Control of fully actuated ocean surface vessels using a class of feedforward approximators

By Keng Peng Tee, Shuzhi Sam Ge. IEEE Transactions on Control Systems Technology, 2006

332 citations recorded by the source corpus.

Subfields: Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/30513d0e376264868e886af7585717ae2dbca5cd

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

1. Wei He
2. Mou Chen
3. Xu Jin
4. Jun Wang
5. Zhouhua Peng
6. Shaocheng Tong
7. Shuzhi Sam Ge
8. Dan Wang

Citations in the corpus

• Distributed Maneuvering of Autonomous Surface Vehicles Based on Neurodynamic Optimization and Fuzzy Approximation (2018, methodology): “For illustrations, the NCGFC method is compared with a direct neural backstepping control (DNBC) method [28] and a direct neural DSC (DNDSC) method [13].”
• Robust Adaptive Neural Network Control for a Class of Uncertain MIMO Nonlinear Systems With Input Nonlinearities (2010, background): “Lemma 1 [38], [42]: For bounded initial conditions, if there exists a continuous and positive-definite Lyapunov func- tion satisfying , such that , where are class functions and is a positive constant, then the solution is uniformly bounded.”
• Adaptive Dynamic Surface Control for Formations of Autonomous Surface Vehicles With Uncertain Dynamics (2013, background): “The trajectory tracking problem for fully-actuated vessels is considered in [23], where a radial basis function (RBF) NN is employed to handle both the unknown uncertainties and the time-varying disturbances.”
• Direct Adaptive Neural Control for a Class of Uncertain Nonaffine Nonlinear Systems Based on Disturbance Observer (2013, background): “Lemma 2 [81], [82]: Consider the following linear system:”
• An Overview of Recent Advances in Coordinated Control of Multiple Autonomous Surface Vehicles (2021, methodology): “observer schemes are proposed to estimate unmeasured states such as wave-filtering observers [60], high-gain observers [23], [32], neural-network-based observers [43],”
• Adaptive Fixed-Time Control for MIMO Nonlinear Systems With Asymmetric Output Constraints Using Universal Barrier Functions (2019, background): “Remark 1: In applications, certain aircraft [32], ships [33], and rigid robots and motors [34] dynamics can be converted to the plant model as in (1).”
• Dynamic Surface Control Using Neural Networks for a Class of Uncertain Nonlinear Systems With Input Saturation (2015, background): “Assumption 1 [56]: For all t > 0, the reference signal y d ( t ) is a sufficiently smooth function of t , and y d , ˙ y d , and ¨ y d are bounded, that is, there exists a positive constant B 0 such that (cid:7) 0 := { ( y d , ˙ y d , ¨ y d ) : Assumption 2 [57]: The signs of g i , i = 1 , . . . , n…”
• Adaptive Impedance Control for an Upper Limb Robotic Exoskeleton Using Biological Signals (2017, background): “Neural network (NN) is powerful in controller design for uncertain dynamical systems, which has been verified in [8]– [11], [21].”
• Distributed Containment Maneuvering of Multiple Marine Vessels via Neurodynamics-Based Output Feedback (2017, methodology): “Second, in contrast to the observer designs in [22], [24], and [26], the unknown vessel dynamics and unmeasured velocity information can be simultaneously identified.”
• Adaptive Neural Network Control of a Marine Vessel With Constraints Using the Asymmetric Barrier Lyapunov Function (2017, methodology): “The dynamics of a marine surface vessel [24] with MIMO are described by”
• Neural Network Control of a Robotic Manipulator With Input Deadzone and Output Constraint (2016, background): “The unmeasurable state x 2 is approximated as (π 2 /(cid:4)) [71]. e 2 can, therefore, be estimated as where π 2 is described as According to Lemma 2, we have where (cid:4) is a small constant, ψ = π 2 + λ 1 π 1 and there exits positive constants t ∗ and h 2 such that ∀ t > t ∗ , we have ξ 2…”
• Adaptive tracking control of uncertain MIMO nonlinear systems with input constraints (2011, background): “For example , rigid robots and motors (Dawson, Carroll, & Schneider, 1994), ships (Tee & Ge, 2006) and aircraft (Tang et al., 2007; Tee, Ge, & Tay, 2008).”
• Adaptive Robust Finite-Time Trajectory Tracking Control of Fully Actuated Marine Surface Vehicles (2016, unknown): “, just to name a few, [18]–[24].”