A fully adaptive decentralized control of robot manipulators

Hsu, Su-Hau; Fu, Li‐Chen

doi:10.1016/j.automatica.2006.05.012

Cited by 85 publications

(44 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Specifically both the isolated subsystems and interconnections are fully nonlinear in Yan and Dai (1998) and Yan et al (2013). A tracking problem is considered in Hsu and Fu (2006) where decentralised state feedback controller are designed for robot manipulators. Broadly, the literature can be separated into state feedback (Lee and Wang, 1993;Lee, 1995;Hsu, 1997;Akar and Ozguner, 2002;Matthews and Decarlo, 1988), static output feedback (Yan et al, 2012 and dynamical/observer-based output feedback (Lee, 1995;Yan et al, 2006).…”

Section: Decentralised Variable Structure Controlmentioning

confidence: 99%

“…As a typical interconnected system, multi-machine power systems have received much attention and many decentralised control strategies have been developed including state feedback, static output feedback and dynamical output feedback control approaches Jiang and Wu, 2004;Liu et al, 2012). A multi-link robot can be modelled as an interconnected system, and various decentralised control schemes have been developed to study the control of robot manipulators (Hsu and Fu, 2006;Yang et al, 2012). Decentralised H 2 /H ∞ tracking control has been applied to car-like mobile robots in Hwang and Chang (2007) and a decentralised cooperative control scheme has been developed for a team of mobile robots in Rezaee and Abdollahi (2014).…”

Section: Applicationsmentioning

confidence: 99%

“…It should be noted that there are some decentralised variable structure controllers which may not result in sliding motion (see Hsu and Fu, 2006;Yan et al, , 2013Yan and Dai, 1998). Static output feedback controllers are proposed in Yan et al ( , 2013 and Yan and Dai (1998) to stabilise interconnected systems where the focus is on applying the nonlinear bounds on the uncertainty and interconnections to enhance the robustness.…”

Section: Decentralised Variable Structure Controlmentioning

confidence: 99%

See 2 more Smart Citations

Decentralised control for complex systems - an invited survey

Yan

Zhang

Spurgeon

et al. 2014

IJMIC

View full text Add to dashboard Cite

Section: Decentralised Variable Structure Controlmentioning

confidence: 99%

Section: Applicationsmentioning

confidence: 99%

Section: Decentralised Variable Structure Controlmentioning

confidence: 99%

See 1 more Smart Citation

Decentralised control for complex systems - an invited survey

Yan

Zhang

Spurgeon

et al. 2014

IJMIC

View full text Add to dashboard Cite

“…Equation (50) CalculatingV from (15) and (50), and using sgn(e)e = |e| results inV (e) =ρ (t)e − kie 2 kp .…”

Section: Assumption 2 the External Disturbance φ(T) Is Bounded As |φmentioning

confidence: 99%

“…The decentralized controller is still adopted by majority of modern robots in favor of computational simplicity and low-cost hardware setup [2] . To form the decentralized control, a robotic system is decomposed into individual single-input/singleoutput systems.…”

Section: Introductionmentioning

confidence: 99%

A Precise Robust Fuzzy Control of Robots Using Voltage Control Strategy

Fateh

2013

Int. J. Autom. Comput.

View full text Add to dashboard Cite

Fuzzy control of robot manipulators with a decentralized structure is facing a serious challenge. The state-space model of a robotic system including the robot manipulator and motors is in non-companion form, multivariable, highly nonlinear, and heavily coupled with a variable input gain matrix. Considering the problem, causes and solutions, we use voltage control strategy and convergence analysis to design a novel precise robust fuzzy control (PRFC) approach for electrically driven robot manipulators. The proposed fuzzy controller is Mamdani type and has a decentralized structure with guaranteed stability. In order to obtain a precise response, we regulate a fuzzy rule which governs the origin of the tracking space. The proposed design is verified by stability analysis. Simulations illustrate the superiority of the PRFC over a proprotional derivative like (PD-like) fuzzy controller applied on a selective compliant assembly robot arm (SCARA) driven by permanent magnet DC motors.

show abstract

Adaptive neuro‐predictive control for redundant robot manipulators in presence of static and dynamic obstacles: A Lyapunov‐based approach

Jasour

Farrokhi

2013

Adaptive Control & Signal

View full text Add to dashboard Cite

SUMMARYThis paper presents a novel approach for online path tracking and obstacle avoidance of redundant robot manipulators. To this end, a nonlinear model predictive control (NMPC) method is designed that can track a desired path or reaches a moving target in the Cartesian space while avoiding static or moving obstacles as well as singular configurations in the workspace of the robot. The finite cost function of the NMPC is optimized at every sampling time, yielding an online optimal approach. In order to avoid collisions with moving obstacles and, at the same time, capturing a moving target, the future positions of the obstacles and the moving target are predicted using artificial neural networks (ANNs). Moreover, ANNs are employed to find a proper nonlinear model for the NMPC. The adaptation laws for the ANNs are obtained using the Lyapunov's direct method. The advantages of the proposed method are fourfold: (i) an adaptive and optimal approach is obtained, which can cope with changes in the system parameters; (ii) no inverse kinematics of the redundant manipulators is required; (iii) no prior knowledge about the obstacles and motion of the moving object is required; and (iv) stability of the closed‐loop system is guaranteed. Numerical simulations, performed on a four degree‐of‐freedom redundant spatial manipulator actuated by DC servomotors, show effectiveness of the proposed method. Copyright © 2013 John Wiley & Sons, Ltd.

show abstract

A fully adaptive decentralized control of robot manipulators

Cited by 85 publications

References 13 publications

Decentralised control for complex systems - an invited survey

Decentralised control for complex systems - an invited survey

A Precise Robust Fuzzy Control of Robots Using Voltage Control Strategy

Adaptive neuro‐predictive control for redundant robot manipulators in presence of static and dynamic obstacles: A Lyapunov‐based approach

Contact Info

Product

Resources

About