode algorithms such as super-twisting for control of the maritime autonomous robots, were carried out in [10,11]. In [12] there were presented two control algorithms for the trajectory tracking of an autonomous marine platform, in which the input constraints and disturbances induced by constant ocean currents were regarded. In this research the first approach was based on a Lyapunov design strategy, while the second was developed by adopting a MPC algorithm.…”

Section: Introductionmentioning

confidence: 99%

Model Predictive Super-Twisting Sliding Mode Control for an Autonomous Surface Vehicle

Esfahani

Szlapczynski

2019

Polish Maritime Research

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This paper presents a new robust Model Predictive Control (MPC) algorithm for trajectory tracking of an Autonomous Surface Vehicle (ASV) in presence of the time-varying external disturbances including winds, waves and ocean currents as well as dynamical uncertainties. For fulfilling the robustness property, a sliding mode control-based procedure for designing of MPC and a super-twisting term are adopted. The MPC algorithm has been known as an effective approach for the implementation simplicity and its fast dynamic response. The proposed hybrid controller has been implemented in MATLAB / Simulink environment. The results for the combined Model Predictive Super-Twisting Sliding Mode Control (MP-STSMC) algorithm have shown that it significantly outperforms conventional MPC algorithm in terms of the transient response, robustness and steady state response and presents an effective chattering attenuation in comparison with the Super-Twisting Sliding Mode Control (STSMC) algorithm.

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A review of path following control strategies for autonomous robotic vehicles: Theory, simulations, and experiments

Hùng

Rego

Quintas

et al. 2022

Journal of Field Robotics

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This article presents an in-depth review of path following control strategies that are applicable to a wide range class of marine, ground, and aerial autonomous robotic vehicles. From a control system standpoint, path following can be formulated as the problem of stabilizing a path following error system that describes the dynamics of position and possibly orientation errors of a vehicle with respect to a path, with the errors defined in an appropriate reference frame. In spite of the large variety of path following methods described in the literature we show that, in principle, most of them can be categorized in two groups: stabilization of the path following error system expressed either in the vehicle's body frame or in a frame attached to a "reference point" moving along the path, such as a Frenet-Serret (F-S) frame or a Parallel Transport (P-T) frame. With this observation, we provide a unified formulation that is simple but general enough to cover many methods available in the literature. We then discuss the advantages and disadvantages of each method, comparing them from the design and implementation standpoint. We further show experimental results of the path following methods obtained from field trials testing with under-actuated and over-actuated autonomous marine vehicles. In addition, we introduce open-source Matlab and Gazebo/ROS simulation toolboxes that are helpful in testing path following methods before their integration in the combined guidance, navigation, and control systems of autonomous vehicles.autonomous cars, autonomous marine vehicles (AMVs), autonomous surface vehicles (ASVs), control, guidance, over-actuated vehicles, path following, under-actuated vehicles, underwater autonomous vehicles (UAVs), unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs) | INTRODUCTIONPath-following (PF) is one of the most fundamental tasks to be executed by autonomous vehicles. It consists of driving a vehicle to and maintaining it on a pre-defined path while tracking a pathdependent speed profile. Unlike trajectory tracking, the path is not parameterized by time but rather by any other useful parameter that in some cases may be the path length. Thus, there is more flexibility in making the vehicle first converge to the path smoothly then move along it while tracking a given speed assignment. Path following is useful in many applications where the main objective is to accurately traverse the path, while maintaining a certain speed is a secondary task. Stated in simple terms, it is not required for the vehicle to be at specific positions at specific instants of time, a strong requirement in

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Input-Constrained Path Following for Autonomous Marine Vehicles with a Global Region of Attraction

Cited by 10 publications

References 11 publications

Cooperative path following of constrained autonomous vehicles with model predictive control and event‐triggered communications

Cooperative path following of constrained autonomous vehicles with model predictive control and event‐triggered communications

Model Predictive Super-Twisting Sliding Mode Control for an Autonomous Surface Vehicle

A review of path following control strategies for autonomous robotic vehicles: Theory, simulations, and experiments

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