Intelligent control for accurate position tracking of electrohydraulic actuators

Santos, J. dos; Bessa, Wallace Moreira

doi:10.1049/el.2018.7218

Cited by 11 publications

(12 citation statements)

References 6 publications

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“…Autonomous diving agents are very representative of modern intelligent mechatronic systems since they should be able to present real-time learning in order to regulate their depth even considering all uncertainties and perturbations related to the underwater environment. This framework has been also successfully applied to the accurate position tracking of electrohydraulic servomechanism [89].…”

Section: Depth Control Of a Micro Diving Agentmentioning

confidence: 99%

A Biologically Inspired Framework for the Intelligent Control of Mechatronic Systems and Its Application to a Micro Diving Agent

Bessa

Brinkmann

Duecker

et al. 2018

Mathematical Problems in Engineering

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Mechatronic systems are becoming an intrinsic part of our daily life, and the adopted control approach in turn plays an essential role in the emulation of the intelligent behavior. In this paper, a framework for the development of intelligent controllers is proposed. We highlight that robustness, prediction, adaptation, and learning, which may be considered the most fundamental traits of all intelligent biological systems, should be taken into account within the project of the control scheme. Hence, the proposed framework is based on the fusion of a nonlinear control scheme with computational intelligence and also allows mechatronic systems to be able to make reasonable predictions about its dynamic behavior, adapt itself to changes in the plant, learn by interacting with the environment, and be robust to both structured and unstructured uncertainties. In order to illustrate the implementation of the control law within the proposed framework, a new intelligent depth controller is designed for a microdiving agent. On this basis, sliding mode control is combined with an adaptive neural network to provide the basic intelligent features. Online learning by minimizing a composite error signal, instead of supervised off-line training, is adopted to update the weight vector of the neural network. The boundedness and convergence properties of all closed-loop signals are proved using a Lyapunov-like stability analysis. Numerical simulations and experimental results obtained with the microdiving agent demonstrate the efficacy of the proposed approach and its suitableness for both stabilization and trajectory tracking problems.

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Section: Depth Control Of a Micro Diving Agentmentioning

confidence: 99%

A Biologically Inspired Framework for the Intelligent Control of Mechatronic Systems and Its Application to a Micro Diving Agent

Bessa

Brinkmann

Duecker

et al. 2018

Mathematical Problems in Engineering

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“…Intelligent control, on the other hand, has proven to be a very attractive approach to cope with uncertain nonlinear systems [2]- [6], [9], [15], [16], [23]. By combining nonlinear control techniques, such as feedback linearization or sliding modes, with adaptive intelligent algorithms, for example fuzzy logic or artificial neural networks, the resulting intelligent control strategies can deal with the nonlinear characteristics as well as with modeling imprecisions and external disturbances that can arise.…”

Section: Intelligent Feedback Linearizationmentioning

confidence: 99%

A framework for the development of intelligent mechanical systems

Bessa¹

2022

Preprint

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From autonomous vacuum cleaners to self-driving cars, intelligent mechanical systems are becoming an intrinsic part of our daily lives. In this work, a framework for the development of intelligent mechanical systems is presented.Considering that in this scenario the adopted control approach plays an essential role, I show that the proposed scheme should be able to not only adapt itself to changes in the environment, but also learn from its own experience.

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“…Nevertheless, it should be highlighted that ANN alone may not guarantee the necessary robustness to allow safe operating conditions. On the other hand, by combining ANN with non-linear control methods, the resulting intelligent controller is able to meet both stability and robustness requirements while maintaining the learning and approximation features provided by neural networks [10,11].…”

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confidence: 99%

“…Since sat(s/ϕ) = sgn(s) outside the boundary layer, by updating w according to ẇ = ν s ψ (11) and defining the control gain as κ > η + ε, with η being a strictly positive constant, we get…”

mentioning

confidence: 99%

“…Regarding the adaptive neural network, seven neurons with Gaussian activation functions are adopted in the hidden layer. The weight vector is initialized as w = 0 and updated by applying the Euler integration method to (11). For a fair evaluation of the adopted method, the proposed intelligent control scheme is also compared with an adaptive sliding mode controller.…”

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confidence: 99%

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Intelligent control of a single‐link flexible manipulator using sliding modes and artificial neural networks

Lima

Porto

Oliveira

et al. 2021

Electronics Letters

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This letter presents a new intelligent control scheme for the accurate trajectory tracking of flexible link manipulators. The proposed approach is mainly based on a sliding mode controller for underactuated systems with an embedded artificial neural network to deal with modelling inaccuracies. The adopted neural network only needs a single input and one hidden layer, which drastically reduces the computational complexity of the control law and allows its implementation in low-power microcontrollers. Online learning, rather than supervised offline training, is chosen to allow the weights of the neural network to be adjusted in real time during the tracking. Therefore, the resulting controller is able to cope with the underactuating issues and to adapt itself by learning from experience, which grants the capacity to deal with plant dynamics properly. The boundedness and convergence properties of the tracking error are proved by evoking Barbalat's lemma in a Lyapunov-like stability analysis. Experimental results obtained with a small single-link flexible manipulator show the efficacy of the proposed control scheme, even in the presence of a high level of uncertainty and noisy signals.

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Intelligent control for accurate position tracking of electrohydraulic actuators

Cited by 11 publications

References 6 publications

A Biologically Inspired Framework for the Intelligent Control of Mechatronic Systems and Its Application to a Micro Diving Agent

A Biologically Inspired Framework for the Intelligent Control of Mechatronic Systems and Its Application to a Micro Diving Agent

A framework for the development of intelligent mechanical systems

Intelligent control of a single‐link flexible manipulator using sliding modes and artificial neural networks

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