Experimental Study of a Command Governor Adaptive Depth Controller for an Unmanned Underwater Vehicle

Makavita, Charita D.; Jayasinghe, Shantha Gamini; Nguyen, Hung D.; Ranmuthugala, Dev

doi:10.1016/j.apor.2019.02.016

Cited by 20 publications

(9 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…T is the full state vector of the system. The matrices A ′ ∈ R n×n , b ∈ R n×m , c ∈ R p×n in (19), and the rest of the parameters are defined as before in (4). By writing (19) in the state space form, the matrices A, B and C in (4) can be defined as…”

Section: Problem Formulationmentioning

confidence: 99%

“…One of the hazardous environments that human being has not been able to discover effectively is the deep see and ocean surfaces. The underwater robots have been proven a useful technology to explore and investigate such inaccessible environments 4,5 . Moreover, in nuclear applications underwater robots have shown a great potential for inspection and monitoring of the environments.…”

Section: Introductionmentioning

confidence: 99%

“…The L 1 adaptive controller has two fundamental architectures (i) state feedback setting, and (ii) output feedback setting 4 . An L 1 adaptive output feedback control architecture for an underactuated MIMO system has been developed in 32 .…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Ahmadian

Arefi

Khayatian

et al. 2021

Preprint

View full text Add to dashboard Cite

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.

show abstract

Section: Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Ahmadian

Arefi

Khayatian

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Lin et al [8] designed an adaptive fuzzy stabilization controller for an underactuated surface vessel in the presence of unknown time-varying environmental disturbances and input saturation. Makavita et al [9] reported the results of an experimental study conducted to compare the performances of different adaptive control methods for the depth control of a UUV, which represents a significant challenge compared with heading control due to increased noise, time delay, and thrust requirements. Bui and Kim [10] used an FLC method that enabled AUVs to navigate safely through obstacles to a goal, with the optimal path proven by their simulation results.…”

Section: Introductionmentioning

confidence: 99%

“…Next, we used the approach in an experiment with multiple obstacles in a real environment. (8) and (9), the cost function values of the destination and obstacle were negative and positive, respectively. Therefore, the sum of the cost function values from Equation (7) was negative, which meant that no obstacles were present inside the range of the scanning sonar, and the ROV traveled toward the destination.…”

mentioning

confidence: 98%

Application of Fuzzy Theory and Optimum Computing to the Obstacle Avoidance Control of Unmanned Underwater Vehicles

et al. 2020

View full text Add to dashboard Cite

Autonomous underwater vehicles and remotely operated vehicles (ROVs) are unmanned underwater vehicles widely used in marine environments. Establishing an efficient obstacle avoidance approach in underwater environments remains a challenge for these vehicles. Most studies have relied on simulated results; few have been conducted with vehicles in a real environment. This study used an ROV equipped with a scanning sonar as an experimental platform and applied fuzzy logic control to solve nonlinear and uncertain problems, which are difficult to address using conventional control theory. Using data from the depth and inertial sensors, fuzzy logic control can output defuzzification command values that are passed through a fuzzy inference engine to control ROV motion. Fuzzy logic control was used to evaluate depth and heading degrees in navigation experiments. In heading navigation, scanning sonar was used to detect obstacles in the scanning range. An optimum navigation strategy was also developed to calculate appropriate headings to safely and stably navigate during a mission to attain a predetermined destination. The results indicated that the ROV with fuzzy logic control had superior control stability and obstacle avoidance in an underwater environment.

show abstract

Dynamics identification‐driven diving control for unmanned underwater vehicles

Zhong,

Yu,

Xiang

et al. 2024

Journal of Field Robotics

View full text Add to dashboard Cite

This paper presents a comprehensive study of dynamics identification‐driven diving control for unmanned underwater vehicles (UUVs). Initially, a diving dynamics model of UUVs is established, serving as the foundation for the development of subsequent algorithms. A noise‐reduction least squares (NRLS) algorithm is then derived for parameter identification, demonstrating convergence under measurement noise from a probabilistic perspective. A notable feature of this algorithm is its skill in correcting raw data, thereby improving parameter identification accuracy. Based on the identified model, an improved fast terminal sliding mode control (FTSMC) algorithm is introduced for diving control, consistently ensuring rapid convergence under 16 scenarios. Importantly, the proposed diving control algorithm effectively mitigates chattering by incorporating a dedicated filter, adaptively adjusting the switching gain, and substituting saturation function for sign function. Through experimental validation, the NRLS algorithm's advantage over the traditional least squares method becomes evident, with depth errors consistently below 3.5 cm. This indicates that the identified model closely aligns with the actual model, showcasing a commendable fit. Additionally, when compared to the traditional sliding mode controller and the proportional‐integral‐derivative algorithm, the FTSMC algorithm has superior performance, as indicated by a mean absolute percentage error consistently below 4%.

show abstract

Experimental Study of a Command Governor Adaptive Depth Controller for an Unmanned Underwater Vehicle

Cited by 20 publications

References 28 publications

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

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

Application of Fuzzy Theory and Optimum Computing to the Obstacle Avoidance Control of Unmanned Underwater Vehicles

Dynamics identification‐driven diving control for unmanned underwater vehicles

Contact Info

Product

Resources

About