DRFNN adaptive observer based sliding mode tracking control of an underactuated surface vehicle

Xia, Guoqing; Wang, Guoqing; Chen, Xinghua; Zhao, Ang; Pang, Chengcheng

doi:10.1109/icma.2015.7237837

Cited by 5 publications

(3 citation statements)

References 10 publications

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“…As shown in Figure 6, the proposed controller can provide faster convergence and higher tracking performance than the controller of Ref. 40. Figure 7 illustrates good performance of the designed observer (16) for estimating the unmeasured states.…”

Section: Simulationsmentioning

confidence: 85%

Observer based robust neuro-adaptive control of non-square MIMO nonlinear systems with unknown dynamics

Sarand¹,

Karimi²

2017

IJCIS

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This paper addresses a robust adaptive control problem of non-square nonlinear systems with unmeasurable states. The systems are assumed to be multi-input/multi-output subject to dynamical uncertainties and external disturbances. The approach is studied for two cases, i.e., underactuated and over-actuated nonlinear systems. The new observer does not need to satisfy the SPR conditions. Moreover, a constant full-rank matrix with an adaptive gain is used to approximate the unknown gain matrix. Therefore, the proposed controller's structure simplifies its implementation. The unknown nonlinearity is estimated neural networks. Stability of the closed-loop system is proved using Lyapunov analysis. The feasibility of the proposed approach is validated by simulation examples.

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Section: Simulationsmentioning

confidence: 85%

Observer based robust neuro-adaptive control of non-square MIMO nonlinear systems with unknown dynamics

Sarand¹,

Karimi²

2017

IJCIS

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“…A sliding mode trajectory tracking controller is designed to track the reference trajectory for USV as in [ 11 ]. All relevant position, orientation, linear and angular velocities, acceleration and forces describing the USV’s trajectory are calculated.…”

Section: Sensor Measurement Simulationmentioning

confidence: 99%

INS/GNSS Tightly-Coupled Integration Using Quaternion-Based AUPF for USV

Xia

Wang

2016

Sensors

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This paper addresses the problem of integration of Inertial Navigation System (INS) and Global Navigation Satellite System (GNSS) for the purpose of developing a low-cost, robust and highly accurate navigation system for unmanned surface vehicles (USVs). A tightly-coupled integration approach is one of the most promising architectures to fuse the GNSS data with INS measurements. However, the resulting system and measurement models turn out to be nonlinear, and the sensor stochastic measurement errors are non-Gaussian and distributed in a practical system. Particle filter (PF), one of the most theoretical attractive non-linear/non-Gaussian estimation methods, is becoming more and more attractive in navigation applications. However, the large computation burden limits its practical usage. For the purpose of reducing the computational burden without degrading the system estimation accuracy, a quaternion-based adaptive unscented particle filter (AUPF), which combines the adaptive unscented Kalman filter (AUKF) with PF, has been proposed in this paper. The unscented Kalman filter (UKF) is used in the algorithm to improve the proposal distribution and generate a posterior estimates, which specify the PF importance density function for generating particles more intelligently. In addition, the computational complexity of the filter is reduced with the avoidance of the re-sampling step. Furthermore, a residual-based covariance matching technique is used to adapt the measurement error covariance. A trajectory simulator based on a dynamic model of USV is used to test the proposed algorithm. Results show that quaternion-based AUPF can significantly improve the overall navigation accuracy and reliability.

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“…In [12], Mu et al proposed an adaptive trajectory tracking control strategy for underactuated USV subject to unknown dynamics and time-varying external disturbances, where a first-order sliding surface and a second-order sliding surface are used to design surge control law and yaw control law respectively. In [13], designed a nonlinear adaptive observer through nonlinear adaptive observer to deal with the unknown dynamics in an underactuated USV model. In order to better solve the influence of external disturbance on ship trajectory tracking, [14], [15] proposed a disturbance observer to compensate unknown disturbances in real time.…”

Section: Introductionmentioning

confidence: 99%

Trajectory Tracking Control for Unmanned Surface Vehicle Subject to Unmeasurable Disturbance and Input Saturation

Zhao

Wang

et al. 2020

IEEE Access

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This paper presents a trajectory tracking control controller for an unmanned surface vehicle in the presence of unmeasurable disturbance and input saturation. An adaptive trajectory tracking control strategy is developed with the aid of disturbance observer, neural shunting model, adaptive technology, and auxiliary design system. Disturbance observer is used to estimate unmeasurable disturbances in order to compensate for them. The functions of neural shunting model and adaptive technology are respectively used to deal with the "computational explosion" caused by derivation of virtual control law and to enhance the robustness of the controlled system. Besides, to avoid the potential input saturation issue, auxiliary design system is employed in the design of control strategy. By Lyapunov stability theory, it is proved that all the error signals in the trajectory tracking control system are uniform ultimate bounded. The simulation results are given to prove the correctness of the proposed control scheme. INDEX TERMS unmanned surface vehicle, trajectory tracking, input saturation, neural shunting model

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DRFNN adaptive observer based sliding mode tracking control of an underactuated surface vehicle

Cited by 5 publications

References 10 publications

Observer based robust neuro-adaptive control of non-square MIMO nonlinear systems with unknown dynamics

Observer based robust neuro-adaptive control of non-square MIMO nonlinear systems with unknown dynamics

INS/GNSS Tightly-Coupled Integration Using Quaternion-Based AUPF for USV

Trajectory Tracking Control for Unmanned Surface Vehicle Subject to Unmeasurable Disturbance and Input Saturation

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