Neural network based dynamic modeling of flexible-link manipulators with application to the SSRMS

Talebi, Heidar Ali; Patel, Rajni V.; Asmer, H.

doi:10.1002/1097-4563(200007)17:7<385::aid-rob4>3.0.co;2-3

Cited by 33 publications

(23 citation statements)

References 11 publications

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“…A point close to the tip has been introduced in [10] and utilized in many researches [4,6,11,12,5]. This redefined output " " is denoted by (6):…”

Section: Dynamic Modeling and Feedback Linearizationmentioning

confidence: 99%

“…In this paper, an outer control loop has been applied to overcome these oscillations. The relative degree of this system is "2" so the dynamics associated with the redefined output has been derived as follow [4], [6]: (10), the linearized output is achieved as (12).…”

Section: Dynamic Modeling and Feedback Linearizationmentioning

confidence: 99%

“…It was assumed to be less than 10 / which is a reasonable value especially in practical applications [6], [4]. Consequently is taken to be a low pass filter as (26) …”

Section: A the Proposed "mentioning

confidence: 99%

“…The instability of the zero dynamics associated with the tip position, restricts the fascinating application of feedback linearization even where all nonlinearities are known [4], [5]. The non-minimum phase characteristic degrades the perfect tracking utilizing a causal controller [6]. To overcome this problem, some modifications have been performed.…”

Section: Introductionmentioning

confidence: 99%

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Tip position tracking of flexible-link manipulators based on online robust trajectory modification

Atashzar¹,

Talebi²,

Yazdanpanah³

et al. 2010

IECON 2010 - 36th Annual Conference on IEEE Industrial Electronics Society

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This paper presents a composite controller for tip position tracking of flexible link manipulators. In this regard, an inner/outer control structure is proposed. Opposed to previous conducted researches in this area, the desired reference trajectory is robustly modified (online) to minimize the tracking error of the tip utilizing the outer controller. The outer trajectory modifier is a synthesis based controller which modifies the reference trajectory of the inner loop in the uncertain situations. In the inner control loop a Lyapunov Redesign Feedback Linearization (LRFL) approach is applied to alleviate the degrading effects of uncertainties and nonlinearities presented in the dynamics of the flexible link. In the inner control loop, a conventional redefined output namely "close to the tip" is considered to avoid the difficulties associated with non-minimum phase behavior of the main output (tip). In conventional control strategies this choice leads to undesirable oscillations in tip. These oscillations are considerably minimized applying the outer trajectory modifier. Simulation results performed on a single-link flexible manipulator (with the parameters of an experimental setup) are presented to illustrate the significant improvements of performance over the conventional methods.

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“…A point close to the tip has been introduced in [10] and utilized in many researches [4,6,11,12,5]. This redefined output " " is denoted by (6):…”

Section: Dynamic Modeling and Feedback Linearizationmentioning

confidence: 99%

Section: Dynamic Modeling and Feedback Linearizationmentioning

confidence: 99%

“…It was assumed to be less than 10 / which is a reasonable value especially in practical applications [6], [4]. Consequently is taken to be a low pass filter as (26) …”

Section: A the Proposed "mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Tip position tracking of flexible-link manipulators based on online robust trajectory modification

Atashzar¹,

Talebi²,

Yazdanpanah³

et al. 2010

IECON 2010 - 36th Annual Conference on IEEE Industrial Electronics Society

Self Cite

View full text Add to dashboard Cite

show abstract

“…To tackle these problems in this paper, a nonlinear in parameter neural network (NLPNN) controller is introduced to use global approximation property, and despite of most of the previous approaches, learning rules are rooted in the well-known BP optimization algorithm. BP algorithm is the most relevant learning rule to the control problems, which owes its fame to its promising results and simplicity [31], [32]. The main drawback of the previous approaches on BP algorithm lies in lack of stability analysis.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Control of Uncertain Nonaffine Nonlinear Systems With Input Saturation Using Neural Networks

Esfandiari

Abdollahi

Talebi

2015

IEEE Trans. Neural Netw. Learning Syst.

109

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This paper presents a tracking control methodology for a class of uncertain nonlinear systems subject to input saturation constraint and external disturbances. Unlike most previous approaches on saturated systems, which assumed affine nonlinear systems, in this paper, tracking control problem is solved for uncertain nonaffine nonlinear systems with input saturation. To deal with the saturation constraint, an auxiliary system is constructed and a modified tracking error is defined. Then, by employing implicit function theorem, mean value theorem, and modified tracking error, updating rules are derived based on the well-known back-propagation (BP) algorithm, which has been proven to be the most relevant updating rule to control problems. However, most of the previous approaches on BP algorithm suffer from lack of stability analysis. By injecting a damping term to the standard BP algorithm, uniformly ultimately boundedness of all the signals of the closed-loop system is ensured via Lyapunov's direct method. Furthermore, the presented approach employs nonlinear in parameter neural networks. Hence, the proposed scheme is applicable to systems with higher degrees of nonlinearity. Using a high-gain observer to reconstruct the states of the system, an output feedback controller is also presented. Finally, the simulation results performed on a Duffing-Holmes chaotic system, a generalized pendulum-type system, and a numerical system are presented to demonstrate the effectiveness of the suggested state and output feedback control schemes.

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Introduction

Esfandiari¹,

Abdollahi²,

Talebi³

2021

Neural Network-Based Adaptive Control of Uncertain Nonlinear Systems

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Neural network based dynamic modeling of flexible-link manipulators with application to the SSRMS

Cited by 33 publications

References 11 publications

Tip position tracking of flexible-link manipulators based on online robust trajectory modification

Tip position tracking of flexible-link manipulators based on online robust trajectory modification

Adaptive Control of Uncertain Nonaffine Nonlinear Systems With Input Saturation Using Neural Networks

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