Robust stabilizing control of nonholonomic systems with uncertainties via adaptive integral sliding mode

Sarfraz, Muhammad; Rehman, Fazal ur; Shah, Ibrahim

doi:10.1177/1729881417732693

Cited by 23 publications

(26 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The final identification results of the parameters could be obtained by solving equations (6) and (17), and then the final results are shown in Table 3.…”

Section: Parameter Identification Algorithm Designmentioning

confidence: 99%

“…An accurate modelling of the dynamic response of the propellers/rudders used to control UMV is fundamental to obtain a meaningful simulation of the behaviour of the vehicle and, in particular, for the realistic simulation of the vehicle navigation and control systems. [4][5][6] At present, the main methods of obtaining UMV model parameters are numerical calculation and model test. However, the accuracy of model parameters obtained by numerical calculation is not high, and the cost of model parameters obtained by model test is too high.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Parameter identification of unmanned marine vehicle manoeuvring model based on extended Kalman filter and support vector machine

Dong

Yang

Zheng

et al. 2019

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

To predict the manoeuvrability of unmanned marine vehicle and improve its manoeuvrability, the parameters of the manoeuvring model of unmanned marine vehicle need to be obtained. Aiming at the inconvenience of obtaining model parameters under the traditional experimental method, this article studies the parameter identification of unmanned marine vehicle's manoeuvring model based on extended Kalman filter and support vector machine. Firstly, the secondorder nonlinear manoeuvring response model of unmanned marine vehicle is discretized by the difference method, and the corresponding data are collected by the manoeuvring motion simulation of the response model. Secondly, the discrete response model is transformed into an augmented state vector based on extended Kalman filter, and the optimal estimation of the state vector is calculated to identify the parameters. And then, the discrete response model is transformed into a support vector machine-based regression model, the collected data are processed and a set of support vectors are obtained to further identify the parameters of the response model. Finally, by comparing the simulation experiments' results from the original model and the identification model, the recognition results-based extended Kalman filter and support vector machine are analysed and some research results are obtained. The results of this article will provide a powerful reference for the design of unmanned marine vehicle's motion control algorithm.

show abstract

“…The final identification results of the parameters could be obtained by solving equations (6) and (17), and then the final results are shown in Table 3.…”

Section: Parameter Identification Algorithm Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Parameter identification of unmanned marine vehicle manoeuvring model based on extended Kalman filter and support vector machine

Dong

Yang

Zheng

et al. 2019

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

show abstract

“…Based on simulation results, chattering reduction and fast convergence is demonstrated when parameter uncertainties of 20 % and time variant disturbances were considered. In [25], an adaptive integral SMC for AUV stabilization was proposed. In this paper, two scenarios were considered.…”

Section: Introductionmentioning

confidence: 99%

Trajectory tracking for autonomous underwater vehicle: An adaptive approach

et al. 2019

View full text Add to dashboard Cite

During sea missions, underwater vehicles are exposed to changes in the parameters of the system and subject to persistent external disturbances due to the ocean current influence. These issues make the design of a robust controller a quite challenging task. This paper focuses on the design of a adaptive high order sliding mode control for trajectory tracking on an underwater vehicle. The main feature of the developed control law is that it preserves the advantages of robust control, it does not need the knowledge of the upper bound of the disturbance and easy tuning in real applications. Using Lyapunov concept, asymptotic stability of the closed-loop tracking system is ensured. The effectiveness and robustness of the proposed controller for trajectory tracking in depth and yaw dynamics are demonstrated through real-time experiments.

show abstract

“…For this kind of manipulation strategy, there exist several issues which ought to be considered: the underactuated characteristic and the external disturbances in the sea. 6 It is worth mentioning that it is not unique for practical application, as in real robotics applications, the publications 7,8 related to nonholonomic systems can reveal the same issue. When the vessel sails on the sea, it is inevitable to take the external disturbance into account.…”

Section: Introductionmentioning

confidence: 99%

Path following for underactuated surface vessels with disturbance compensating predictive control

Liu

Zhang

et al. 2020

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

It is common that underactuated surface vessels sailing on the sea suffer from strong external sea disturbances, such that the large roll motion can be probably caused resulting to the bad performance of path following. In order to realize the coordinate control of the rudder roll stabilization and path following, a robust controller with roll constraints is designed by combining predictive control with disturbance observer. Firstly, the rudder angle operating range is divided in the Serret-Frenet coordination frame, and then linear models corresponding to different equilibrium can be established with heading control and roll dynamics. Secondly, considering external disturbance and unmodeled error as the lumped disturbances, the disturbance observer can be utilized to achieve the real-time feedforward compensation leading to the improved system robustness. Thirdly, the output redefinition method is adopted to transfer the original system into a new minimum phase system. Based on the receding horizon and state prediction strategies of the predictive control, the analytical control law can be obtained, and the linear programming method is used to guarantee the roll constraint for the simplified computational burden. Lastly, the simulations have been carried out to show the good performance of the proposed algorithm with the heading control and roll reduction.

show abstract

Robust stabilizing control of nonholonomic systems with uncertainties via adaptive integral sliding mode

Cited by 23 publications

References 33 publications

Parameter identification of unmanned marine vehicle manoeuvring model based on extended Kalman filter and support vector machine

Parameter identification of unmanned marine vehicle manoeuvring model based on extended Kalman filter and support vector machine

Trajectory tracking for autonomous underwater vehicle: An adaptive approach

Path following for underactuated surface vessels with disturbance compensating predictive control

Contact Info

Product

Resources

About