Non-Adaptive and Adaptive Control of Manipulation Robots

Stokić, Dragan; Kircanski, N.; Vukobratović, Miomir

doi:10.1007/978-3-642-82201-8

Cited by 79 publications

(25 citation statements)

References 10 publications

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“…In many problems of modern technical practice (in particular, the management of multi-link manipulators [57][58][59] and other mechatronic systems) it is advisable [2][3][4]58,60] to specify the configuration of a mechanical system with parameters taken in a number exceeding the necessary n-number of degrees of freedom system. Then m of these n + m parameters are called redundant coordinates.…”

Section: Holonomic Mechanical Systemsmentioning

confidence: 99%

“…It is this form of these equations that, in our opinion, is one of the most suitable for the study of the dynamics of systems with redundant coordinates, especially for the study of problems of stability and stabilization of steady motions of systems with geometric constraints. The application of the Lagrange equations with constraints factors leads [2][3][4]58,61] to an increase in the dimension of the problem. An alternative way is to go over to time-differentiated equations of geometric constraints and use of equations free from joining factors in the form of M.F.…”

Section: Holonomic Mechanical Systemsmentioning

confidence: 99%

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Analysis of modern modeling methods in problems of stabilization of motions of mechatronic systems with differential constraints

A.Ya¹,

Krasinskaya²

2018

IJET

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The most important problem of controlling mechatronic systems is the development of methods for the fullest possible application of the properties of our own (without the application of controls) motions of the object for the optimal use of all available resources. The basis of this can be a non-linear mathematical model of the object, which allows to determine the degree of minimally necessary interference in the natural behavior of an object with the purpose of stable implementation of a given operating mode. The operating modes of the vast majority of modern mechatronic systems are realized due to the steady motions (equilibrium positions and stationary motions) of their mechanical components, and often these motions are constrained by connections of various kinds. The paper gives an analysis of methods for obtaining nonlinear mathematical models in stabilization problems of mechanical systems with differential holonomic and non-holonomic constraints.

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Section: Holonomic Mechanical Systemsmentioning

confidence: 99%

Section: Holonomic Mechanical Systemsmentioning

confidence: 99%

Analysis of modern modeling methods in problems of stabilization of motions of mechatronic systems with differential constraints

A.Ya¹,

Krasinskaya²

2018

IJET

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“…The first line of the algorithm initials the formation of the neural network parameters according to Eqs. (30) to (33). Then, in the second line, the network weights in the range of one to minus one are defined.…”

Section: Radial Basis Function Neural Networkmentioning

confidence: 99%

“…If the system starts with initial conditions e(0) = 0 andθ(0) = 0, then the H ∞ performance in Eq. (49) can be rewritten as [33,34]:…”

mentioning

confidence: 99%

On The Design of The Robust Neuro-Adaptive Controller for Cable-driven Parallel Robots

et al. 2016

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Original scientific paperIn this study, a robust neuro-adaptive controller for cable-driven parallel robots is proposed. The robust neuroadaptive control system is comprised of a computation controller and a robust controller. The computation controller containing a neural-network-estimator with radial basis function activator is the principal controller and the robust controller is designed to achieve tracking performance. An on-line tuning method is derived to tune the parameters of the neural network for estimating the controlled system dynamic function. To investigate the effectiveness of the robust adaptive control, the design methodology is applied to control a cable-driven parallel robot. Simulation results demonstrate that the proposed robust adaptive control system can achieve favorable tracking performances for the robot.Key words: Robust control, Adaptive law, neural network, Cable-driven Parallel Robot Dizajn robusnog neuro-adaptivnog regulatora za žično pogonjene paralelne robote. U ovome redu predstavljen je neuro-adaptivni regulator za žično pogonjene paralelne robote. Robusni neuro-adaptivni regulator sastoji se od regulatora zasnovanog na estimiranom modelu i robusnog regulatora. Prvi regulator sadrži estimator s neuronskom mrežom s radijalnom aktivacijskom funkcijom glavni je regulator u sustavu, a robusni je regulator dizajniran za slijedenje. Izvedena je on-line metoda podešavanja parametara neuronske mreže za estimaciju dinamike sustava upravljanja. Efikasnost sustava adaptivnog, robusnog regulatora testirana je na na žično pogonjenom paralelnom robotu. Simulacijski rezultati pokazuju da se predloženim robusnim i adaptivnim regulatorom mogu dobiti zadovoljavajuće performanse prilikom slijedenja.Ključne riječi: robusno upravljanje, adaptivni upravljački zakon, neuronska mreža, žično pogonjeni paralelni robot INTRODUCTIONIn the recent decades, robots have been utilized in vast area of industries. However, many parts of manufacturing and engineering does not put upon robots, mainly due to the weakness of conventional robots [1] [2]. For instance, in many applications workspace requirements and load carrying capacity are so much higher than what the conventional robots can provide while cost of the robot should be considered [3] [4]. Toward resolving the latter issue, new class of parallel robots were introduced [5]. Cable-Driven Parallel Robots (CDPR) are structurally similar to parallel actuated robots but with the fundamental difference that cables can only pull the End-Effector (EE) but not push it. Figure 1 represents schematically a CDPR in a general arrangement. It consists of motor, winch system and the EE. From a scientific stand point, feedback control of CDPR is lot more challenging than their counterpart parallel-actuated robots due to the cables behavior. Several efforts had been exerted on modeling and control

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“…Amestegui et al, 1987) while passivity-based control avoids both limitations. We propose to classify the specialized contributions in the field as follows: a. Parameter estimation: such as the linear estimation models suitable for identification of the payload of a partially known robot, going back to (Vukabratovic et al, 1984). b.…”

Section: Conventional Rm Controlmentioning

confidence: 99%

Towards Robotic Manipulator Grammatical Control

Hamdi-Cherif¹

2010

Robot Learning

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Non-Adaptive and Adaptive Control of Manipulation Robots

Cited by 79 publications

References 10 publications

Analysis of modern modeling methods in problems of stabilization of motions of mechatronic systems with differential constraints

Analysis of modern modeling methods in problems of stabilization of motions of mechatronic systems with differential constraints

On The Design of The Robust Neuro-Adaptive Controller for Cable-driven Parallel Robots

Towards Robotic Manipulator Grammatical Control

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