Learning control of mobile robots using a multiprocessor system

Antonini, P.; Ippoliti, Gianluca; Longhi, Sauro

doi:10.1016/j.conengprac.2005.06.012

Cited by 35 publications

(15 citation statements)

References 29 publications

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“…These data sets are used to train the nets offline by the OLS-based algorithm of Sect. 2.2 (Chen et al 1991;Antonini et al 2006). Data have been also normalized in order to have the same range.…”

Section: Structure and Validation Of Implemented Estimatorsmentioning

confidence: 99%

Robust Control of Robot Arms via Quasi Sliding Modes and Neural Networks

Corradini

Giantomassi

Ippoliti

et al. 2014

Advances and Applications in Sliding Mode Control Systems

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This chapter presents a control approach for robotic manipulators based on a discrete-time sliding mode control which has received much less coverage in the literature with respect to continuous time sliding-mode strategies. This is due to its major drawback, consisting in the presence of a sector, of width depending on the available bound on system uncertainties, where robustness is lost because the sliding mode condition cannot be exactly imposed. For this reason, only ultimate boundedness of trajectories can be guaranteed, and the larger the uncertainties affecting the system are, the wider is the bound on trajectories which can be guaranteed. As a possible solution to this problem, in this chapter a discontinuous control law has been proposed, employing a controller inside the sector based on an estimation, as accurate as possible, of the overall effect of uncertainties affecting the system. Different solutions for obtaining this estimate have been considered and the achievable performances have be compared using experimental data. The first approach consists in estimating the uncertain terms by a well established method which is an adaptive on-line procedure for autoregressive modeling of non-stationary multivariable time series by means of a Kalman filtering. In the second solution, radial basis neural networks are used to perform the estimation of the uncertainties affecting the system. The proposed control system is evaluated on the ERICC robot arm. Experimental evidence shows satisfactory trajectory tracking performances and noticeable robustness in the presence of model inaccuracies and payload perturbations.

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Section: Structure and Validation Of Implemented Estimatorsmentioning

confidence: 99%

Robust Control of Robot Arms via Quasi Sliding Modes and Neural Networks

Corradini

Giantomassi

Ippoliti

et al. 2014

Advances and Applications in Sliding Mode Control Systems

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“…Narendra and Xiang 2000;Hespanha et al 2001;Angeli and Mosca 2002;Xiang and Narendra 2002;Liberzon 2003;Ippoliti and Longhi 2004;Antonini, Ippoliti, Longhi 2006;Ippoliti, Jetto and Longhi 2006a;Cavalletti, Ippoliti and Longhi 2007a, b). This makes the approach particularly suited to deal with large parametric variations and/or uncertainties.…”

Section: Introductionmentioning

confidence: 97%

Intelligent control for a remotely operated vehicle

Cavalletti

Ippoliti

Longhi

2009

International Journal of Systems Science

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This article considers the tracking control problem of an underwater vehicle used in the exploitation of combustible gas deposits at great sea depths. The vehicle is subjected to different load configurations that introduce considerable variations of its mass and inertial parameters. In this work it is assumed that the possible vehicle configurations are known, but the time instants when the changes occur and the new vehicle configuration following the change are unknown. A neural network-based switching control is proposed for the considered mode-switch process. This solution simplifies the control scheme implementation and reduces the control signal chattering.

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“…The theory of linear and nonlinear H N is used in Hwang, Chen, and Chang (2004) and Inoue, Siqueira, and Terra (2009) to resolve the mobile robot tracking control. In Antonini, Ippoliti, and Longhi (2006), a Neural Network based control methodologies are further investigated within the context of multiple models control of mobile robots in an adaptive and learning control framework. A switching strategy among these models determines the best possible control input at any given instant.…”

Section: Introductionmentioning

confidence: 99%