Neural Learning Control of Marine Surface Vessels With Guaranteed Transient Tracking Performance

Dai, Shi‐Lu; Wang, Min; Wang, Cong

doi:10.1109/tie.2015.2504553

Cited by 282 publications

(139 citation statements)

References 40 publications

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“…In addition, owing to the existence of approximation errors, only uniform ultimate boundedness stability was guaranteed, and the tracking accuracy was not prior known. Recently, a series of elegant control algorithms were derived by other works, which can achieve predefined transient and steady‐state tracking performance. Unfortunately, the parameter selection of controllers relies on the initial condition of closed‐loop systems, ie, the control design is online.…”

Section: Introductionmentioning

confidence: 99%

Fault‐tolerant leader‐follower formation control of marine surface vessels with unknown dynamics and actuator faults

Zhang

Yang

2018

Intl J Robust & Nonlinear

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Summary This paper concentrates on the leader‐follower formation control problem for marine surface vessels with unknown nonlinear dynamics and actuator faults. The unknown inertia matrix and multiplicative fault render the existing methods infeasible. To solve this problem, a low‐complexity prescribed performance controller is first proposed without the help of auxiliary neural/fuzzy systems or adaptive mechanisms. A modification technique is further adopted to relax the initial condition, such that global closed‐loop stability is guaranteed. Finally, simulation results illustrate the above theoretical results.

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Section: Introductionmentioning

confidence: 99%

Fault‐tolerant leader‐follower formation control of marine surface vessels with unknown dynamics and actuator faults

Zhang

Yang

2018

Intl J Robust & Nonlinear

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“…The matrices P andṖ are also bounded for bounded H f (x). Moreover, according to [49], with a periodic or periodic-like input for the ELM, the matrix H is persistently excited (PE), i.e., there exist T 1 > 0, > 0 such that t+T1 t…”

Section: Robotic Learning Controlmentioning

confidence: 99%

Haptic Identification by ELM-Controlled Uncertain Manipulator

Yang

Huang

Cheng

et al. 2017

IEEE Trans. Syst. Man Cybern, Syst.

146

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Abstract-This paper presents an extreme learning machine (ELM) based control scheme for uncertain robot manipulators to perform haptic identification. ELM is used to compensate for the unknown nonlinearity in the manipulator dynamics. The ELM enhanced controller ensures that the closed-loop controlled manipulator follows a specified reference model, in which the reference point as well as the feedforward force is adjusted after each trial for haptic identification of geometry and stiffness of an unknown object. A neural learning law is designed to ensure finite-time convergence of the neural weight learning, such that exact matching with the reference model can be achieved after the initial iteration. The usefulness of the proposed method is tested and demonstrated by extensive simulation studies.

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“…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

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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.

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Neural Learning Control of Marine Surface Vessels With Guaranteed Transient Tracking Performance

Cited by 282 publications

References 40 publications

Fault‐tolerant leader‐follower formation control of marine surface vessels with unknown dynamics and actuator faults

Fault‐tolerant leader‐follower formation control of marine surface vessels with unknown dynamics and actuator faults

Haptic Identification by ELM-Controlled Uncertain Manipulator

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

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