Adaptive Robust Control for Fuzzy Mechanical Systems: Constraint-Following and Redundancy in Constraints

Huang, Qingmin; Chen, Ye‐Hwa; Cheng, Aiguo

doi:10.1109/tfuzz.2014.2346241

Cited by 41 publications

(18 citation statements)

References 22 publications

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“…6 and 7, the important observations are summarized and discussed as follows: (i) The cooperative control of dual arms must be constrained into their workspace (85). It indicates that initial pose of payload and its length must be consistent with the initial poses of two end effectors [16]. (ii) The response in Fig.…”

Section: Illustraive Examplesmentioning

confidence: 91%

Tracking and Cooperative Designs of Robot Manipulators Using Adaptive Fixed-Time Fault-Tolerant Constraint Control

Hwang

2020

IEEE Access

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At the outset, a nonlinear dynamic system for the generalized robots with input constraint is given to design an adaptive fixed-time fault-tolerant constraint control (AFTFTCC) for trajectory tracking. The proposed AFTFTCC includes a nonlinear filtering tracking error, which can shape the system response. As the operating point is in the neighborhood of the zero nonlinear filtering tracking error, nonlinear filtering gains are increasing to accelerate its tracking ability. The skew-symmetric matrix's condition for the timederivative of the inertia matrix and Coriolis and centrifugal force matrix is not required. The upper bound of uncertainties is learned to improve system performance. The stabilities of the closed-loop system are verified by the Lyapunov stability theory. Without true force feedback, the cooperative task of multiple robots with multiple arms is also addressed by the proposed AFTFTCC. Finally, two examples, including the trajectory tracking control of planar three-link robot arm and the cooperative control of dual planar three-link robot arms, are employed to validate the effectiveness and robustness of the proposed control. INDEX TERMS Adaptive fixed-time fault-tolerant constraint control, nonlinear filtering tracking error and gain, trajectory tracking of robot, cooperative control of multiple robots, Lyapunov stability theory.

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Section: Illustraive Examplesmentioning

confidence: 91%

Tracking and Cooperative Designs of Robot Manipulators Using Adaptive Fixed-Time Fault-Tolerant Constraint Control

Hwang

2020

IEEE Access

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“…In Sections 8.1 and 8.2, we will give examples to further illustrate this approach. The advantages of Udwadia-Kalaba Equation are not only in the modeling of constrained system, but also in the control design for constrained mechanical system [56][57][58][59][60][61]. For this point, we give an example in Section 8.3.…”

Section: Illustrative Examplesmentioning

confidence: 99%

Udwadia–Kalaba Equation for Constrained Mechanical Systems: Formulation and Applications

Zhao

Chen

Zhao

et al. 2018

Chin. J. Mech. Eng.

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There are many achievements in the field of analytical mechanics, such as Lagrange Equation, Hamilton's Principle, Kane's Equation. Compared to Newton-Euler mechanics, analytical mechanics have a wider range of applications and the formulation procedures are more mathematical. However, all existing methods of analytical mechanics were proposed based on some auxiliary variables. In this review, a novel analytical mechanics approach without the aid of Lagrange's multiplier, projection, or any quasi or auxiliary variables is introduced for the central problem of mechanical systems. Since this approach was firstly proposed by Udwadia and Kalaba, it was called Udwadia-Kalaba Equation. It is a representation for the explicit expression of the equations of motion for constrained mechanical systems. It can be derived via the Gauss's principle, d' Alembert's principle or extended d' Alembert's principle. It is applicable to both holonomic and nonholonomic equality constraints, as long as they are linear with respect to the accelerations or reducible to be that form. As a result, the Udwadia-Kalaba Equation can be applied to a very broad class of mechanical systems. This review starts with introducing the background by a brief review of the history of mechanics. After that, the formulation procedure of Udwadia-Kalaba Equation is given. Furthermore, the comparisons of Udwadia-Kalaba Equation with Newton-Euler Equation, Lagrange Equation and Kane's Equation are made, respectively. At last, three different types of examples are given for demonstrations.

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“…Recently, a suboptimal methodology has been developed by fusing neighboring extremals and fuzzy control concepts to deal with large uncertainties and external disturbances . Parts of the suggested control are based on the nominal dynamics of the controlled system; the other parts are then based on various strategies (e.g., fuzzy control , neural network control , adaptive compensation . Consequently, the integrated control becomes effective, flexible, robust, and efficient.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, a simple and extra network is established to deal with the residue of linearization parameterization . Finally, the proposed adaptive RNN enhanced discrete variable structure control (ARNNEDVSC) is designed to with two parts: one is the equivalent control including nominal system function and learning lumped uncertainties , the other is the switching control to improve system robustness . Under mild conditions, the convergent region of tracking error for the proposed ARNNEDSVC can be smaller than that of the DVSC.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Recurrent Neural Network Enhanced Variable Structure Control for Nonlinear Discrete Mimo Systems

Hwang

Hung

2017

Asian Journal of Control

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The stability and performance of robust control for a nonlinear complex dynamic system deteriorates due to large uncertainties. To address this, a discrete variable structure control (DVSC) outside of a convex set is designed to force the operating point into a convergent set, which is verified by Lyapunov stability theory. Due to the dynamic features of the lumped uncertainties, they are learned on-line by a recurrent neural network (RNN) in a specific convex set containing the convergent set of the DVSC. Between this convergent set and the convex set, a switching mechanism determines whether a learning RNN for the lumped uncertainties is executed. An adaptive recurrent-neural-network enhanced discrete variable structure control is then established to improve both transient and steady performances. Simulations, including compared examples and application to the trajectory tracking of a mobile robot, validate the effectiveness and robustness of the proposed control.

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Adaptive Robust Control for Fuzzy Mechanical Systems: Constraint-Following and Redundancy in Constraints

Cited by 41 publications

References 22 publications

Tracking and Cooperative Designs of Robot Manipulators Using Adaptive Fixed-Time Fault-Tolerant Constraint Control

Tracking and Cooperative Designs of Robot Manipulators Using Adaptive Fixed-Time Fault-Tolerant Constraint Control

Udwadia–Kalaba Equation for Constrained Mechanical Systems: Formulation and Applications

Adaptive Recurrent Neural Network Enhanced Variable Structure Control for Nonlinear Discrete Mimo Systems

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