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09.05.2014 | Focus

Adaptive least square control in discrete time of robotic arms

verfasst von: José de Jesús Rubio

Erschienen in: Soft Computing | Ausgabe 12/2015

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Abstract

In this paper, the trajectory tracking problem of robotic arms in discrete time is considered. To solve this problem, an adaptive least square controller is proposed. The uniform stability of the tracking error and parameters error for the aforementioned controller is guaranteed by means of a Lyapunov-like analysis. The effectiveness of the proposed controller is verified by on-line simulations.

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Balaguer-Ballester E, Bouchachia H, Lapish CC (2013) Identifying sources of non-stationary neural ensemble dynamics. BMC Neurosci 14(Suppl 1):15

Blazic S, Skrjanc I, Matko D (2003) Globally stable direct fuzzy model reference adaptive control. Fuzzy Sets Syst 139:3–33MATHMathSciNetCrossRef

Bordignon F, Gomide F (2014) Uninorm based evolving neural networks and approximation capabilities. Neurocomputing 127:13–20CrossRef

Brodka P, Saganowski S, Kazienko P (2013) Ged: the method for group evolution discovery in social networks. Soc Netw Anal Min 3:1–14CrossRef

Buchachia A (2012) Dynamic clustering. Evolv Syst 3(3):133–134CrossRef

Chen Z, Jagannathan S (2008) Generalized hamilton–jacobi–bellman formulation-based neural network control of affine nonlinear discrete-time systems. IEEE Trans Neural Netw 19(1):90–106CrossRef

Dotschel T, Auer E, Rauh A, Aschemann H (2013) Thermal behavior of high-temperature fuel cells: reliable parameter identification and interval-based sliding mode control. Soft Comput 17:1329–1343CrossRef

García-Cuesta E, Iglesias JA (2012) User modeling: through statistical analysis and subspace learning. Expert Syst Appl 39(5):5243–5250CrossRef

Jagannathan S (2001) Control of a class of nonlinear discrete-time systems using multilayer neural networks. IEEE Trans Neural Netw 12(5):1113–1120MathSciNetCrossRef

Jahed M, Farrokhi M (2013) Robust adaptive fuzzy control of twin rotor mimo system. Soft Comput 17:1847–1860CrossRef

Lee M, Choi HS (2000) A robust neural controller for underwater robot manipulators. IEEE Trans Neural Netw 11(6):1465–1469CrossRef

Lewis FL, Dawson DM, Abdallah CT (2004) Robot manipulator control theory and practice. ISBN: 0- 8247-4072-6

Lughofer E (2012) Sigle pass active learning with conflict and ignorance. Evolv Syst 3:251–271CrossRef

Lughofer E, Trawinski B, Trawinski K, Kempa O, Lasota T (2011) On employing fuzzy modeling algorithms for the valuation of residential premises. Inf Sci 181:5123–5142CrossRef

Lughofer E (2011) Evolving fuzzy systems: methodologies. Advanced concepts and applications. Springer, BerlinCrossRef

Maciel L, Lemos A, Gomide F, Ballini R (2012) Evolving fuzzy systems for pricing fixed income options. Evolv Syst 3:5–18CrossRef

Marques Silva A, Caminhas W, Lemos A, Gomide F (2014) A fast learning algorithm for evolving neo-fuzzy neuron. Appl Soft Comput 14(B):194–209CrossRef

Musial K, Kazienko P (2013) Social networks on the internet. World Wide Web 16:31–72CrossRef

Perez-Cruz JH, Rubio JJ, Pacheco J, Soriano E (2014a) State estimation in MIMO nonlinear systems subject to unknown deadzones using recurrent neural networks. Neural Comput Appl. doi:10.1007/s00521-013-1533-5

Perez-Cruz JH, Chairez I, Rubio JJ, Pacheco J (2014b) Identification and control of a class of nonlinear systems with nonsymmetric deadzone using recurrent neural networks. IET Control Theory Appl 8(3):183–192MathSciNetCrossRef

