Fuzzy observer‐based tracking control of an underactuated underwater vehicle with linear velocity estimation

Duan, Kairong; Fong, Simon; Chen, C. L. Philip

doi:10.1049/iet-cta.2019.0604

Cited by 25 publications

(17 citation statements)

References 42 publications

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“…The control of underactuated robots can be generally categorized into two different problems: collocated and non-collocated controls, where the former controls the "actuated" DoF whose dimension is the same as the number of torque inputs and the latter stabilizes the remaining "passive" degrees. 9,10 Over the years, an enormous amount of control strategies has been developed based on sliding modes, [11][12][13][14] fuzzy logics, [15][16][17] adaptive controls, [18][19][20][21] optimal controls, [22][23][24] and backstepping techniques 4,25 for the collocated and non-collocated controls of underactuated robots. With the goal of designing multi-objective controllers that can satisfy a large and diverse set of objectives for nonlinear dynamical systems, a quadratic program-based control Lyapunov-control barrier function (QP-CLBF) has been recently introduced to address both stabilization and safety in an optimal fashion.…”

Section: Introductionmentioning

confidence: 99%

Robust adaptive control Lyapunov‐barrier function for non‐collocated control and safety of underactuated robotic systems

Azimi

Hutchinson

2022

Intl J Robust & Nonlinear

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Quadratic program‐based control Lyapunov‐control barrier function (QP‐CLBF) is a recently developed control scheme that balances stability and safety in an optimal fashion. However, direct application of such controllers to uncertain nonlinear systems with model uncertainties and disturbances could potentially degrade the performance of closed‐loop systems and violate/restrict safety‐critical constraints. This article presents a novel robust QP‐based adaptive approach for non‐collocated control of a class of underactuated robotic systems with a semi‐strict‐feedback form through which exponential disturbance‐to‐error (eDE) stability of all system solutions is ensured. We begin by developing a backstepping design technique based on which a neural network‐based adaptive control is designed to approximate unknown nonlinear functions. To compensate for uncanceled uncertainties, including modeling approximation error, chained error effects between coordinates, and disturbances, virtual inputs are designed whose gains are evolved by projection‐based adaptation mechanisms. To construct a three‐term control law, including feedforward, adaptive, and optimal terms, a QP optimization problem is synthesized to compute a family of optimal stabilizing signals while encoding time‐varying robust CLF (TVRCLF) and CBF (TVRCBF), and control bounds as constraints. In contrast with existing QP‐CLBF, our control technique with the proposed TVRCLF and TVRCBF significantly improves control objectives and strictly guarantees safety by automatically compensating for unknown uncertainties without the need for knowing their bounds a priori. The eDE stability of all system errors is proven using Lyapunov stability arguments for both collocated and non‐collocated coordinates. Simulations and comparisons to a baseline QP‐CLBF‐based feedback linearization (QP‐CLBF/FL) on a single‐link flexible‐joint robot verify the benefits of the proposed control approach.

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Section: Introductionmentioning

confidence: 99%

Robust adaptive control Lyapunov‐barrier function for non‐collocated control and safety of underactuated robotic systems

Azimi

Hutchinson

2022

Intl J Robust & Nonlinear

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“…So far, different forms of state observers have been presented. Among them, high-gain observer or variants thereof has been widely applied to estimate the unmeasured velocity, due to its simple structure [26,27]. In order to obtain more precise estimation results, other state observers with complex forms have been investigated.…”

Section: Introductionmentioning

confidence: 99%

Observer-based region tracking control for underwater vehicles without velocity measurement

et al. 2022

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This paper addresses the problem of region tracking control for underwater vehicles without velocity measurement in marine environment. For this case, an improved region tracking control strategy is proposed based on a Nussbaum state observer. In the proposed method, a Nussbaum state observer is developed to estimate the unmeasured velocity of the vehicle. And then an improved region tracking control strategy is presented by incorporating the estimated results of the state observer, such that the tracking errors satisfy the requirement of the prescribed boundaries. In addition, a RBF neural network is applied to approximate the unknown dynamics of the vehicle. It is verified that the estimated error and the tracking error are uniformly ultimately bounded. Finally, the proposed observer-based region tracking control strategy is applied on an underwater vehicle to perform simulation studies and compared with a traditional backstepping controller and a traditional region tracking controller based on a high-gain observer. The comparative simulation results demonstrate the effectiveness of the proposed region tracking control strategy.

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“…Recently, autonomous marine vehicles have been greatly studied, which have been applied in different ocean engineering fields, such as seafloor mapping, maritime rescue, ocean sampling surveillance, and mine development and so forth 1‐17 . Therefore, many scholars have devoted themselves on the control problems of such systems 18‐25 .…”

Section: Introductionmentioning

confidence: 99%

Neural network‐based tracking control of autonomous marine vehicles with unknown actuator dead‐zone

Wang

Guo

et al. 2021

Intl J Robust & Nonlinear

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This article studies the neural‐network based backstepping control problem for autonomous marine vehicles perturbed by external disturbances. The actuator dead‐zone phenomenon, which is a common non‐smooth property caused by the complicated operating environment of autonomous marine vehicles, is also considered. To cope with the issue of “complexity explosion” and further decrease the tracking errors, a command filtering compensation strategy is also proposed, which guarantees satisfactory tracking performance and boundedness of the closed‐loop system signals. Finally, simulation studies are given to further demonstrate the effectiveness of the proposed control method.

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Fuzzy observer‐based tracking control of an underactuated underwater vehicle with linear velocity estimation

Cited by 25 publications

References 42 publications

Robust adaptive control Lyapunov‐barrier function for non‐collocated control and safety of underactuated robotic systems

Robust adaptive control Lyapunov‐barrier function for non‐collocated control and safety of underactuated robotic systems

Observer-based region tracking control for underwater vehicles without velocity measurement

Neural network‐based tracking control of autonomous marine vehicles with unknown actuator dead‐zone

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