Adaptive prescribed performance second-order sliding mode tracking control of autonomous underwater vehicle using neural network-based disturbance observer

Ding, Zhongjun; Wang, Haipeng; Sun, Yanchao; Qin, Hongde

doi:10.1016/j.oceaneng.2022.111939

Cited by 32 publications

(5 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A continual interplay of adjustments is undertaken to optimize forecast accuracy by optimizing the interneuron connection weights. These alterations exemplify the neural network algorithm's adaptation process to meet the predefined accuracy criterion set by the user [8].…”

Section: Literature Reviewmentioning

confidence: 99%

Sports Prediction Model through Cloud Computing and Big Data Based on Artificial Intelligence Method

Eid,

Miled,

Fatnassi

et al. 2024

JILSA

View full text Add to dashboard Cite

This article delves into the intricate relationship between big data, cloud computing, and artificial intelligence, shedding light on their fundamental attributes and interdependence. It explores the seamless amalgamation of AI methodologies within cloud computing and big data analytics, encompassing the development of a cloud computing framework built on the robust foundation of the Hadoop platform, enriched by AI learning algorithms. Additionally, it examines the creation of a predictive model empowered by tailored artificial intelligence techniques. Rigorous simulations are conducted to extract valuable insights, facilitating method evaluation and performance assessment, all within the dynamic Hadoop environment, thereby reaffirming the precision of the proposed approach. The results and analysis section reveals compelling findings derived from comprehensive simulations within the Hadoop environment. These outcomes demonstrate the efficacy of the Sport AI Model (SAIM) framework in enhancing the accuracy of sports-related outcome predictions. Through meticulous mathematical analyses and performance assessments, integrating AI with big data emerges as a powerful tool for optimizing decision-making in sports. The discussion section extends the implications of these results, highlighting the potential for SAIM to revolutionize sports forecasting, strategic planning, and performance optimization for players and coaches. The combination of big data, cloud computing, and AI offers a promising avenue for future advancements in sports analytics.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Sports Prediction Model through Cloud Computing and Big Data Based on Artificial Intelligence Method

Eid,

Miled,

Fatnassi

et al. 2024

JILSA

View full text Add to dashboard Cite

show abstract

“…Firstly, similar to the design steps in the literature [42], the normalized tracking error is defined as given:…”

Section: Model-parameter-free Controller Designmentioning

confidence: 99%

Model-Parameter-Free Prescribed Time Trajectory Tracking Control for Under-Actuated Unmanned Surface Vehicles with Saturation Constraints and External Disturbances

Ren,

Zhang,

Ying

et al. 2023

JMSE

View full text Add to dashboard Cite

This paper mainly addresses the model-parameter-free prescribed time trajectory tracking control issue for under-actuated unmanned surface vehicles (USVs) that are susceptible to model uncertainties, time-varying disturbances, and saturation constraints. Firstly, a state extension based on coordinate transformation was designed to address the lack of control in the sway channel. Secondly, nonlinear behavior stemming from saturation constraints is not always differentiable. Regarding this, a smooth dead-zone-based model was conducted to fit the behavior, leaving a relatively simple actuator model. Then, an improved prescribed time–prescribed performance function (PTPPF) and error transformation method were utilized to propose a model-parameter-free control algorithm that guarantees user-defined constrained boundaries while ensuring all tracking errors converge within small domains before a preassigned settling time. The theoretical analysis was conducted by the initial value theorem, Lyapunov’s second method, and proof by contradiction, followed by comparative simulation results that verified the effectiveness of the proposed control scheme.

show abstract

“…Based on the above research, it is necessary to combine disturbance observer technology with NN control to solve the tracking control problem of nonlinear robot systems. A composite controller combining the observer control method with a sliding mode control and a neural network was designed to achieve control objectives for nonlinear systems [26][27][28][29]. In [26], it introduces the use of optimization methods to obtain the optimal weights of the NN observer, which reduces the workload of adjusting parameters.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Neural Network Tracking Control of Robotic Manipulators Based on Disturbance Observer

Li,

Zhang,

Zhang

et al. 2024

Processes

View full text Add to dashboard Cite

This article presents an adaptive neural network (ANN) control scheme based on a disturbance observer that can achieve trajectory tracking control of robotic manipulators under external disturbances and dynamic model uncertainties. Firstly, an ANN controller based on full-state feedback is derived using the backstepping technique to achieve an online approximation of uncertainty. The integral sliding mode surface with a position error is introduced into the controller, which reduces the steady-state error of the system and enhances robustness. Then, a novel disturbance observer is designed to estimate both the approximation errors of the ANN and external disturbances, and to provide compensation for the controller, effectively suppressing the trajectory tracking errors caused by approximation errors and disturbances. Subsequently, the Lyapunov stability theory is utilized to demonstrate the stability of the developed control strategy and the boundedness of all closed-loop signals. Finally, numerical simulations are used to confirm the efficacy of the proposed control method.

show abstract

Adaptive prescribed performance second-order sliding mode tracking control of autonomous underwater vehicle using neural network-based disturbance observer

Cited by 32 publications

References 35 publications

Sports Prediction Model through Cloud Computing and Big Data Based on Artificial Intelligence Method

Sports Prediction Model through Cloud Computing and Big Data Based on Artificial Intelligence Method

Model-Parameter-Free Prescribed Time Trajectory Tracking Control for Under-Actuated Unmanned Surface Vehicles with Saturation Constraints and External Disturbances

Adaptive Neural Network Tracking Control of Robotic Manipulators Based on Disturbance Observer

Contact Info

Product

Resources

About