A robust H-infinity based depth control of an autonomous underwater vehicle

Nag, Anirban; Patel, Surendra Singh; Kishore, Kaushal; Akbar, S. A.

doi:10.1109/icaes.2013.6659363

Cited by 10 publications

(5 citation statements)

References 7 publications

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“…Though it is possible to find a single controller to stabilize the plant, the controller has a worse disturbance-attenuation performance. The single controller to stabilize the plant is designed according to Nag, Patel, et al (2013) [19] (This single controller is taken as baseline for comparison) (19) The single controller can stabilize Eq. (18), but its system gain for disturbance-attenuation is about 2.467, larger than our expectation.…”

Section: Simulation Results and Analysesmentioning

confidence: 99%

Performance enhancement of supervisory control for largely mismatched processes

Gao

2015

2015 34th Chinese Control Conference (CCC)

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Even through being an effective approach to handle robustness, the single controller is challenging to meet strict performance requirement when the modeling mismatch is relatively large. This paper presents a supervisory method to enhance the robust stability and performance of control using a multiple model switching configuration. A new scheduling logic which selects the most proper controller into loop is proposed to ensure bounded exponentially weighted norm of the closed loop system. The candidate controllers are obtained by solving a set of linear matrix inequalities (LMIs). The effectiveness of this supervisory control method is validated with a fist-order inertial process with pure time delay.

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Section: Simulation Results and Analysesmentioning

confidence: 99%

Performance enhancement of supervisory control for largely mismatched processes

Gao

2015

2015 34th Chinese Control Conference (CCC)

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“…In ( 16), r g (s) = K g R(s),η 1 (t) =θ T (t)∥x(t)∥ ∞ +σ (t), R(s) is the Laplace transform of the reference input r(t), and the input gain K g is defined as K g = − CA −1 m B −1 . In (15), K > 0 is the feedback gain, and D(s) is a diagonal matrix with elements of strictly proper transfer functions that leads to designing the low-pass filter C(s) in ( 17)…”

Section: Standard L 1 Adaptive Control Problemmentioning

confidence: 99%

“…The problems are originated from the intrinsic time-varying and high level of nonlinearities, resulting from the hydrodynamic effects and the high level of external disturbance, caused by the sea currents and the drag effects acting on the vehicle 6,7 . To overcome these problems, various control methods have been proposed in the literature such as adaptive RISE 8 , feedback linearization controller 9 , output feedback control 10 , L 1 adaptive controller 11,12 , and adaptive fuzzy control 13 , Lyapunov-based model predictive control 14 , robust H ∞ control 15 , fuzzy backstepping sliding mode control 16 . A more detailed review of the proposed controllers for AUVs can be found in 17 .…”

Section: Introductionmentioning

confidence: 99%

A fast L1 adaptive constrained back-stepping controller using barrier Lyapunov function for anuncertain submersible vehicle

Ahmadian

Arefi

Khayatian

et al. 2021

Preprint

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In this paper, a new L1 adaptive back-stepping controller based on the barrier Lyapunov function (BLF) is proposed to respect the position and velocity constraints usually imposed in designing Euler-Lagrange systems. The purpose of this investigation is to improve different aspects of a conventional L1 adaptive control. More specifically, the modified controller has a lower complexity by removing the low-pass filter from the design procedure. The performance of the controller is also enhanced by having a faster convergence speed and increased robustness against nonlinear uncertainties and disturbances arising in practical applications. The proposed scheme is evaluated on two different Euler-Lagrange systems, i.e. a 6-DOF remotely operated vehicle (ROV) and a single-link manipulator. The results for the new back-stepping design are assessed in both scenarios in terms of settling time, percentage of overshoot, and trajectory tracking error. The results confirm that both tracking and state estimation errors for position and velocity outputs outperform the standard L1 adaptive control technique. The results also demonstrate the high performance of the proposed approach in removing the matched nonlinear time-varying disturbances and dynamic uncertainties and a good trajectory tracking despite the uncertainty on the input gain of the system.

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“…Motion control systems for AUV have become very challenging due to strong coupling, high nonlinearity and external disturbances. A variety of control systems are available, such as PID control [1], [2], sliding mode control [3], [4], H ∞ control [5], [6] and adaptive control [7]. Nevertheless, the PID (proportional integral derivative) control is still the most widely used in AUV because of the simplicity and reliability.…”

Section: Introductionmentioning

confidence: 99%

Fractional-Order PID Motion Control for AUV Using Cloud-Model-Based Quantum Genetic Algorithm

Wan

Wang

et al. 2019

IEEE Access

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Aiming to improve the performance of motion for autonomous underwater vehicle (AUV), a fractional-order PID strategy is proposed. It is a more generalized form for the conventional integer-order PID controller, keeping its simplicity and utilizing the generalized derivative and integral control actions. The fractional-order PID controller has been successfully applied to heading control, diving control and path-following system of AUV on sea trial. In addition, the fractional-order closed-loop system has proven to be stable. By comparing simulations and experiments, the satisfactory performance, such as overshoot, settling time and steady-state error, has been achieved. The cloud-model-based quantum genetic algorithm (CQGA) is employed to tune coefficients of fractional-order PID controller. The quantum bits and quantum superposition states avoid the pressure of selection and maintain the diversity of population in chromosome coding. Due to the randomness and stability tendency of cloud droplets, the cloud crossover operator and the cloud mutation operator can effectively overcome the shortcomings of premature and slow searching speed. Numerical simulations show that the CQGA is more efficient to find the optimal coefficients of fractionalorder PID controller than GA. INDEX TERMS Fractional-order PID, AUV, cloud-model-based quantum genetic algorithm (CQGA), steady error.

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A robust H-infinity based depth control of an autonomous underwater vehicle

Cited by 10 publications

References 7 publications

Performance enhancement of supervisory control for largely mismatched processes

Performance enhancement of supervisory control for largely mismatched processes

A fast L1 adaptive constrained back-stepping controller using barrier Lyapunov function for anuncertain submersible vehicle

Fractional-Order PID Motion Control for AUV Using Cloud-Model-Based Quantum Genetic Algorithm

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