Perng J-W (2013) Limit-cycle analysis of dynamic fuzzy control systems. Soft Comput 17:1553–1561CrossRef

Precup R-E, David R-C, Petriu EM, Preitl S, Radac M-B (2014) Novel adaptive charged system search algorithm for optimal tuning of fuzzy controllers. Expert Syst Appl 41:1168–1175CrossRef

Pratama M, Anavatti SG, Angelov PP, Lughofer E (2014) Panfis: a novel incremental learning machine. IEEE Trans Neural Netw Learn Syst 25(1):55–68CrossRef

Pratama M, Anavatti SG, Lughofer E (2014) GENEFIS: Towards An Effective Localist Network. IEEE Trans Fuzzy Syst. doi:10.1109/TFUZZ.2013.2264938

Rubio JJ (2014) Stable and optimal controls of a proton exchange membrane fuel cell. Int J Control. doi:10.1080/00207179.2014.913201

Rubio JJ, Angelov P, Pacheco J (2011) An uniformly stable backpropagation algorithm to train a feedforward neural network. IEEE Trans Neural Netw 22(3):356–366CrossRef

Rubio JJ (2012) Modified optimal control with a backpropagation network for robotic arms. IET Control Theory Appl 6(14):2216–2225

Rubio JJ, Zamudio Z, Pacheco J (2013) Proportional derivative control with inverse dead-zone for pendulum systems. Math Probl Eng 2013:1–9MathSciNet

Rubio JJ (2014) Evolving intelligent algorithms for the modelling of brain and eye signals. Appl Soft Comput 14(B):259–268

Rubio JJ, Perez-Cruz JH (2014) Evolving intelligent system for the modelling of nonlinear systems with dead-zone input. Appl Soft Comput 14(B):289–304

Song R, Xiao W, Wei Q (2013) Multi-objective optimal control for a class of nonlinear time-delay systems via adaptive dynamic programming. Soft Comput 17:2109–2115CrossRef

Skrjanc I, Blazic S, Matko D (2003) Model-reference fuzzy adaptive control as a framework for nonlinear system control. J Intell Robot Syst 36:331–347CrossRef

Spong MW, Vidyasagar M (1989) Robot dynamics and control. Wiley, New York

Sun F, Sun Z, Woo PY (2001) Neural network-based adaptive controller design of robotic manipulators with an observer. IEEE Trans Neural Netw 12(1):54–66CrossRef

Tar JK, Rudas IJ, Bito JF, Preitl S, Precup RE (2010) Convergence stabilization by parameter tuning in robust fixed point transformation based adaptive control of underactuated MIMO systems, International joint conference on computational cybernetics and technical informatics, pp 407–412

Trawinski B (2013) Evolutionary fuzzy system ensemble approach to model real estate market based on data stream exploration. J Univ Comput Sci 19(4):539–562

Wai RJ, Chen PC (2004) Intelligent tracking control for robot manipulator including actuator dynamics via tsk-type fuzzy neural network. IEEE Trans Fuzzy Syst 12(4):552–558CrossRef

Yang F, Yuan R, Yi J, Fan G, Tan X (2013) Direct adaptive type-2 fuzzy neural network control for a generic hypersonic flight vehicle. Soft Comput 17:2053–2064CrossRef

Yu W, Rubio JJ (2009) Recurrent neural networks training with stable bounding ellipsoid algorithm. IEEE Trans Neural Netw 20(6):983–991CrossRef

Zhao D, Wang B, Liu D (2013) A supervised actor–critic approach for adaptive cruise control. Soft Comput 17:2089–2099CrossRef

Titel: Adaptive least square control in discrete time of robotic arms
verfasst von: José de Jesús Rubio
Publikationsdatum: 09.05.2014
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 12/2015
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-014-1300-2

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