Design and Adaptive Depth Control of a Micro Diving Agent

Bessa, Wallace Moreira; Kreuzer, Edwin; Lange, Johann; Pick, Marc-André; Solowjow, Eugen

doi:10.1109/lra.2017.2714142

Cited by 17 publications

(17 citation statements)

References 23 publications

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“…They have been deployed in the investigation of ocean phenomena [87] and in the development of underwater sensor networks [88]. However, the design of accurate controllers for this kind of mechatronic system can become very challenging, due to their intrinsic nonlinear behavior and highly uncertain dynamics [49]. Furthermore, they must have a compact size to be hydrodynamically transparent [87], which clearly restricts the dimensions of the embedded electronics and the associated computational power.…”

Section: Depth Control Of a Micro Diving Agentmentioning

confidence: 99%

“…The adopted microrobot is a revised and improved version of the agent introduced in [49] and was developed at the Institute of Mechanics and Ocean Engineering at Hamburg University of Technology [90]. The microdiving agent and its main components are presented in Figure 2.…”

Section: Depth Control Of a Micro Diving Agentmentioning

confidence: 99%

“…Considering that the actuator dynamics should not be neglected [49], a first-order low pass filter is used to describe its behavioṙ=…”

Section: System Dynamicsmentioning

confidence: 99%

“…It should be emphasized that the designed intelligent depth controller represents an important improvement over our recently proposed scheme, which is discussed in [49]. By means of the combination of sliding mode control with artificial neural networks, the new intelligent depth controller's contribution is twofold: it provides robustness and enhances the learning capacities of the diving agent.…”

Section: Numerical Studiesmentioning

confidence: 99%

“…Modeling imprecisions and disturbances lead to a significant increase in tracking error when feedback linearization is applied without a compensation scheme [41]. Hence, neural networks [42][43][44][45][46][47][48], fuzzy logic [49][50][51][52], and evolutionary computation [53][54][55], for instance, have been used to surpass this limitation and to improve control performance.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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%

Section: Depth Control Of a Micro Diving Agentmentioning

confidence: 99%

“…Considering that the actuator dynamics should not be neglected [49], a first-order low pass filter is used to describe its behavioṙ=…”

Section: System Dynamicsmentioning

confidence: 99%

Section: Numerical Studiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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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Sliding Mode Control with Gaussian Process Regression for Underwater Robots

Lima

Trimpe

Bessa

2020

J Intell Robot Syst

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Intelligent control of cardiac rhythms using artificial neural networks

2023

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Cardiac rhythms are related to heart electrical activity, being an essential aspect of the cardiovascular physiology. Usually, these rhythms are represented by electrocardiograms (ECGs) that are useful to detect cardiac pathologies. This paper investigates the control of cardiac rhythms in order to induce normal rhythms from pathological responses. The strategy is based on the electrocardiograms and considers different pathologies. An intelligent controller is proposed considering the ECG as the observable variable. In order to allow the assessment of the control performance, synthetic ECGs are produced from a reduced-order mathematical model that presents close agreement with experimental measurements. The adopted model comprises a network of oscillators formed by sinoatrial node, atrioventricular node and His-Purkinje complex. Three nonlinear oscillators are employed to represent each one of these nodes that are connected by delayed couplings. The controller considers the control variable at the His-Purkinje complex. To evaluate the ability of the control law to deal with both intra- and interpatient variability, the heart model is assumed to be not available to the controller designer, being used only in the simulator to assess the control performance. The incorporation of artificial neural networks into a Lyapunov-based control scheme, however, allows the presented intelligent approach to compensate for unknown cardiac dynamics. Results show that abnormal rhythms can be avoided by applying the proposed control scheme, turning the electrocardiogram closer to the expected normal behavior and preventing critical cardiac responses.

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Design and Adaptive Depth Control of a Micro Diving Agent

Cited by 17 publications

References 23 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

Sliding Mode Control with Gaussian Process Regression for Underwater Robots

Intelligent control of cardiac rhythms using artificial neural networks

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