Identification and Learning Control of Ocean Surface Ship Using Neural Networks

Dai, Shi‐Lu; Wang, Cong; Luo, Fei

doi:10.1109/tii.2012.2205584

Cited by 212 publications

(72 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The result was extended to nonlinear systems satisfying matching conditions [38][39][40]. By combining recursive design technologies such as backstepping control and the system decomposition strategy, the deterministic learning was also applied to solve learning problem of accurate identification of ocean surface ship and robot manipulation in uncertain dynamical environments [41][42][43][44]. However, due to the recursive property of backstepping control, the convergence of neural weights has to be recursively verified based on the system decomposition strategy.…”

Section: Complexitymentioning

confidence: 99%

Neural Learning Control of Flexible Joint Manipulator with Predefined Tracking Performance and Application to Baxter Robot

Wang

2017

Complexity

View full text Add to dashboard Cite

This paper focuses on neural learning from adaptive neural control (ANC) for a class of flexible joint manipulator under the output tracking constraint. To facilitate the design, a new transformed function is introduced to convert the constrained tracking error into unconstrained error variable. Then, a novel adaptive neural dynamic surface control scheme is proposed by combining the neural universal approximation. The proposed control scheme not only decreases the dimension of neural inputs but also reduces the number of neural approximators. Moreover, it can be verified that all the closed-loop signals are uniformly ultimately bounded and the constrained tracking error converges to a small neighborhood around zero in a finite time. Particularly, the reduction of the number of neural input variables simplifies the verification of persistent excitation (PE) condition for neural networks (NNs). Subsequently, the proposed ANC scheme is verified recursively to be capable of acquiring and storing knowledge of unknown system dynamics in constant neural weights. By reusing the stored knowledge, a neural learning controller is developed for better control performance. Simulation results on a single-link flexible joint manipulator and experiment results on Baxter robot are given to illustrate the effectiveness of the proposed scheme.

show abstract

Section: Complexitymentioning

confidence: 99%

Neural Learning Control of Flexible Joint Manipulator with Predefined Tracking Performance and Application to Baxter Robot

Wang

2017

Complexity

View full text Add to dashboard Cite

show abstract

“…By combining dynamic surface control technology [31], the result in [30] was further extended to th-order strictfeedback systems. The deterministic learning method was also applied in many physical systems such as marine surface vessels [32,33] and robot manipulators [34].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Learning from Adaptive Neural Control of Uncertain Robots with Guaranteed Full-State Tracking Precision

Wang

Zhang

2017

Complexity

View full text Add to dashboard Cite

A dynamic learning method is developed for an uncertain -link robot with unknown system dynamics, achieving predefined performance attributes on the link angular position and velocity tracking errors. For a known nonsingular initial robotic condition, performance functions and unconstrained transformation errors are employed to prevent the violation of the full-state tracking error constraints. By combining two independent Lyapunov functions and radial basis function (RBF) neural network (NN) approximator, a novel and simple adaptive neural control scheme is proposed for the dynamics of the unconstrained transformation errors, which guarantees uniformly ultimate boundedness of all the signals in the closed-loop system. In the steadystate control process, RBF NNs are verified to satisfy the partial persistent excitation (PE) condition. Subsequently, an appropriate state transformation is adopted to achieve the accurate convergence of neural weight estimates. The corresponding experienced knowledge on unknown robotic dynamics is stored in NNs with constant neural weight values. Using the stored knowledge, a static neural learning controller is developed to improve the full-state tracking performance. A comparative simulation study on a 2-link robot illustrates the effectiveness of the proposed scheme.

show abstract

“…In addition, a variety of hybrid controllers are excogitated in [24][25][26][27][28][29][30][31][32][33] for trajectory-tracking control of UMVs. An adaptive supervisory control algorithm that combines a switching method with an iterative Lyapunov technique is proposed in [24]; a stable adaptive neural network controller combined with a backstepping technique and Lyapunov theory is designed in [25]; a state feedback adaptive backstepping fuzzy logic controller is addressed in [27]; a hybrid sliding-mode control strategy based on a bioinspired model is developed in [24]; a suboptimal robust control methodology is presented in [30]; and a hybrid control algorithm based on neural network and dynamic surface control is presented in both [28] and [33].…”

Section: Introductionmentioning

confidence: 99%

Horizontal-Plane Trajectory-Tracking Control of an Underactuated Unmanned Marine Vehicle in the Presence of Ocean Currents

Dong

Wan

Liu

et al. 2016

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

Based on an integral backstepping approach, a trajectorytracking control algorithm is proposed for an underactuated unmanned marine vehicle (UMV) sailing in the presence of ocean-current disturbance. Taking into consideration the UMV model's fore/aft asymmetry, a nonlinear threedegree-of-freedom (3DOF) underactuated dynamic model is established for the horizontal plane. First, trajectorytracking differences between controllers designed based on symmetric and asymmetric models of the UMV are discussed. In order to explicitly study the effect of oceancurrent interference on the trajectory-tracking controller, the ocean current is integrated into the kinematic and dynamic models of the UMV. Detailed descriptions of distinct trajectory-tracking control performances in the presence of different ocean-current velocities and direction angles are presented. The well-known persistent exciting (PE) condition is completely released in the designed trajectory-tracking controller. A mild integral item of trajectory tracking error is merged into the control law, and global stability analysis of the UMV system is carried out using Lyapunov theory and Barbalat's Lemma. Simulation experiments in the semi-physical simulation platform are implemented to confirm the effectiveness and superiority of the excogitated control algorithm.

show abstract

Identification and Learning Control of Ocean Surface Ship Using Neural Networks

Cited by 212 publications

References 21 publications

Neural Learning Control of Flexible Joint Manipulator with Predefined Tracking Performance and Application to Baxter Robot

Neural Learning Control of Flexible Joint Manipulator with Predefined Tracking Performance and Application to Baxter Robot

Dynamic Learning from Adaptive Neural Control of Uncertain Robots with Guaranteed Full-State Tracking Precision

Horizontal-Plane Trajectory-Tracking Control of an Underactuated Unmanned Marine Vehicle in the Presence of Ocean Currents

Contact Info

Product

Resources

About